From ccolo@mompou.iqs.url.es Tue Oct 22 06:16:25 1996 Received: from fletxa.iqs.url.es for ccolo@mompou.iqs.url.es by www.ccl.net (8.8.0/950822.1) id GAA05096; Tue, 22 Oct 1996 06:05:57 -0400 (EDT) Received: from mompou.iqs.url.es by fletxa.iqs.url.es (AIX 4.1/UCB 5.64/4.03) id AA37494; Tue, 22 Oct 1996 11:39:22 +0200 Date: Tue, 22 Oct 1996 11:26:49 +0100 (GDT) From: Carles Colominas To: chemistry@www.ccl.net Subject: Summary:Benchmarks Message-Id: Mime-Version: 1.0 Content-Type: TEXT/PLAIN; charset=US-ASCII Dear Netters, Some days ago I posted a message about new HP and SGI workstations perfomance. My original question and answers are shown below. The last part of this message is a review by Martyn F. Guest at CCLRC Daresbury Laboratory about 'Performance of Various Computers in Computational Chemistry' that includes lots of useful data. Thanks to all who answered: Steen Hammerum Bill DeSimone Jose Luis Garcia de Paz Oakley H. Crawford Marc C. Nicklaus Vikram Varma Alexander Hofmann Martyn F. Guest ========= ========= ========= ======== ****** ****** From: Steen Hammerum I recently asked the very same question, and here is the summary I posted: ------------------------------------------------------------------------ Summary: Speed of new HP and SGI workstations I recently asked this group for advice with regard to the performance of new HP and SGI workstations running the Gaussian programs ------------------------------------------------------------------------ Does anyone have or know of benchmarks for Gaussian 94 or related programs running on the new SGI and HP chips (R10000 and PA8000), or other information to assist us before we decide on new machinery? We are currently considering SGI Power Indigo 2 and HP C160 workstations that will be used predominantly for Gaussian calculations, but we are not sure how well the SPECint95 and SPECfp95 numbers allow us to assess the relative performance. ------------------------------------------------------------------------- --- Roberto Gomperts (roberto@boston.sgi.com) provided the following comparison of the SGI R8000 and R10000 chips when running test178 from the Gaussian 94 test suite (a single point, direct scf calculation with 300 basis functions), using Gaussian 94, rev. D3. Machine Chip/Frequency Sec. Cache Time(min.) Power Indigo2 r8k/75 MHz 2 MB 8.04 Power Challenge r8k/90 MHz 4 MB 6.50 Power Indigo2 r10k/195 MHz 1 MB 7.30 Power Challenge r10k/195 MHz 1 MB 7.08 Power Challenge r10k/195 MHz 2 MB 5.95 The Power Challenge runs are done on 1 processor. These are all CPU times. The Wall clock times are very similar to these (less than 20 sec. difference) --- John Brodholt (j.brodholt@ucl.ac.uk) pointed me to http://gserv1.dl.ac.uk/TCSC/disco/TechPapers/bench/bench.html This site provides a detailed, critical and very useful comparison of the performance of a wide variety of newer workstations; unfortunately, no results obtained with HP PA8000 machines are included (html version not yet available, but a postscript version can be downloaded). The comparison is based on timing data obtained with GAMESS-UK (rather than Gaussian). The results illustrate that the relative performance varies quite a bit with the type of calculation undertaken. --- Eric Billings (billings@helix.nih.gov) pointed me to http://www.ki.si/parallel/summary.html The information was designed to compare parallel architectures, but the single CPU column provides useful information. --- Finally, Glenn McEnroe (gmcenroe@crl.com) suggested that I looked elsewhere: "Regarding your question about SGI vs HP you should check out the latest issue of Journal of Computational Chem V17 No. 11 1385-86 entitled Viability of Molecular Modeling with Pentium based PCs. This article does not compare these new chips for SGI and HP but it appears that you may be better off running your application on a pentium based machine if Gaussian 94 is available for this platform." --- Many thanks to everyone who answered. ---------------------------------------------------------------- Since writing the summary, I have had the opportunity to perform trisl calculations on both new SGI and new HP machines. The results with R10k SGI machines confirm Roberto Gomperts' results (see summary), that is, the new chip is very fast but not much faster than the old chip, and cache size matters a lot. My HP results have been somewhat disappointing, insofar as the new chip in real life situations (if G94 calculations can be called "real life") does not seem to be quite as fast as the benchmarks would lead you to expect. Hope this helps, Steen -- Steen Hammerum steen@kiku.dk Department of Chemistry (+45) 35 32 02 08 University of Copenhagen, Denmark fax: (+45) 35 32 02 12 ========= ========= ========= ======== ****** ****** From: desimone@mroa.ENET.dec.com Carles, The AlphaStation 500 with the 500 MHz chip should perform at least as well if not better than HP and SGI on Gaussian and AMBER. A customer in Isreal just bought an AlphaStation 500 to run Gaussian. I can see if this customer would talk to you if you are interested. Unfortunately all my Gaussian benchmarks are on AlphaServers , which BTW, beat the competition on Gaussian benchmarks. Also, the UCSF data on AMBER is on very old AlphaStations. Bill DeSimone Science & Research Applications High Performance Computing DEC ========= ========= ========= ======== ****** ****** From: DEPAZ@ccuam3.sdi.uam.es Carles, En la Univ Autonoma de Madrid se acaba de comprar un DEC de 8 cpu, en competencia con un SGI de ocho r10000. Hicimos pruebas con el gaussian (en eso estuve yo) y EN TODAS nos salio un 20% mas repido el chip dec que el chip silicon. Hubo test del gaussian usando una sola cpu, usando varias (paralelo), etc. No hubo color. La version gaussian para dec iba mejor que la gaussian que habian modificado para silicon. Yo estuve en la comision tecnica como quimico cuantico y no tuve dudas. Un saludo Jose Luis Garcia de Paz quimica fisica aplicada 3974263-4957 ========= ========= ========= ======== ****** ****** From:crawfordoh@ornl.gov If you have access to the www, look at http://www.netlib.org/performance/html/PDStop.html for performance data on a vast array of machines. Otherwise, send the following message to netlib@ornl.gov, to receive performance data in postscript files: send performance.ps from benchmark send mp-computers.ps from benchmark Finally, if you want more info about netlib's collection, send the following message: send index Good luck, Oakley Crawford ---------------------------------------------------------------------------- Oakley H. Crawford Phone: +1-423-574-5048 Oak Ridge National Laboratory Fax: +1-423-574-6210 P. O. Box 2008, MS 6123 E-mail: crawfordoh@ornl.gov Oak Ridge, Tennessee 37831-6123 Express delivery, add: Bethel Valley Road USA ---------------------------------------------------------------------------- ========= ========= ========= ======== ****** ****** From: "M. Nicklaus" Dear Dr. Colominas, We have a four-processor Digital AlphaServer 4/275 which we use mostly to run Gaussian 94 and the Molecular Mechanics program CHARMM. We are very happy with it. We have not done benchmark comparisons with an SG R10000 system ourselves (since we don't have one), but I remember having seen quite a few postings of, or at least pointers to, benchmarks with ab initio and MM programs that included SGI, HP, and/or DEC systems, and which should be retrievable from the CCL archives. Hope this helps. Regards, Marc C. Nicklaus ------------------------------------------------------------------------ Marc C. Nicklaus Lab. of Medicinal Chemistry e-mail: mn1@helix.nih.gov National Cancer Institute, NIH Phone: (301) 402-3111 Bldg 37, Rm 5B29 Fax: (301) 496-5839 BETHESDA, MD 20892-4255 USA WWW: http://www.nci.nih.gov/intra/lmch/MCNBIO.HTM ------------------------------------------------------------------------ ========= ========= ========= ======== ****** ****** From: Vikram Varma Also consider the Pentium Pros - in parallel, you can get a lot of bang for your buck!! >>>>> Vikram Varma National Research Council of Canada Phone: 613 993 5150 Institute for Biological Sciences FAX: 613 952 9092 Room 3071 100 Sussex Drive, e-mail: varma@nrcbs8.bio.nrc.ca Ottawa, Ontario K1A 0R6 www: http://nrcbsa.bio.nrc.ca/~varma <<<<< ========= ========= ========= ======== ****** ****** From: Alexander Hofmann Some Gaussian-benchmarks http://www.chem.joensuu.fi/people/juha_muilu/Misc/benchmarks.html regards alex ========= ========= ========= ======== ****** ****** From: "M.F.Guest" Dear Carles, In response to your mailing, I thought you might find the attached of value. This is a plain text version of an assessment of a wide variety of machines in computational chemistry, including the R10000 and a prototype version of the new HP machine. While it doesnt specifically include GAUSSIAN and AMBER, I hope that you will find it of use. Let me know if I can be of any further assistance. Best regards Martyn ****************************************************************** * Martyn F. Guest * * Head, Advanced Research Computing email: m.f.guest@dl.ac.uk * * CCLRC Daresbury Laboratory FAX : +44 (0)1925 603634 * * Warrington voice: +44 (0)1925 603247 * * Cheshire WA4 4AD * * England, UK * ****************************************************************** ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Performance of Various Computers in Computational Chemistry Martyn F. Guest September 1996 Abstract This report compares the performance of a number of different computer systems using a variety of software from the discipline of computational chemistry. The software includes matrix operations, a variety of chemistry kernels from quantum chemistry and molecular dynamics, and a set of twelve quantum chemistry calculations using the GAMESS-UK electronic structure program. The comparison involves approximately fifty computers, ranging from a Cray YMP-C98 to scientific workstations from IBM, Sun, Hewlett Packard, Digital and Silicon Graphics, and Pentium Pro-based PCs. 1. Introduction This report presents a performance evaluation through benchmarking of a number of different computer systems specifically in the area of computational chemistry. This work has been ongoing since 1988, when the intention was to include representative hardware typifying supercomputers, superminis, workstations and the emerging class of parallel, novel architecture machines. Throughout the 1980's the cost effective debate in scientific computing centred on the relative merits of conventional vector supercomputers [1] and so-called superminis, machines costing some 10% of supercomputers, and exhibiting some 10% of supercomputer performance [2]. Over the past 7 years the supermini category of computational resource has to a large extent disappeared from both the vocabulary and offerings of most hardware vendors, to be replaced by high-end workstation servers. We have retained those machines that originally belonged in the supermini bracket, the Convex 220, the FPS M64/60 [2] and Alliant FX2808, primarily to provide a historical perspective at the evolution of workstation capabilities, and how machines that typically fell in the $500K price range are now outperformed by machines costing some 10% of this figure. Supercomputers used in this report include the Cray X-MP, Y-MP, YMP/J90 and YMP/C98, and the Convex C3860. A large number of workstations have been benchmarked, including those from - IBM, with the Power and Power2 Risc system RS6000-based models, - Hewlett Packard with the HP/Apollo DN10020 and the PA-RISC based HP model 9000 series, including the more recent PA8000 CPU. Note that the latter was housed in a single-processor HP PA/9000-K460, with a 160 Mhz PA8000 (and not 180 MHz). - DEC, with the DEC Station 500, the AXP EV4-based 3000 series. and the EV5-based 600, 800 and 2100 alpha series, - Silicon Graphics, with the R3000-, R4000-, and R8000-based machines including the Challenge, Power Challenge, Indy, Indigo and Indigo2, Crimson, and 4D series, plus machines with the more recent R10000 and R5000 CPUs. - Stardent (1520, 3020 and VISTRA 800), and - Sun 4/370, SPARCstation-2, SPARC-5 and SPARC-10 and the more recent Hyper- and Ultra-SPARC processors (Ultra-1/140 and 170, and the Ultra-2/200). While workstations have, for the majority of scientific applications, demonstrated their cost-effectiveness against vector supercomputers, recent developments with PCs suggest that the position of the workstation as the desktop system of choice is now under threat. We thus include for the first time one of the most recent offerings from the PC marketplace, the 200 MHz Pentium Pro. Parallel, or ``novel architecture'' machines include the iPSC/860 from Intel, both transputer and i860-based Meiko Computing Surfaces, the KSR-2 from Kendall Square Research, the Cray T3D and IBM SP2. Machines featuring in this exercise, together with associated configurations, are given in the Appendix. We should stress from the outset that our access to much of the hardware evaluated herein has been at best short lived, and has often involved the temporary loan or donation of machines as part of one of the hardware evaluation exercises run at the Daresbury Laboratory. In many cases these machines were not optimally configured in terms of either memory, or high speed disk, and consideration of the results presented here should be viewed in that light. Following an introductory evaluation of hardware based on the Whetstone Benchmark, we present in Sections 2, 3, and 4 results using a variety of chemistry-oriented software. This 64-bit floating point precision FORTRAN-based code may be classified into three distinct categories, each designed to provide a pointer to the relative hardware capabilities in the discipline of computational chemistry; 1. The first category (the MATRIX Benchmark, section 2) reflects the dependence of many of the algorithms in the area of electronic structure calculations on matrix operations, and includes both matrix multiplication and matrix diagonalization; 2. The second category (the Computational Chemistry Kernels, section 3) includes four chemistry `kernels', each comprising less than a 1000 lines of FORTRAN code, and intended to be representative of the typical calculations undertaken in the area of computational chemistry. Described in more detail in section 3, these kernels include direct-SCF, molecular dynamics (MD), quantum monte carlo (QMC), and a Jacobi eigen solver (JACOBI); 3. Finally, we include complete quantum chemistry applications (section 4), using the GAMESS-UK electronic structure program [3]. Twelve typical applications are included, featuring both conventional Hartree Fock self-consistent field (SCF) and direct SCF, complete active Space SCF (CASSCF) and multiconfiguration SCF (MCSCF), configuration interaction calculations, both direct-CI and conventional table-driven MRD-CI, Moller Plesset perturbation theory (MP2), and both SCF and MP2 analytic 2nd derivatives. 2. The MATRIX Benchmark 2.1 Whetstone Benchmark A comparison of the single processor Whetstone performance on a variety of machines, including vector supercomputers, minisupers, superworkstations and workstations, together with that obtained on a single node of various novel architecture machines is given in Table 1. Data provided includes the Mflop performance on a variety of floating point vector loops (VL=1024), together with the total cpu time to execute the benchmark, and the MWips performance. The primary aim of this benchmark is to provide a performance measure of both floating point (FP) and integer arithmetic; thus while trends in the VL Mflop ratings are of interest, only a small part of the total CPU time is actually involved in these operations. The wide variety of standard functions exercised (abs, sqrt, exp, alog, sin, cos, atan etc.) consume a far larger fraction of the reported times; note the latter provide a close mapping onto the measured rate of instruction processing (MWips, million whetstones instructions per second). This benchmark was originally designed to monitor the performance of vector supercomputers, and an examination of Table 1 reveals that both the Cray Y-MP and YMP-C90 continue to outperform all other machines. As expected, the Cray YMP-J90 is less impressive, some four times slower than the C90, and slower than five of the leading workstation CPUs. The fastest CPU is seen to be the 195 MHz R10000 processor from SGI, some 1.5 times faster than the 333 MHz EV5 of the alpha 600/5. Both CPU time and MWips rate suggest that the latter processor is marginally faster than the recently released PA8000 processor of the HP PA/9000-K460, and 1.5 times the speed of the 200 MHz Ultra-2/200 from Sun. The performance of the K460 is far below that expected based on the published SPEC-fp95 ratings, and it is clear that the compiler, libraries etc on the machine under test (a single processor 160 Mhz prototype of the PA8000) were not optimal, with the results of this and subsequent benchmarks not reflecting the true potential of the PA8000. SGI's other new processor, the R5000 from mips, is also seen to perform well, and while about 1.8 times slower than the R10000, is faster than the Ultra-2 and alpha 266 Mhz EV5. In contrast the R8000-based machines from SGI appear to under-perform, 4 times slower than the R10000, three times slower than the AXP-600/5/333, and slower than both the HP PA/9000-735/125 and IBM Power 2 RS/6000-3CT. The R8000 is seen to outperform its predecessor, the R4400, by just a factor of 1.7, based on the CPU time for the complete benchmark. Sun's latest processor, the 200 MHz Ultra-2/200 is found to be more than twice the speed of the 125 Mhz HyperSPARC, and 8.5 times the 40 MHz SuperSPARC processor in the Sun SPARC 10/41. Considering the MPP single node performance of Table 1, the KSR-2 custom processor is seen to be comparable with mid-range workstation CPUs (IBM RS/6000-360 and DEC AXP/3000-500), while the quality of the FORTRAN compiler on the Cray T3D is at least in part responsible for the Whetstone timing of 121 seconds, 1.8 times slower than the DEC AXP/3000-500 (68.7 secs) which does, of course, house the same CPU. The IBM-SP2 TN2 processor is by far the dominant CPU, twice the speed of the KSR-2 and 3.5 times that of the Cray T3D. Finally, we note that the performance of the Pentium Pro is broadly in line with expectations. It outperforms the R8000-based SGI and Power2-based IBM workstations, while slower than the leading CPUs from SGI, Digital, HP and Sun, by factors of 3.8, 2.5, 2.3 and 1.7 respectively. It is important to realise that this level of performance is only achieved using "commercial" Fortran compilers (in this case from Intel). Using public domain f2c and a variety of c-compilers produced far inferior figures, some 3-4 times slower than those of Table 1. 2.2 Sparse Matrix Multiply Benchmark The matrix multiply operation (MMO) is central to the efficient operation of modern QC codes on vector processors [1], it being possible both to extract near peak performance for this kernel and to formulate many QC steps around this operation. A comparison of the single processor sparse MMO performance on a variety of machines, is given in Table 2. In this benchmark a series of MMOs (R = A X B) involving matrices of order 10, 20, 30, ... 150 were performed. Each MMO was conducted a number of times, this number being inversely proportional to the order of the matrices, so that the summed CPU times of Table 2. refer to 150 MMOs of order 10 matrices, 140 of order 20 matrices, and so on up to 10 MMOs for matrices of order 150. Figures are presented for both `full' (0% sparse) and 50%-sparse B matrices, with the performance figures referring to code written entirely in Fortran. The potential of the PA8000 processor is clearly seen in the figures of Table 2, with the HP/PA9000-K460 recording the same time as the Cray Y-MP/C98, and marginally faster than the R10000/195 processor. Both processors outperform the Cray Y-MP/8128, with the HP/PA9000-K460 twice as fast as the R8000-based SGI machines and Power2 RS/6000-3CT. The latter machines exhibit identical performance on this benchmark (with 0% sparsity timings of 1.8 seconds). Both CPUs are marginally faster than the EV5 600/5/333 and 84000/5/300, 1.3 times faster than the Ultra-2/200 and 1.4 times faster than the HP PA/9000-735/125. Both exhibit comparable timings to that on the Y-MP/8128 (1.5 seconds), achieve some 50% of the YMP-C98 rating, and outperform the Cray YMP-J90 by a factor of 1.4. The performance of the R5000 processor is perhaps disappointing, four times slower than the R10000. The R1000-based Power Onyx is seen to outperform the corresponding R8000 machine by a factor of 1.6, which in turn outperforms the R4400 Challenge L by a factor of 3.9, and the earlier 50 Mhz R4000 Challenge by a factor of 5.6. Much of the speed of the R8000 may be attributed to the KAP pre-processor, which enhances FORTRAN performance by a factor of 1.8. The performance of the Pentium Pro/200 is again seen to be impressive, recording exactly the same benchmark time as the DEC Alpha 600/5-266 and Sun Ultra-2/200. Of historical note is the timing of 448.6 seconds recorded on the T800 20MHz Transputer, some 500 times slower than the PA8000 CPU and the Cray YMP-C98. Considering the MPP nodes, the IBM-SP2 TN2 node outperforms the Cray AXP node of the T3D by a factor of 4.7, and the KSR-2 by a factor of 6.6. Note that the T3D node remains 1.5 times slower than the DEC AXP/3000-500. One additional feature of the MMO benchmark not apparent in Table 2 is the significantly enhanced performance found on all machines when comparing assembly language MMO to Fortran MMO. Historically this has often involved the user having to code key routines in assembly language, for few vendors initially provided optimized mathematical libraries; improvement factors of 3.2 (Cray X-MP), 4.2 (IBM 3090/VF), 3.1 (Convex C-220) and 3.4 (FPS-M64/60) have previously been reported with assembly language implementations of the sparse MMO routine, MXMB. Optimized BLAS libraries are now fairly commonplace on the majority of workstation platforms, the most notable exception, until recently, being the SPARC offerings from Sun (note that the Cray-optimised libraries are now available on both Hyper- and UltraSPARC machines). The impact of optimized library routines continues to be evident on current workstations, with the 0% sparsity timings of Table 2 improving by factors of 1.8, 1.9, 2.3, 2.5, 2.7 and 3.1 for the SGI R10000/195, IBM RS/6000-3CT, SGI R8000 Indigo2, HP PA/9000-735/125, DEC Alpha 600/5/333 and Alpha 8400/5-300 respectively when using the BLAS dgemm routine. A corresponding factor of 3.8 is found when using the Kuck Library on the i860, and perhaps somewhat surprisingly, a factor of 4.5 when using SCILIB on the Cray YMP-C98. The availability and degree of functionality of library software are, we believe, important issues when considering cost-effective performance. This effect is evident from the second benchmark which, given in Table 3, involves performing a series of similarity transforms (Q*HQ) using both a scalar and vector algorithm. The scalar code collapses the matrix transposition and multiplications to yield an algorithm with fewer FLOPS than the vector code, which adopts a brute force approach by explicitly performing the transposition and two matrix multiplications. In the latter case we utilize the BLAS library routine DGEMM (where available) for performing the requisite MMOs, in the former case the dot product BLAS routine, DDOT. Considering the scalar algorithm, the SGI R10000 and HP PA/9000-K460 exhibit the optimum performance, with the R10000 1.1 times the speed of the Sun Ultra-2/200 and 1.2 times that of the IBM RS/6000-3CT. The Power2 CPU is marginally faster than the DEC 600/5/333, and 1.2 times faster than the R8000-based SGI machines and the Sun Ultra-1/170. With a couple of notable exceptions. this ordering is similar to that found for the vector case, where the R10000 just outperforms the Sun Ultra-2/200 and the DEC 600/5/333, which are in turn superior to both the IBM RS/6000-590 and R8000-based SGI machines. The performance of the R10000 in the scalar algorithm is first class, faster than the Cray Y-MP/J90, Cray Y-MP/8128 and Cray YMP C98/4256 by factors of 4.3, 2.3 and 1.2 respectively. The Pentium Pro/200 again fares well, recording comparable scalar timings to the DEC Alpha 600/5-266, Sun Ultra-1/140, and DEC Alpha 2100/5-250, although 1.7 times slower than the R10000. The notably poor performance on the vector algorithm for the HP PA/9000-K460, SGI Indy 5000 and Pentium Pro/200 may be attributed to either use of poorly tuned maths libraries (as on the HP and SGI machine), or to reliance on straight Fortran code (as on the Pentium). A similar effect was seen on the RS/6000-3CT, where the relatively unimpressive timings from the vector algorithm were caused by the un-tuned library DGEMM routine available through -lblas; the corresponding ESSL routine (-lessl) improves performance by a factor of 1.52 on the IBM SP2. The Cray YMP-C98 at last justifies its supercomputer tag, outperforming the SGI R10000, R8000 and RS/6000-3CT by factors of 2.6, 3.2 and 3.6 respectively; the same cannot be said for the Cray Y-MP/J90, which remains a factor of 1.7 slower than the R10000. The latter outperforms the R5000 by factors of 3.4 (scalar) and 5.3 (vector). As noted above all of the more recent CPUs (with the exception of the PA/9000-K460 and Pentium Pro) exhibit superior performance on the vector algorithm, with average factors of 2.1 (DEC Alpha 600/5-333), 2.0 (SUN Ultra-1/170), 1.8 (DEC Alpha 8400/300 and HP PA/9000-735), 1.7 (SGI R10000, SUN Ultra-2/200 and SGI R8000), and 1.6 (IBM RS/6000, with the exception of the 590). Much higher factors are found with the vector CPUs; the figure of 4.2 on the Cray J90 leads to the Cray being competitive on the vector algorithm, but slower than the leading 24 workstations on the scalar code. Note again the inadequacies of the FORTRAN compiler on the Cray T3D; the unexceptional scalar algorithm timing of 79.3 secs. (to be compared with that of 45.1 secs. on the DEC AXP/3000-500), improves significantly with the vector algorithm, where use of the BLAS routines produces timings of 27.5, to be compared with 23.9 secs on the AXP/3000-500. This effect is also seen in comparison with the KSR-2 and IBM SP2 timings; the T3D is slower by a factor of 1.4 on the scalar algorithm, and faster by a factor of 1.3 in the vector case compared to the KSR-2, while the differential with the SP2 is reduced from 5.8 in the scalar case to 3.3 for the vector algorithm. 2.3 Diagonalization Benchmark Table 4 presents the results of a matrix diagonalization benchmark intended to supplement the previous analysis conducted by Dunning and co-workers [4]. We consider a similar benchmark, based on diagonalizing a series of real symmetric matrices, with rank 10, 20, 30, ... 100, using 64-bit floating point arithmetic. Again the CPU time was measured for the diagonalization of each size matrix, with the summed times used as the benchmark execution time. Results are presented for a range of compiler options available on the depicted hardware. While the previous analysis was restricted to the EISPACK RS routine, we consider below the performance of eight diagonalization routines available in various mathematical libraries and quantum chemistry codes: (i) EIGRS, an unoptimized FORTRAN version of the library routine RS (available in SCILIB on the Cray) from the IMSL library of routines [5]. (ii) F02ABF from the NAG library [6]. (iii) HQRII [7], as implemented in the semiempirical MOPAC program [8]. (iv) GIVENS, adapted from the QCPE program exchange (number 62.1). (v) SDIAG2, as implemented in the MUNICH system of programs. (vi) JACOBI, from the ATMOL system of programs [9]. (vii) JACO, the diagonalization routine from the direct-SCF program DISCO [10]. (viii) ERDUW, as taken from the Berkeley System of Quantum Chemistry codes. The first four routines are all based on the Householder QR method, whilst the last four use the Jacobi method. Note that the only optimization performed involved inserting calls to the BLAS for two-dimensional rotations (DROT) and vector interchange (DSWAP). The timings of Table 4 suggest that the SGI R10000/195 is again the fastest CPU, just ahead of the HP PA/9000-K460 and DEC Alpha 600/5-333, and 1.5 times faster than the Sun Ultra-2/200 and DEC Alpha 8400/5-300. The R8000-based machines, HP/9000-735/125 and IBM RS/6000-3CT are seen to exhibit comparable timings (8.0-8.1 secs.), a factor of 2.5 times slower than the R10000. This benchmark tends to be dominated by the slowest of the diagonalization routines in use, JACO, which typically accounts for > 40% of the total CPU time. Significantly the leading twenty workstations are seen to outperform the Cray YMP/C98, while the Cray Y-MP/J90 is bettered by the leading 44 workstation CPUs. The Pentium Pro is seen to be five times faster than the J90, and is only outperformed by the leading six workstation CPUs. The performance of the Cray T3D node is comparable to the IBM RS/6000-350, and again significantly slower than the IBM-SP2 TN2 node (21.7 secs. vs. 9.3 secs.) 2.4 Relative Performance on Matrix Operations To summarize the performance of the various workstations on the matrix multiply and diagonalization benchmarks detailed above, we show in Table 5 the performance of each relative to the SGI R10000/195. It is clear from these figures that the SGI R10000 and PA8000 processor (in the HP PA/9000-K460) lie ahead of the competition, with the R10000 1.3 times faster than the DEC Alpha 600/5-333 and 1.4 times faster than the Sun Ultra-2/200. While the PA8000 is marginally slower than the R10000, we belief that the arrival of tuned maths libraries on the former will reverse this order to provide a picture more consistent with the SPECfp95 ratings. We see that the relative ordering of processors within a given family are broadly in line with clock speeds. Considering the EV5-based CPUs, we find the DEC Alpha 600/5-333, 8400/5-300, 600/5-266 and 2100/5-250 to be slower than the R10000 by factors of 1.25, 1.49, 1.82 and 1.67 respectively. For the Sun Ultra, the Sun Ultra-2/200, Sun Ultra-1/170, and Sun Ultra-1/140 are slower than the R10000 by factors of 1.43, 1.69 and 2.05 respectively. We also note the following: - the 266 MHz EV5-based CPU outperforms the corresponding EV4 by a factor of 1.33; - the R10000 exhibits a speed up of 1.7 against its predecessor, the R8000 in the Power Onyx, and a speed up of 3.4 against the R5000 (due in the main to the poor library performance on the R5000); - the R8000 exhibits a speed up of 3.3 against its predecessor, the R4400 in both the Indigo2 and Challenge L, and, - the position of the Pentium Pro as the 14th fastest CPU of those considered will undoubtedly improve given the advent of optimised libraries (not available in this current exercise). 3. Computational Chemistry Kernels One of the crucial requirements in evaluating the increasingly broad range of hardware platforms, whether these be parallel machines (true MIMD message-passing machines, workstation clusters, shared-memory multiprocessors etc), or simply the lastest workstation, is the availability of portable benchmarking codes that are representative of the application area under consideration. In an attempt to provide such capabilities in computational chemistry, we have described previously four representative codes, each of which is less than 1000 lines of FORTRAN, and is sufficiently portable that migration to any hardware platform can typically be achieved in a matter of hours. In this report we limit our discussion to the performance of these codes on a variety of single CPUs. The benchmarking kernels comprise the following programs that are realistic models of actual chemical applications or algorithms; 1. Self Consistent Field (SCF); This Self Consistent Field (SCF) electronic structure kernel uses distributed primitive 1s gaussian functions as a basis (thus emulating use of s,p,... functions) and computes integrals to essentially full accuracy. It is a direct SCF code, with an atomic density used for a starting guess. There are two available problem sizes, corresponding to 60 basis functions Be4 and 240 basis functions (Be16). The timings of Table 6 refer to the former. 2. Molecular Dynamics (MD); This program bounces a few thousand argon atoms around in a box with periodic boundary conditions. Pairwise interactions (Leonard-Jones) are used with a simple integration of the Newtonian equations of motion. 3. Monte Carlo (MC); This code evaluates the energy of the simplest explicitly correlated electronic wavefunction for the He atom ground state using a variational monte-carlo method without importance sampling. 4. Jacobi iterative linear equation solver (JACOBI)]; Uses a naive jacobi iterative algorithm to solve a linear equation. All the time is spent in a large matrix vector product. Total CPU timings for the SCF, MD and MC benchmarks are presented in Table 6, together with the Mflop ratings from the JACOBI benchmark. These results present a somewhat confusing picture, with the processor ordering very much a function of the particular chemistry kernel under examination. With the exception of the JACOBI benchmark, the SGI R10000/195, DEC Alpha 600/5-333 and 8400/5-300, and Sun Ultra-2/200 processors are seen to be the superior CPUs, although the processor ordering in the SCF, MD and MC benchmarks varies significantly. Thus for the SCF kernel, the SGI R10000 and Alpha 600/5-333 are 1.5 times the speed of the HP PA/9000-K460, twice the speed of the Sun Ultra-2/200 and three times that of the POWER2 RS/6000. The HP/9000-735/125 exhibits good relative performance - it is 1.3-1.4 times faster than the R8000 and POWER2 RS/6000 (TN2 and 3CT). The Sun SS20/HS21 is also seen to perform well on this kernel, with the same execution time as the latter CPUs. The performance of the Pentium Pro/200 matches that of the SGI R8000 and and POWER2 RS/6000. The SGI R10000 is clearly the optimum processor in the MD benchmark, followed by the Sun Ultra-2/200 and Alpha 600/5-333. The POWER2 is seen to perform very poorly in the MD Benchmark where it is apparently almost 4.5 times as slow as the R10000, with the 3CT 3.8 times slower than the Sun Ultra-2/200. In fact the whole family of RS6000 processors remains consistently unimpressive on this benchmark. The relative processor ordering found in the MD and SCF kernels is seen to be markedly different to that suggested by the JACOBI benchmark. The Mflop ratings of Table 6 suggest that the Power2 3CT is the optimum CPU, with the 81 Mflop rating 2.1 times that achieved on the SGI R10000, 2.5 times that found on the R8000 (32.9 Mflop), and 2.6 times that recorded on the HP PA/9000-K460 (31.0 Mflop). The Sun Ultra performance on JABOBI is also impressive, with the Ultra-2/200, and Ultra-1/170 and Ultra-1/140 all surpassing the MFlop rate on the EV5 and R10000 processors. In the MC benchmark, the Alpha 600/5-333 and SGI R10000 are the fastest processors, twice the speed of the RS/6000 POWER2 and Ultra-2/200. Comparing the R10000/195 and R8000 processors, the R10000 is 3.2 times faster in the SCF kernel, 2.3 in the MC kernel, but only 1.2 times faster in JACOBI. The Indy R5000 demonstrates comparable performance to the R8000 - it is slower than the R10000 in the SCF, MD, MC, and JACOBI benchmarks by factors of 2.9, 2.1. 1,9 and 3.1 respectively. The performance of the Pentium Pro/200 is perhaps somewhat less impressive than that found in the matrix benchmarks. Its overall performance is similar to that shown by the DEC Alpha 250/4-266 and Indy R5000, approximately one half the speed of the R10000. 4. The Quantum Chemistry Benchmark The benchmark described below (and summarized in Table 7) is designed to highlight the typical range of calculations commonly performed by the ab initio quantum chemist. It includes 12 calculations carried out using the GAMESS-UK electronic structure code, and includes the following functionality; - Conventional SCF Calculations, on morphine and 2,4,6 tri-nitro-toluene (Calculations 1. and 2. respectively); - Valence-only ECP calculations, with a geometry optimization of Na7Mg+ in an ECP-DZ+D(Mg) basis of 70 GTOs (calculation 3). - A Direct-SCF calculation on cytosine (82 GTOs, 6-31G basis) Calculation 4); - Multi-configuration SCF calculations, with a CASSCF geometry optimization on H2CO (Calculation 5), and a larger CASSCF calculation, also on H2CO (Calculation 6); - Configuration interaction calculations, both Direct-CI on the H2CO/H2+CO transition state (Calculation 7), and conventional table driven-CI on TiCl4 (Calculation 8); - Moller Plesset calculations, with a MP2 geometry optimization of H3SiNCO (Calculation 9); - Analytic second derivatives, at both the SCF (pyridine) molecule in a 6-31G basis, Calculation 10) and MP2 level (C4) in a 6-31G* basis, Calculation 11); - A Direct-MP2 calculation on pyridine in a DZ + D(N) basis set (Calculation 12). 4.1 QC Benchmarks - Single Processor Results The data presented in Table 8 is collected under control of the UNIX command time where available, and includes CPU time (both user and system), total elapsed time and Efficiency, measured as CPU versus elapsed. While our original aim was to base comparisons strictly on elapsed times, such timings could not be consistently gathered over the range of machines considered. For example, the range of disk configurations varies enormously, from primitive SCSI disks to striped high-speed raid disks, and the loading on the machines varies, from effectively single-user loading on many of the workstations, to multi-user environments, such as on the Convex. Thus while reporting the elapsed times, we use such figures to identify, where appropriate, the requirement for enhanced disk configurations, rather than as any definite criticism of the machine in question. The total user CPU timings of Table 8 suggest that the DEC Alpha 600-5/333 and SGI R10000/195 are the optimum machines, with summed timings of 23.4 and 23.1 minutes for all 12 calculations. These are significantly less than those for the leading machines from Sun and IBM which exhibit summed timings of 31.5 and 40.1 for the Sun Ultra-2/200 and RS/6000-3CT respectively. A somewhat different picture emerges when considering the system CPU and Elapsed times. With the exception of the AXP and SGI R10000, all machines exhibit a system CPU time of the order of 10-15% of the user time; this percentage increases significantly, particularly on the AXP, to between 20-40% on the systems of Table 8. Considering the elapsed times and associated efficiences. the R8000 based machines from SGI appear to be the most balanced. Both the R8000 Power Onyx and Indigo2 exhibit efficiences of > 95%, to be compared with figures of 76%, 64% and 59% for the RS/6000-3CT, 9000-735/125 and Alpha 600/5-266. What is noticeable is the significant improvements in these efficiency ratios on the more recent Sun (Ultra-2/200) and Digital hardware (both the DEC Alpha 600/5/333 and DEC Alpha 2100/5/250) compared to the figures recorded in the past. Initial experience with the HP PA/9000-K460 suggested that the compiler was too unreliable to even attempt to perform the GAMESS-UK Benchmark, with numerous routines mis-compiling. The poor elapsed time on the Sun Ultra-1/140 was due in part to the limited memory on the machine (32 MByte). Overall factors of 10.6 (in total CPU) and 13.1 (in elapsed times) are found when comparing the ``slowest'' (Sun SPARCstation 10/30) and ``fastest'' (SGI R10000 Power Onyx) machines in Table 8. The corresponding CPU factors in both the Matrix and Chemistry Kernels are somewhat higher, 18.9 and 11.2 respectively. Considering machines from a given vendor, we find that the EV5-based Alpha 600/5/266 outperforms the corresponding EV4 system, the 250/4/266 by factors of 1.5 (CPU) and 1.4 (elapsed). The R10000 from SG is found to be approximately twice the speed of the R8000-based machines, and 3.2 times as fast as the Indy R5000. The R8000-based machines in turn are found to perform some 1.9 times faster than the corresponding Power Challenge L and R4400 Indigo2. Significantly higher ratios are found in some of the individual calculations of Table 7, with ratios of 2.5 - 2.8 found in the MCSCF, direct-CI, MP2 and 2nd derivative calculations. Two of the 12 benchmark calculations impact seriously on the final ratios, the ECP and MRDCI calculations exhibiting negligible speedups of 1.13 and 1.18 respectively. 5. Summary As a summary of this work, we present in Table 9 the relative performance of 34 of the leading workstations against the DEC Alpha 600/5/333 in terms of quoted SPEC (``Systems Performance Evaluation Cooperative'') benchmarks, and those from the present Matrix, Chemistry Kernels and GAMESS-UK benchmarks. Note that this analysis has been somewhat complicated by the inconsistent approach adopted by some vendors to providing a smooth transition between the older SPECfp92 and SPECint92 values, and the more recent SPECfp95 and SPECint95 benchmark. SGI in particular appear to provide only SPECfp95 values for the R10000 and R5000, and only SPECfp92 values for their older processors. In the following discussion we have used the SPEC-95 results when available (see Table 9) The SPEC benchmark suite contains non-tuned application-based code to measure processor speed for both integer (SPECint) and floating point (SPECfp) arithmetic. Based on the published SPECfp ratings, and normalising with respect to the DEC Alpha 600/5/333, we would expect the PA8000 processor of the HP PA/9000-K460 (124%) to be the fastest CPU, followed by the DEC Alpha 600/5/333 itself, the SGI R10000/195 (94%) and DEC Alpha 8400/5/300 (94%), the DEC Alpha 600/5/266 CPU (89%), the 200 MHz Sun Ultra-2/200 (84%), and the IBM Power2 RS/6000-3CT (77%) (where the %-values in parentheses indicate performance relative to the Alpha 600/5/333). Based on these relative SPECfp values given in the table, we expect a factor of 12 between the fastest and slowest processor, the Sun SPARC/10-30. A somewhat different processor ordering is revealed by the SPECint ratings, with the processors from SGI (notably the R8000) and IBM (the Power2) significantly slower than those from DEC, Sun and the HP (PA8000). It is perhaps worth noting that SGI have not as yet published the SPECint95 rating for either the R10000 or R5000. We note also that the Specint95 ratings suggest that Pentium Pro/200 is some 88% of the DEC Alpha 600/5/333, to be compared with the smaller rating of 51% based on SPECfp95. When considering the results, there are several factors we wish to consider based on the present evaluation exercise: 1. Do the SPECfp values provide a reliable metric for evaluating the capabilities of hardware in computational chemistry? If this so, we would expect to find a close mapping of the ratios for the various chemistry benchmarks onto the SPECfp ratios (note that the CPU values for the GAMESS-UK benchmark are used, since SPEC does not adequately incorporate I/O in its evaluation); 2. Does any particular CPU consistently ``underperform'' based on the SPECfp criteria? - this would manifest itself as the ratios from the chemistry benchmarks falling below the SPECfp ratios; 3. Do the ``simple'' Matrix and Chemistry Kernel benchmarks lead to the same conclusions as the GAMESS-UK benchmarks? In terms of relative speed, we find that all the chemistry benchmarks are broadly in line with the SPECfp predictions, the only notable exceptions being summarised below: - The poor performance of the HP PA/9000-K460 - while the matrix kernels are in line with the SPECfp ratio, the chemistry kernels would appear to be running at around half the expected speed. - The impressive performance of the SGI R10000 on all the benchmarks, with figures of 130%, 104% and 116% on the matrix, kernels and GAMESS-UK benchmarks, as against the value of 94% based on the SPECfp95 figures. Indeed, the R10000 would certainly appear to be the optimum CPU based on the benchmarks conducted in this report. The R5000 appears, however, to follow closely the SPECfp95 ratings. - There is some evidence from Table 9 that neither the DEC EV5-based Alpha 600/266 or the 8400/300 are performing as well as the SPEC ratings might suggest, with all benchmark ratios some 10% lower than expected based on SPECfp alone. In contrast the DEC Alpha 2100/5/250 is performing consistently above its SPEC rating. - The UltraSPARC systems from Sun appear to be performing exactly in line with the SPECfp95 ratings. - The R8000-based systems perform well on the matrix benchmarks, but are closer to the SPECfp ratings on the kernels and GAMESS-UK. - All IBM Power1 RS/6000 CPUs exhibit enhanced performance on the chemistry codes relative to the SPEC rankings e.g.. the SPECfp ratio of the RS/6000-370 to DEC 600/5 is 22%, to be compared with the Matrix, Kernels and GAMESS-UK ratios of 30%, 37% and 36% respectively. - A similar effect is shown with the HP PA/9000-735/125, where the SPECfp ratio of 35% increases to 54%, 40% and 52% for the chemistry benchmarks. - The potential of the Pentium Pro is evident, with the performance in the Matrix benchmarks exceeding the SPECfp95 figures. References [1] M.F. Guest and S. Wilson, Daresbury Laboratory Preprint, DL/SCI/P290T; Supercomputers in Chemistry, ed. P.Lykos and I.Shavitt, A.C.S. Symposium series 173 (1981) 1; V.R. Saunders and M.F. Guest, Comp.Phys.Comm., 26 (1982) 389: M.F. Guest, in ``Supercomputer Simulations in Chemistry'', Ed. M. Dupuis, Lecture Notes in Chemistry, 44, Springer Verlag (1986) 98. [2] M.F. Guest, R.J. Harrison, J.H. van Lenthe and L.C.H. van Corler, ``Computational Chemistry on the FPS-X64 Scientific Computers: Experience on single- and multi-processor systems'', Theoret. Chim. Acta 71 (1987) 117. [3] GAMESS-UK is a package of ab initio programs written by M.F. Guest, J.H. van Lenthe, J. Kendrick, K. Schoeffel and P. Sherwood, with contributions from R.D. Amos, R.J. Buenker, M. Dupuis, N.C. Handy, I.H. Hillier, P.J. Knowles, V. Bonacic-Koutecky, W. von Niessen, R.J. Harrison, A.P. Rendell, V.R. Saunders, and A.J. Stone. The package is derived from the original GAMESS code due to M. Dupuis, D. Spangler and J. Wendoloski, NRCC Software Catalog, Vol. 1, Program No. QG01 (GAMESS), 1980. [4] R. Shepard, R.A. Bair, R.A. Eades, A.F. Wagner, M.J. Davis, C.B. Harding and T.H. Dunning Jr., Int. J. Quant. Chem. , (1983) 17; R.A. Bair and T.H. Dunning Jr., J.Comp.Chem., 5 (1984) 44; R. Bair, ``FPS-164 Matrix Multiplication Subroutine Guide'', Argonne National Laboratory, (1984); T.H. Dunning Jr. and R.A. Bair, ``Advanced Theories and Computational Approaches to the Electronic Structure of Molecules'', NATO ASI Series, D. Reidel, 1984, p1. [5] IMSL Program Library, 1978. [6] NAG Fortran Library, Numerical Algorithms Group Ltd, 1984. [7] Y. Beppu, Computers and Chemistry, 6 (1982). [8] J.P. Stewart, ``MOPAC - A General Molecular Orbital Package'', QCPE 455, 1984. [9] V.R. Saunders and M.F. Guest, ``ATMOL3 Part 9'', RL-76-106, 1976; M.F. Guest and V.R. Saunders, Mol.Phys., 28 (1974) 819; D. Moncrieff and V.R. Saunders, ``ATMOL-Introduction Notes'', UMRCC, May, 1986; Cyber-205 Note Number 32, UMRCC, September, 1985. [10] J. Almlof et al, J.Comp.Chem., 3 (1982) 385. Performance of Various Computers in Computational Chemistry Table 1. Vector Whetstone Benchmark. Total CPU times (seconds), Mflop ratings (see text) and MWIP ratings ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Machine Mflop Ratings (VL=1024) Total CPU MWIPS N2 N3 N8 (seconds) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ iPSC/860 (RX) 4.6 4.4 5.3 282.7 15.0 Cray T3D (AXP) 9.8 8.4 7.3 120.5 33.5 KSR-2 32.8 31.3 29.7 68.6 59.6 IBM SP2 (TN2 node) 49.2 38.2 49.3 35.0 116.5 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Sun 4/370 2.0 1.8 1.0 642.8 6.3 SOLBOURNE S4000 1.4 1.3 0.9 629.3 6.4 Sun SPARCstation 2/GS 4.3 3.2 1.8 366.7 11.0 DEC S5000/120 4.5 4.0 2.7 351.3 11.5 IBM RS/6000-320 9.4 2.4 1.3 305.3 13.3 SGI 4D/220 5.6 4.4 2.9 289.5 14.0 Stardent 1520 6.3 5.9 6.0 283.2 14.5 DEC S5000/200 6.4 5.4 3.4 274.7 14.8 SGI R3000 Indigo 6.8 3.7 3.7 233.8 17.3 SGI 4D/320 7.6 5.9 3.6 227.0 18.0 Sun SPARC/5-85 3.3 3.1 4.3 205.5 19.7 Stardent VISTRA-800 4.7 4.4 5.3 196.5 19.0 SGI 4D/420 9.4 6.9 4.4 188.0 21.6 Sun SPARC/10-30 7.0 3.4 4.0 177.2 21.0 Sun SPARC/10-41 9.4 8.1 5.3 145.0 26.5 SGI IRIS Crimson 21.8 14.3 6.1 121.7 33.7 IBM RS/6000-540 15.1 7.7 5.7 120.3 33.8 HP PA/9000-720 11.6 11.1 9.3 119.3 34.0 IBM RS/6000-340 17.9 4.0 6.1 115.9 34.8 Sun SPARCserver 1000 12.5 10.9 6.5 111.6 33.8 SGI R4000 Indigo 21.8 13.6 6.4 105.8 38.1 IBM RS/6000-530H 16.4 10.4 6.8 102.0 39.9 RS/6000 PowerPC-250 15.1 8.4 5.4 95.0 38.7 IBM PowerPC-25T 15.1 8.4 6.4 93.2 41.1 HP PA/9000-730 14.0 14.2 11.5 93.4 43.4 SGI Challenge L/100 21.8 13.6 9.1 90.8 43.7 IBM RS/6000-550 21.8 10.8 7.9 86.8 46.6 IBM RS/6000-350 19.7 8.0 7.7 84.8 46.9 DEC AXP/3000-300 19.4 15.7 10.3 79.1 50.9 IBM RS/6000-360 24.6 9.0 9.2 70.7 56.1 DEC AXP/3000-500 20.8 16.9 10.7 68.7 58.7 DEC AXP/3000-600 26.2 19.8 12.7 64.4 62.8 HP PA/9000-750 15.1 27.5 24.3 64.1 63.6 SGI R4400 Indigo^2 32.7 19.9 13.1 62.3 63.8 SGI Challenge L/150 32.8 20.5 14.2 62.9 63.3 IBM RS/6000-370 32.8 12.0 11.5 56.5 70.2 HP PA/9000-715/100 32.8 29.9 15.8 54.5 73.3 Sun SPARCstation-20/HS21 30.0 25.5 18.2 49.8 81.6 HP PA/9000-J200 39.3 32.8 33.9 41.0 99.2 HP PA/9000-755 39.3 44.4 30.9 38.9 104.7 DEC AXP/3000-700 34.1 25.6 23.6 36.5 111.1 RS/6000 PowerPC-43P 21.9 9.8 24.2 36.4 92.2 SGI R8000 Indigo^2 27.0 26.2 109.2 36.4 112.4 SGI R8000 PowerOnyx 27.1 26.2 109.6 36.2 112.8 IBM RS/6000-590 32.8 31.3 44.2 34.8 117.3 IBM RS/6000-3CT 39.3 37.2 46.8 34.4 117.6 IBM RS/6000-3BT 39.2 37.2 47.2 34.2 118.9 Pentium Pro/200 41.8 35.2 21.7 32.3 123.7 HP PA/9000-735/125 39.3 57.3 38.9 30.8 132.0 DEC Alpha 250/4-266 36.6 34.8 72.5 26.9 150.5 Sun Ultra-1/140 19.9 19.3 170.9 26.3 155.1 Sun Ultra-2/200 29.8 27.0 239.6 18.7 218.2 DEC Alpha 2100/5-250 143.9 39.4 107.4 17.0 238.1 DEC Alpha 600/5-266 83.9 83.9 126.3 15.8 256.1 DEC Alpha 8400/5-300 94.4 97.9 133.5 14.2 286.7 HP PA/9000-K460 196.6 196.6 190.5 13.8 298.3 DEC Alpha 600/5-333 201.4 52.4 138.7 12.8 311.0 SGI PowerOnyx 10000/195 118.6 76.0 139.2 8.5 476.8 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Convex C-220 21.4 17.7 15.7 59.6 70.0 Convex C-3860 54.0 44.3 37.8 23.0 185.2 CRAY YMP/J90 115.9 117.9 145.9 16.6 260.2 CRAY Y-MP/8128 190.0 193.5 297.2 7.9 552.0 CRAY YMP-C98/4256 542.1 539.3 668.7 3.9 1114.0 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Table 2. Sparse MMO Benchmark: Total CPU times (seconds) for a series of sparse MMOs (R = A X B, see text) implemented in Fortran. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Machine Sparsity in B-Matrix 0% 50% ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ iPSC/860 RX-node 21.2 11.0 KSR-2 11.3 5.9 Cray T3D AXP-node 8.5 4.5 IBM SP2 TN2-node 1.8 1.1 Solbourne S4000 74.4 38.4 Sun 4/370 57.4 30.6 DEC S5000/120 50.7 25.9 HP/Apollo DN10020 44.2 22.8 SGI 4D/220 39.4 20.1 DEC S5000/200 33.1 17.1 SGI R3000 Indigo 31.0 14.3 SGI 4D/320 27.9 14.4 Sun SPARCstation 2/GS 27.5 14.4 Stardent 1520 26.6 17.0 SGI 4D/420 23.7 12.1 Stardent VISTRA-800 20.4 10.6 Sun SPARCstation 10/30 16.1 7.9 Sun SPARCstation 5/85 14.9 7.8 Stardent 3020 14.7 9.1 IBM RS/6000-320 14.6 7.1 SGI R4000 Indigo 12.6 6.4 SPARCstation 10/41 11.8 6.1 SGI IRIS Crimson 11.0 5.6 SGI Challenge L/100 10.0 4.9 IBM RS/6000-530 9.3 4.9 Sun SPARCserver 1000 9.3 4.7 IBM PowerPC-25T 9.2 4.3 RS/6000 PowerPC-250 9.1 4.4 IBM RS/6000-340 8.9 4.1 HP PA/9000-720 8.5 4.8 IBM RS/6000-530H 7.8 4.1 IBM RS/6000-350 7.6 3.6 SGI R4400 Indigo^2 7.1 3.4 SGI Challenge L/150 7.1 3.4 HP PA/9000-730 6.7 3.5 IBM RS/6000-360 6.4 3.1 DEC AXP/3000-300 6.1 3.3 DEC AXP/3000-500 5.7 2.9 IBM RS/6000-550 5.6 3.1 HP PA/9000-750 5.1 2.7 IBM RS/6000-370 5.1 2.4 DEC AXP/3000-600 5.0 2.6 RS/6000 PowerPC-43P 4.3 2.0 HP PA/9000-715/80 3.7 2.0 DEC AXP/3000-700 3.7 1.8 Sun SPARCstation 20/HS21 3.5 1.8 Sun Ultra-1/140 3.4 1.7 DEC Alpha 250/4/266 3.2 1.6 HP PA/9000-755 3.1 1.7 HP PA/9000-715/100 3.0 1.6 Sun Ultra-1/170 2.8 1.4 DEC Alpha 2100/5/250 2.6 1.3 HP PA/9000-735/125 2.5 1.3 Sun Ultra-2/200 2.3 1.2 DEC Alpha 600/5-266 2.3 1.2 Pentium Pro/200 2.3 1.2 HP PA/9000-J200 2.1 1.1 DEC Alpha 8400/5-300 2.1 1.1 IBM RS/6000-3BT 2.1 1.1 IBM RS/6000-590 1.9 1.0 DEC Alpha 600/5-333 1.9 1.0 SGI R8000 Indigo^2 1.8 1.1 SGI R8000 Power Onyx 1.8 1.1 IBM RS/6000-3CT 1.8 1.0 SGI PowerOnyx 10000/195 1.1 0.6 HP PA/9000-K460 0.9 0.5 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Alliant FX2808 (1CE) 17.2 9.3 FPS-M64/60 17.1 8.9 Convex C-220 10.6 6.5 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ IBM 3090-600-E/VF 11.3 6.0 Convex C-3860 5.2 3.3 Cray X-MP/416 2.8 1.8 Cray YMP/J90 2.6 1.8 Cray Y-MP/8128 1.5 0.9 CRAY YMP C98/4256 0.9 0.6 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Table 3. Sparse MMO Benchmark: Total CPU times (seconds) for a series of Similarity Transformations (H=Q*HQ, see text) using both Scalar and Vector Algorithms. Machine Algorithm Scalar Vector ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ T800-20 5176.0 4439.0 iPSC/2 SX-node 3809.6 4456.1 Meiko MK086 node 240.1 262.2 iPSC/860 RX-node 118.8 60.1 KSR-2 55.3 34.8 Cray T3D AXP-node 79.3 27.5 IBM SP2 TN2-node 13.8 8.5 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ SOLBOURNE S4000 711.9 750.2 Dec S5000/120 710.2 724.6 Sun 4/370 615.9 609.6 Dec S5000/200 368.3 445.1 SGI 4D/220 344.8 440.3 Sun SPARCstation 2/GS 333.3 394.0 SGI R3000 Indigo 285.0 356.4 SGI 4D/320 245.5 329.5 HP/Apollo DN10020 273.8 245.9 Stardent 1520 236.9 252.8 SGI 4D/420 200.1 273.8 Stardent VISTRA-800 177.0 209.4 Stardent 3020 160.4 144.2 Sun SPARCstation 10/30 141.5 162.3 Sun SPARCstation 10/41 111.3 119.3 Sun SPARCstation 5/85 105.9 151.6 Sun SPARCserver 1000 88.8 92.6 SGI Indigo R4000 98.2 78.7 SGI IRIS Crimson 94.2 78.7 IBM RS/6000-320 139.0 73.3 IBM PowerPC-25T 88.2 68.9 RS/6000 PowerPC-250 92.5 68.7 HP PA/9000-720 124.7 66.5 SGI Challenge L/100 74.9 58.2 HP PA/9000-730 89.9 48.4 IBM RS/6000-530 96.3 46.9 IBM RS/6000-340 70.4 43.4 HP PA/9000-750 58.6 41.5 SGI Indigo^2 R4400 52.3 39.8 SGI Challenge L/150 56.5 39.0 IBM RS/6000-540 66.9 38.4 IBM PowerPC-43P 44.3 36.4 Sun SPARCstation 20/HS21 42.2 35.1 IBM RS/6000-350 55.8 34.8 HP PA/9000-715/80 52.7 34.4 DEC AXP/3000-300 61.0 34.0 IBM RS/6000-530H 58.8 34.0 SGI Indy R5000 34.7 32.1 HP PA/9000-715/100 47.0 29.5 IBM RS/6000-360 48.0 29.3 IBM RS/6000-550 48.3 27.7 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ DEC AXP/3000-500 45.1 23.9 HP PA/9000-735 34.8 20.0 IBM RS/6000-370 37.6 23.3 DEC AXP/3000-600 38.5 20.2 HP PA/9000-755 35.6 20.1 Pentium Pro/200 17.1 23.2 HP PA/9000-J200 33.5 16.2 IBM RS/6000-3BT 30.8 16.2 HP PA/9000-735/125 29.2 16.2 DEC AXP/3000-700 28.3 16.2 DEC Alpha 250/4-266 24.2 13.1 HP PA/9000-K460 10.9 12.0 DEC Alpha 600/5-266 17.7 10.2 IBM RS/6000-3CT 12.6 9.7 Sun Ultra-1/140 17.1 9.5 SGI R8000 Indigo^2 15.2 8.7 SGI R8000 Power Onyx 15.1 8.7 DEC Alpha 2100/5-250 17.7 8.2 Sun Ultra-1/170 15.5 7.9 DEC Alpha 8400/5-300 13.8 7.9 Sun Ultra-2/200 11.5 6.8 DEC Alpha 600/5-333 13.2 6.2 SGI PowerOnyx 10000/195 10.2 6.1 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Alliant FX2800 (1CE) 243.1 86.3 FPS-M64/60 141.2 50.8 CONVEX C-220 128.6 49.0 CONVEX C-3860 55.1 20.1 Cray YMP/J90 44.1 10.6 Cray Y-MP/8128 23.2 5.7 Cray YMP C98/4256 12.6 2.6 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Table 4. Matrix Diagonalization Benchmark. Total CPU times (seconds) for a series of Matrix Diagonalizations (see text) using eight Different Routines. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Machine CPU Time Compiler (seconds) Options ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ T800-20 1100.5 iPSC/2 (SX) 1059.0 -OLM MK086 i860 66.8 -OLM iPSC/860 (RX) 62.3 -O3 KSR-2 27.3 -O2 Cray T3D (AXP) 21.7 -O IBM SP2 (TN2) 9.3 -O3 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Stardent 1520 255.0 -O2 Sun 4/370 219.9 -O SOLBOURNE S4000 169.0 -O3 DEC S5000/120 145.8 -O2 Sun SPARCstation 2/GS 103.3 -O SGI 4D/220 97.4 -O2 Apollo DN10020 96.2 -O DEC S5000/200 90.6 -O2 SGI R3000 Indigo 78.8 -O2 Stardent 3020 68.1 -O2 SGI 4D/320 67.2 -O2 Stardent VISTRA 59.2 -O3 SGI 4D/420 58.1 -O2 IBM RS/6000-320 46.4 -O Sun SPARCstation 10/30 42.2 -O -dalign Sun SPARCstation 10/41 35.9 -O -dalign IBM RS/6000-530 35.6 -O Sun SPARCstation 5/85 32.4 -O -dalign HP PA/9000-720 31.9 -O IBM RS/6000-540 30.8 -O SGI IRIS Crimson 27.6 -sopt IBM RS/6000-340 27.6 -O IBM RS/6000-530H 26.0 -O SGI R4000 Indigo 26.0 -O -mips2 RS6000 PowerPC-250 25.1 -O Sun SPARCserver 1000 24.7 -O -dalign HP PA/9000-730 24.3 -O IBM PowerPC-25T 24.0 -O3 IBM RS/6000-550 22.2 -O IBM RS/6000-350 21.3 -O HP PA/9000-750 20.8 +O3 SGI Challenge L/100 20.4 -O -mips2 IBM RS/6000-360 17.7 -O HP PA/9000-715/80 15.9 +O3 SGI Challenge L/150 15.6 -O -mips2 DEC AXP/3000-300 15.5 -O SGI R4400 Indigo^2 15.2 -O -mips2 DEC AXP/3000-500 14.9 -O IBM RS/6000-370 14.2 -O3 HP PA/9000-715-100 12.8 +O3 Sun SS20/HS21 12.4 -O -dalign Sun SPARCstation 20/HS21 12.4 -O -dalign DEC AXP/3000-600 12.1 -O IBM PowerPC-43P 11.2 -O3 qarch=ppc HP PA/9000-735 10.0 +O3 HP PA/9000-755 10.2 +O3 HP PA/9000-J200 9.6 +O4 IBM RS/6000-3BT 9.4 -O3 qarch=pwr2 IBM RS/6000-590 9.0 -O3 qarch=pwr2 DEC AXP/3000-700 8.7 -O IBM RS/6000-3CT 8.1 -O3 qarch=pwr2 HP PA/9000-735/125 8.1 +O3 SGI R8000 Indigo^2 8.0 -O3 -mips4 SGI R8000 Power Onyx 8.0 -O3 -mips4 SGI Indy R5000 7.7 -O3 -mips4 DEC Alpha 250/4-266 7.3 -fast Sun Ultra-1/140 6.2 -fast -O4 DEC Alpha 600/5-266 5.6 -fast Sun Ultra-1/170 5.4 -fast -O4 Pentium Pro/200 5.3 -G6 -O2 DEC Alpha 2100/5-250 5.2 -fast DEC Alpha 8400/5-300 4.6 -fast Sun Ultra-2/200 4.5 -fast -O4 DEC Alpha 600/5-333 3.9 -fast HP PA/9000-K460 3.5 +Oaggressive SGI PowerOnyx 10000/195 3.1 -O3 -mips4 etc ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Convex C-220 86.5 -O2 Alliant FX2808 99.5 -Og FPS-M64/60 80.7 OPT3 Convex C-3860 36.0 -O2 Cray X-MP4/16 21.3 Cray YMP/J90 26.7 Cray Y-MP4/64 16.0 Cray YMP C98/4256 9.9 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Table 5. The Matrix Benchmark: Performance relative to the SGI Power Onyx R10000/195 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Machine MMO MMO Diagonal Total (FORTRAN) (Q*HQ) -ization ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ SGI R10000/195 100% 100% 100% 100% HP PA/9000-K460 123% 57% 90% 90% DEC Alpha 600/5-333 61% 99% 80% 80% Sun Ultra-2/200 50% 90% 70% 70% DEC Alpha 8400/5 300 55% 78% 68% 67% DEC Alpha 2100/5 250 44% 75% 60% 60% Sun Ultra-1/170 41% 78% 58% 59% SGI R8000 Power Onyx 63% 71% 39% 58% SGI R8000 Indigo^2 63% 71% 39% 58% IBM SP2 (TN2 node) 63% 72% 35% 57% DEC Alpha 600/5-266 50% 60% 56% 55% IBM RS/6000-3CT 63% 64% 38% 55% Sun Ultra-1/140 33% 64% 50% 49% Pentium Pro/200 50% 36% 59% 48% HP/9000-J200 55% 38% 32% 42% IBM RS/6000-3BT 54% 38% 33% 42% DEC Alpha 250/4-266 36% 47% 43% 42% HP PA/9000-735/125 46% 38% 39% 41% Cray YMP/J90 44% 58% 12% 38% DEC AXP/3000-700 31% 38% 36% 35% HP PA/9000-755 37% 31% 31% 33% SGI Indy R5000 25% 22% 41% 29% HP PA/9000-715/100 38% 21% 34% 28% DEC AXP/3000-600 23% 30% 26% 26% Sun SparcStation-20/HS21 33% 17% 25% 25% IBM PowerPC-43P 27% 17% 28% 24% IBM RS/6000-370 22% 26% 22% 24% HP PA/9000-715/80 31% 18% 20% 23% DEC AXP/3000-500 20% 26% 21% 22% IBM RS/6000-360 20% 21% 18% 20% DEC AXP/3000-300 19% 18% 20% 19% IBM RS/6000-550 20% 21% 14% 19% HP PA/9000-750 22% 15% 15% 18% SGI R4400 Indigo^2 16% 15% 21% 17% SGI Challenge L/150 16% 15% 20% 17% Cray T3D AXP 13% 22% 14% 16% IBM RS/6000-350 15% 18% 15% 16% IBM RS/6000-340 13% 14% 11% 13% SGI Challenge L/100 11% 11% 15% 12% IBM PowerPC-25T 12% 9% 13% 12% Sun SPARCserver-1000 12% 7% 13% 11% HP PA/9000-720 13% 9% 10% 11% SGI R4000 Indigo 9% 8% 12% 10% Sun SparcStation-10/41 10% 6% 9% 8% Sun SparcStation-5/85 8% 6% 10% 8% IBM RS/6000-320 8% 8% 7% 8% Sun SparcStation-10/30 7% 4% 7% 6% Stardent VISTRA 6% 3% 5% 5% SGI 4D/420 5% 3% 5% 4% SGI 4D/320 4% 3% 5% 4% SGI R3000 Indigo 4% 2% 4% 3% Sun SPARCstation-2 4% 2% 3% 3% DEC S5000/200 3% 2% 3% 3% Stardent 1520 4% 3% 1% 3% Apollo DN10020 3% 2% 3% 3% SGI 4D/220 3% 2% 3% 3% DEC S5000/120 2% 1% 2% 2% Solbourne S4000 2% 1% 2% 1% ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Table 6. Computational Chemistry Kernels ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Machine Total CPU time (secs) Mflop ===================== SCF MD MC Jacobi ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Sun SPARCstation 10/30 758 2214 299 6.9 Sun SPARCstation 10/41 665 1788 266 6.6 Sun SPARCstation 5/85 1036 1082 195 6.7 Sun SPARCserver 1000 441 783 148 6.7 RS6000 Power-PC 250 603 1322 149 15.6 IBM RS/6000-530H 649 1594 176 26.7 IBM RS/6000-340 692 1608 187 21.2 IBM Power-PC 25T 409 1322 145 15.6 SGI Challenge L/100 400 794 130 9.5 SGI R4000 Indigo 375 809 112 9.5 DEC AXP/3000-300 427 716 98 12.0 IBM RS/6000-350 523 1288 141 27.4 HP PA/9000-750 237 554 163 11.5 %IBM RS/6000-550 563 1266 149 39.9 IBM RS/6000-360 433 1071 117 35.1 HP PA/9000-715/80 220 427 133 17.1 SGI Challenge L/150 233 517 78 13.3 SGI R4400 Indigo^2 234 523 78 13.7 DEC AXP/3000-600 314 541 79 21.6 HP 9000/715-100 177 354 107 17.3 IBM Power-PC 43P 135 470 56 14.8 IBM RS/6000-370 344 854 93 44.2 HP PA/9000-J200 121 292 92 12.4 Sun SPARCstation 20/HS21 132 319 84 16.3 HP PA/9000-755 131 284 102 18.6 DEC AXP/3000-700 146 411 54 21.7 HP PA/9000-735/125 103 236 91 15.0 SGI Indy R5000 124 219 42 12.5 SGI R8000 Indigo^2 139 218 49 23.7 Pentium Pro/200 142 321 38 28.4 SGI Power Onyx R8000 138 218 49 32.9 DEC Alpha 250/4/266 79 272 37 20.9 IBM/RS6000-3BT 140 389 43 44.8 Sun Ultra-1/140 110 186 61 45.2 IBM RS/6000-590 146 453 43 86.0 Sun Ultra-1/170 98 146 58 47.3 DEC Alpha 2100/5/250 59 187 28 27.3 IBM SP2 TN2-node 138 383 43 68.6 HP PA/9000-K460 68 138 31 31.0 DEC Alpha 600/5/266 57 210 31 40.9 IBM RS/6000-3CT 138 462 42 81.1 Sun Ultra-2/200 82 121 49 56.5 DEC Alpha 8400/5/300 50 183 27 43.9 DEC Alpha 600/5/333 46 137 21 44.9 SGI PowerOnyx 10000 43 106 22 39.1 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ CONVEX C-220 1015 1017 329 33.1 CONVEX C-3860 453 342 141 69.9 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Table 7. The GAMESS-UK Single Processor Benchmark Number Module Basis (GTOs) Details Molecular Species ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 1. SCF STO-3G (124) Morphine 2. SCF 6-31G (154) C6H3(NO2)3 3. ECP Geometry ECPDZ (70) Na7Mg+ Optimization 4. Direct-SCF 6-31G (82) Cytosine 5. CASSCF TZVP (52) 480 csf H2CO Geometry Opt. 6. MCSCF (5s3p2d/3s1p/f(O) 5608 H2CO (74) 7. Direct-CI (5s3p2d/3s1p) 3M/167194 H2CO/H2+CO TS (64) 8. Table-CI (26M/6R) ECP (59) 2301815/4097 TiCl4 9. MP2 Geometry 6-31G* (70) H3SiNCO Optimization 10. SCF Second 6-31G (64) C5H5N Derivatives 11. MP2 Second 6-31G* (60) C4 Derivatives 12. Direct-MP2 DZ+D(N) (76) C5H5N ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Table 8. The GAMESS-UK Benchmark: Total CPU time (user and system) Elapsed time (minutes) and Efficiency (%) for Calculations 1 - 12 (see text) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Machine CPU Time Elapsed Efficiency User System Time (%) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ DEC Alpha 600/5/333 23.4 7.6 34.4 91% SGI PowerOnyx 10000 23.1 3.6 36.7 73% Sun Ultra-2/200 31.5 3.0 38.1 91% DEC Alpha 2100/5/250 30.2 8.2 43.6 88% SGI R8000 Power Onyx 48.1 5.6 55.2 97% Sun Ultra-1/170 40.5 4.9 56.3 81% IBM RS/6000-3CT 40.1 4.4 58.3 76% SGI Indigo^2 R8000 50.8 7.2 60.6 96% IBM RS/6000-590 46.6 4.7 74.8 68% DEC Alpha 600/5/266 30.6 6.4 62.2 59% DEC Alpha 250/4/266 46.0 9.7 85.7 65% DEC Alpha 8400/5/300 26.9 10.4 92.8* 40% HP PA/9000-735/125 51.6 8.4 93.3 64% HP PA/9000-755 64.9 8.8 98.2 75% SGI Indy R5000 72.9 11.3 101.0 83% DEC AXP/3000-700 53.4 11.5 105.4 62% DEC AXP/3000-600 72.1 14.1 107.8 80% IBM RS/6000-370 78.7 8.4 108.8 80% SGI R4400 Challenge L/150 93.3 10.3 110.6 94% SGI R4400 Indigo^2 94.3 10.0 120.6 86% HP 9000/715-100 81.4 14.3 124.7 77% Sun Ultra-1/140 45.3 5.7 139.8* 36% HP PA/9000-715/80 93.7 15.9 140.1 78% DEC AXP/3000/500 93.0 40.1 148.6 90% HP PA/9000-750 111.1 17.4 140.2 92% IBM RS/6000-550 117.6 11.2 146.4 88% SGI Challenge L/100 140.8 17.1 178.0 89% DEC AXP/3000-300 129.8 39.6 184.0 92% Sun SS-20/HS21 82.5 16.2 202.7 49% SGI R4000 Indigo 153.2 17.7 206.0 83% Sun SPARCserver 1000 175.1 50.8 445.6 51% Sun SPARCstation 10/41 201.4 21.0 260.4 85% Sun SPARCstation 5/85 240.5 25.8 299.0 89% Sun SPARCstation 10/30 251.4 31.7 306.3 92% ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ CONVEX C-3860 124.3 3.5 210.9 61% Cray Y-MP/8128 56.8 6.0 188.4 33% Cray YMP-C98/4256 37.9 2.0 41.2 97% ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ * see footnotes to Table 9 Table 9. The Chemistry Benchmark: Performance relative to the DEC Alpha 600/5/333 (see text) Machine SPECfp SPECint Matrix CC GAMESS-UK Kernels CPU Wall ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ DEC Alpha 600/5/333 100 100 100 100 100 100 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ HP PA/9000-K460 124 113 124 76 SGI R10000/195 94 130 104 116 93 DEC Alpha 8400/5/300 94 81 85 85 83 37 * DEC Alpha 600/5/266 89 86 71 76 84 55 Sun Ultra-2/200 84 83 87 86 90 90 IBM RS/6000-3CT 77 37 72 73 70 59 Sun Ultra-1/170 69 60 73 70 68 61 DEC Alpha 2100/5/250 64 65 74 71 81 79 Sun Ultra-1/140 60 50 61 63 61 25 + SGI Power Onyx $ 57 26 75 53 58 62 Pentium Pro/200 51 88 64 48 SGI Indigo^2 R8000 $ 49 27 75 48 54 57 DEC Alpha 250/4/266 48 56 53 53 56 40 DEC 3000/700 AXP $ 43 40 45 38 48 32 SGI Indy R5000 36 39 44 37 34 HP 9000/735-125 35 43 54 40 52 37 HP 9000/755 $ 31 26 43 36 42 35 DEC 3000/600 AXP $ 30 28 33 29 36 17 Sun SS-20/HS21 $ 28 32 34 35 31 17 DEC 3000/500 AXP 28 23 28 19 23 23 HP 9000/715-100 26 31 38 31 32 27 HP 9000/715-80 $ 23 23 31 27 28 24 IBM RS/6000-370 $ 22 17 30 37 36 31 DEC 3000/300 AXP $ 17 16 25 20 18 19 SGI Challenge L/150 $ 16 22 22 26 30 31 SGI Indigo^2 R4400 $ 16 22 23 26 30 28 SPARC SS-1000 $ 15 14 14 14 8 HP 9000/750 $ 14 12 24 21 24 24 IBM RS/6000-550 $ 13 9 24 31 24 23 SGI Challenge L/100 $ 12 15 16 17 20 19 SGI Indigo R4000 $ 11 14 13 17 18 17 Sun SS-10/41 10 12 11 9 14 13 Sun SS-5/85 $ 10 16 10 11 12 11 Sun SS-10/30 $ 10 11 8 9 11 11 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ $ - SPECfp and SPECint rations based on SPEC92 values. * - multi-user environment on DEC Alpha 8400/5/300. + - small memory configuration (32 MByte) on Sun Ultra-1/140. Table 10. APPENDIX I: Machine Configurations under Evaluation ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Machine Configuration Location ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Workstations ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Solbourne S4000 DL (loan) Sun 4/370 DL Sun SPARCstation 2/GS SPARC/40 Mhz DL (loan) Sun SPARCstation 5/85 MicroSPARC II/85 MHz DL (loan) Sun SPARCstation 10/30 SuperSPARC/36 MHz DL (loan) Sun SPARCstation 10/41 SuperSPARC/40 MHz PNL Sun SPARCserver 1000 SuperSPARC/50 MHz DL (loan) Sun SPARCstation 20/HS21 HyperSPARC/125 MHz DL(loan) Sun Ultra-1 Model 140 UltraSPARC-1/143 MHz DL (loan) Sun Ultra-1 Model 170 UltraSPARC-1/167 MHz DL Sun Ultra-2 Model 200 UltraSPARC-2/200 MHz DL (loan) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ HP/Apollo DN10020 PRISM DL HP PA/9000-720 PA7000/50 MHz DL HP PA/9000-730 PA7000/66 MHz MCC HP PA/9000-750 PA7000/66 MHz DL HP PA/9000-715/100 PA7100LC/100 MHz DL (loan) HP PA/9000-715/80 PA7100LC/80 MHz DL (loan) HP PA/9000-755 PA7100/99 MHz DL HP PA/9000-735/125 PA7150/125 MHz PNL HP PA/9000-J200 PA7200/100 MHz DL (loan) HP PA/9000-K460 PA8000/160 MHz DL (loan) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ DEC S5000/120 R3000A/R3010A 20 Mhz DL (loan) DEC S5000/200 R3000A/R3010A 25 MHz DL (loan) DEC AXP/3000-300 AXP A21064/150 MHz PNL DEC AXP/3000-500 AXP A21064/150 MHz DL (loan) DEC AXP/3000-600 AXP A21064/175 MHz PNL DEC AXP/3000-700 AXP A21064A/225 MHz DL (loan) DEC Alpha 250/4/266 AXP A21064A/266 MHz DL (loan) DEC Alpha 600/5/266 AXP A21164/266 MHz DL (loan) DEC Alpha 2100/5/250 AXP A21164/250 MHz DL (loan) DEC Alpha 8400/5/300 AXP A21164/300 MHz RAL DEC Alpha 600/5/333 AXP A21164/333 MHz DL (loan) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ SGI 4D/220 GTX DL SGI 4D/320 R3000A/R3010A 33 MHz DL (loan) SGI 4D/420 DL (loan) SGI IRIS Crimson R4000/R4010 100 Mhz DL (loan) SGI R3000 Indigo R3000A/R3010A 33 MHz DL (loan) SGI R4000 Indigo R4000/R4010 100 MHz DL (loan) SGI Challenge L/100 R4400/R4010 100 MHz Utrecht SGI R4400 Indigo^2 R4400/R4010 150 MHz DL (loan) SGI Challenge L/150 R4400/R4010 150 MHz Southampton SGI R8000 Indigo^2 R8000/R8010 75 MHz DL (loan) SGI R8000 Power Onyx R8000/R8010 75 MHz DL (loan) SGI Indy R5000 R5000/R5000 180 Mhz DL (loan) SGI PowerOnyx 10000 R10000/R10010 195 MHz DL (loan) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Stardent 1520 DL Stardent 3020 DL (loan) Stardent VISTRA-800 DL (loan) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ IBM Power1 RS/6000-320 DL (loan) IBM Power1 RS/6000-530 DL (loan) IBM Power1 RS/6000-530H RS6000/33 MHz DL IBM Power1 RS/6000-540 Perugia IBM PowerPC-25T MPC601 66 MHz DL IBM PowerPC-43P MPC604 100 MHz DL IBM Power1 RS/6000-340 RS6000/33 MHz DL (loan) IBM Power1 RS/6000-350 RS6000/41.6 MHz DL (loan) IBM Power1 RS/6000-550 RS6000/41.6 MHz Perugia IBM Power1 RS/6000-360 RS6000/50 MHz DL (loan) IBM Power1 RS/6000-370 RS6000/62.5 MHz DL IBM Power2 RS/6000-590 RS6000/66 MHz IBM IBM Power2 RS/6000-3BT RS6000/67 MHz DL (loan) IBM Power2 RS/6000-3CT RS6000/72 MHz DL (loan) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ PCs ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Netpower PC Pentium Pro/200Mhz DL (loan) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ SuperMinis ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Alliant FX2808 DL FPS-M64/60 DL Convex C-220 DL ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ SuperComputers ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Convex C-3860 ULCC IBM 3090-600-E VF RAL Cray YMP J90/10 EPCC Cray Y-MP/8128 SARA Cray YMP C98/4256 SARA ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Novel architecture Node ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Meiko Computing Surface T800-20 DL iPSC/2 SX-node DL iPSC/2 VX-node DL iPSC/860 RX-i860 node (40 MHz) DL KSR-2 KSR-2 node (80 MHz) PNL Cray T3D AXP node (150 MHz) Edinburgh IBM SP2 TN2 node (67 MHz) DL ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ========= ========= ========= ======== ****** ****** _______________________________________________________________________ Carles Colominas Dept. of Org. Chem. E-mail ccolo@mompou.iqs.url.es Institut Quimic de Sarria. TEL: (34-3)-203.89.00 Universitat Ramon Llull. FAX: (34-3)-205.62.66 Via Augusta 390. 08017-Barcelona. CATALONIA. _______________________________________________________________________ From ccolo@mompou.iqs.url.es Tue Oct 22 06:16:54 1996 Received: from fletxa.iqs.url.es for ccolo@mompou.iqs.url.es by www.ccl.net (8.8.0/950822.1) id FAA05004; Tue, 22 Oct 1996 05:36:52 -0400 (EDT) Received: from mompou.iqs.url.es by fletxa.iqs.url.es (AIX 4.1/UCB 5.64/4.03) id AA37494; Tue, 22 Oct 1996 11:39:22 +0200 Date: Tue, 22 Oct 1996 11:26:49 +0100 (GDT) From: Carles Colominas To: chemistry@www.ccl.net Subject: Summary:Benchmarks Message-Id: Mime-Version: 1.0 Content-Type: TEXT/PLAIN; charset=US-ASCII Dear Netters, Some days ago I posted a message about new HP and SGI workstations perfomance. My original question and answers are shown below. The last part of this message is a review by Martyn F. Guest at CCLRC Daresbury Laboratory about 'Performance of Various Computers in Computational Chemistry' that includes lots of useful data. Thanks to all who answered: Steen Hammerum Bill DeSimone Jose Luis Garcia de Paz Oakley H. Crawford Marc C. Nicklaus Vikram Varma Alexander Hofmann Martyn F. Guest ========= ========= ========= ======== ****** ****** From: Steen Hammerum I recently asked the very same question, and here is the summary I posted: ------------------------------------------------------------------------ Summary: Speed of new HP and SGI workstations I recently asked this group for advice with regard to the performance of new HP and SGI workstations running the Gaussian programs ------------------------------------------------------------------------ Does anyone have or know of benchmarks for Gaussian 94 or related programs running on the new SGI and HP chips (R10000 and PA8000), or other information to assist us before we decide on new machinery? We are currently considering SGI Power Indigo 2 and HP C160 workstations that will be used predominantly for Gaussian calculations, but we are not sure how well the SPECint95 and SPECfp95 numbers allow us to assess the relative performance. ------------------------------------------------------------------------- --- Roberto Gomperts (roberto@boston.sgi.com) provided the following comparison of the SGI R8000 and R10000 chips when running test178 from the Gaussian 94 test suite (a single point, direct scf calculation with 300 basis functions), using Gaussian 94, rev. D3. Machine Chip/Frequency Sec. Cache Time(min.) Power Indigo2 r8k/75 MHz 2 MB 8.04 Power Challenge r8k/90 MHz 4 MB 6.50 Power Indigo2 r10k/195 MHz 1 MB 7.30 Power Challenge r10k/195 MHz 1 MB 7.08 Power Challenge r10k/195 MHz 2 MB 5.95 The Power Challenge runs are done on 1 processor. These are all CPU times. The Wall clock times are very similar to these (less than 20 sec. difference) --- John Brodholt (j.brodholt@ucl.ac.uk) pointed me to http://gserv1.dl.ac.uk/TCSC/disco/TechPapers/bench/bench.html This site provides a detailed, critical and very useful comparison of the performance of a wide variety of newer workstations; unfortunately, no results obtained with HP PA8000 machines are included (html version not yet available, but a postscript version can be downloaded). The comparison is based on timing data obtained with GAMESS-UK (rather than Gaussian). The results illustrate that the relative performance varies quite a bit with the type of calculation undertaken. --- Eric Billings (billings@helix.nih.gov) pointed me to http://www.ki.si/parallel/summary.html The information was designed to compare parallel architectures, but the single CPU column provides useful information. --- Finally, Glenn McEnroe (gmcenroe@crl.com) suggested that I looked elsewhere: "Regarding your question about SGI vs HP you should check out the latest issue of Journal of Computational Chem V17 No. 11 1385-86 entitled Viability of Molecular Modeling with Pentium based PCs. This article does not compare these new chips for SGI and HP but it appears that you may be better off running your application on a pentium based machine if Gaussian 94 is available for this platform." --- Many thanks to everyone who answered. ---------------------------------------------------------------- Since writing the summary, I have had the opportunity to perform trisl calculations on both new SGI and new HP machines. The results with R10k SGI machines confirm Roberto Gomperts' results (see summary), that is, the new chip is very fast but not much faster than the old chip, and cache size matters a lot. My HP results have been somewhat disappointing, insofar as the new chip in real life situations (if G94 calculations can be called "real life") does not seem to be quite as fast as the benchmarks would lead you to expect. Hope this helps, Steen -- Steen Hammerum steen@kiku.dk Department of Chemistry (+45) 35 32 02 08 University of Copenhagen, Denmark fax: (+45) 35 32 02 12 ========= ========= ========= ======== ****** ****** From: desimone@mroa.ENET.dec.com Carles, The AlphaStation 500 with the 500 MHz chip should perform at least as well if not better than HP and SGI on Gaussian and AMBER. A customer in Isreal just bought an AlphaStation 500 to run Gaussian. I can see if this customer would talk to you if you are interested. Unfortunately all my Gaussian benchmarks are on AlphaServers , which BTW, beat the competition on Gaussian benchmarks. Also, the UCSF data on AMBER is on very old AlphaStations. Bill DeSimone Science & Research Applications High Performance Computing DEC ========= ========= ========= ======== ****** ****** From: DEPAZ@ccuam3.sdi.uam.es Carles, En la Univ Autonoma de Madrid se acaba de comprar un DEC de 8 cpu, en competencia con un SGI de ocho r10000. Hicimos pruebas con el gaussian (en eso estuve yo) y EN TODAS nos salio un 20% mas repido el chip dec que el chip silicon. Hubo test del gaussian usando una sola cpu, usando varias (paralelo), etc. No hubo color. La version gaussian para dec iba mejor que la gaussian que habian modificado para silicon. Yo estuve en la comision tecnica como quimico cuantico y no tuve dudas. Un saludo Jose Luis Garcia de Paz quimica fisica aplicada 3974263-4957 ========= ========= ========= ======== ****** ****** From:crawfordoh@ornl.gov If you have access to the www, look at http://www.netlib.org/performance/html/PDStop.html for performance data on a vast array of machines. Otherwise, send the following message to netlib@ornl.gov, to receive performance data in postscript files: send performance.ps from benchmark send mp-computers.ps from benchmark Finally, if you want more info about netlib's collection, send the following message: send index Good luck, Oakley Crawford ---------------------------------------------------------------------------- Oakley H. Crawford Phone: +1-423-574-5048 Oak Ridge National Laboratory Fax: +1-423-574-6210 P. O. Box 2008, MS 6123 E-mail: crawfordoh@ornl.gov Oak Ridge, Tennessee 37831-6123 Express delivery, add: Bethel Valley Road USA ---------------------------------------------------------------------------- ========= ========= ========= ======== ****** ****** From: "M. Nicklaus" Dear Dr. Colominas, We have a four-processor Digital AlphaServer 4/275 which we use mostly to run Gaussian 94 and the Molecular Mechanics program CHARMM. We are very happy with it. We have not done benchmark comparisons with an SG R10000 system ourselves (since we don't have one), but I remember having seen quite a few postings of, or at least pointers to, benchmarks with ab initio and MM programs that included SGI, HP, and/or DEC systems, and which should be retrievable from the CCL archives. Hope this helps. Regards, Marc C. Nicklaus ------------------------------------------------------------------------ Marc C. Nicklaus Lab. of Medicinal Chemistry e-mail: mn1@helix.nih.gov National Cancer Institute, NIH Phone: (301) 402-3111 Bldg 37, Rm 5B29 Fax: (301) 496-5839 BETHESDA, MD 20892-4255 USA WWW: http://www.nci.nih.gov/intra/lmch/MCNBIO.HTM ------------------------------------------------------------------------ ========= ========= ========= ======== ****** ****** From: Vikram Varma Also consider the Pentium Pros - in parallel, you can get a lot of bang for your buck!! >>>>> Vikram Varma National Research Council of Canada Phone: 613 993 5150 Institute for Biological Sciences FAX: 613 952 9092 Room 3071 100 Sussex Drive, e-mail: varma@nrcbs8.bio.nrc.ca Ottawa, Ontario K1A 0R6 www: http://nrcbsa.bio.nrc.ca/~varma <<<<< ========= ========= ========= ======== ****** ****** From: Alexander Hofmann Some Gaussian-benchmarks http://www.chem.joensuu.fi/people/juha_muilu/Misc/benchmarks.html regards alex ========= ========= ========= ======== ****** ****** From: "M.F.Guest" Dear Carles, In response to your mailing, I thought you might find the attached of value. This is a plain text version of an assessment of a wide variety of machines in computational chemistry, including the R10000 and a prototype version of the new HP machine. While it doesnt specifically include GAUSSIAN and AMBER, I hope that you will find it of use. Let me know if I can be of any further assistance. Best regards Martyn ****************************************************************** * Martyn F. Guest * * Head, Advanced Research Computing email: m.f.guest@dl.ac.uk * * CCLRC Daresbury Laboratory FAX : +44 (0)1925 603634 * * Warrington voice: +44 (0)1925 603247 * * Cheshire WA4 4AD * * England, UK * ****************************************************************** ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Performance of Various Computers in Computational Chemistry Martyn F. Guest September 1996 Abstract This report compares the performance of a number of different computer systems using a variety of software from the discipline of computational chemistry. The software includes matrix operations, a variety of chemistry kernels from quantum chemistry and molecular dynamics, and a set of twelve quantum chemistry calculations using the GAMESS-UK electronic structure program. The comparison involves approximately fifty computers, ranging from a Cray YMP-C98 to scientific workstations from IBM, Sun, Hewlett Packard, Digital and Silicon Graphics, and Pentium Pro-based PCs. 1. Introduction This report presents a performance evaluation through benchmarking of a number of different computer systems specifically in the area of computational chemistry. This work has been ongoing since 1988, when the intention was to include representative hardware typifying supercomputers, superminis, workstations and the emerging class of parallel, novel architecture machines. Throughout the 1980's the cost effective debate in scientific computing centred on the relative merits of conventional vector supercomputers [1] and so-called superminis, machines costing some 10% of supercomputers, and exhibiting some 10% of supercomputer performance [2]. Over the past 7 years the supermini category of computational resource has to a large extent disappeared from both the vocabulary and offerings of most hardware vendors, to be replaced by high-end workstation servers. We have retained those machines that originally belonged in the supermini bracket, the Convex 220, the FPS M64/60 [2] and Alliant FX2808, primarily to provide a historical perspective at the evolution of workstation capabilities, and how machines that typically fell in the $500K price range are now outperformed by machines costing some 10% of this figure. Supercomputers used in this report include the Cray X-MP, Y-MP, YMP/J90 and YMP/C98, and the Convex C3860. A large number of workstations have been benchmarked, including those from - IBM, with the Power and Power2 Risc system RS6000-based models, - Hewlett Packard with the HP/Apollo DN10020 and the PA-RISC based HP model 9000 series, including the more recent PA8000 CPU. Note that the latter was housed in a single-processor HP PA/9000-K460, with a 160 Mhz PA8000 (and not 180 MHz). - DEC, with the DEC Station 500, the AXP EV4-based 3000 series. and the EV5-based 600, 800 and 2100 alpha series, - Silicon Graphics, with the R3000-, R4000-, and R8000-based machines including the Challenge, Power Challenge, Indy, Indigo and Indigo2, Crimson, and 4D series, plus machines with the more recent R10000 and R5000 CPUs. - Stardent (1520, 3020 and VISTRA 800), and - Sun 4/370, SPARCstation-2, SPARC-5 and SPARC-10 and the more recent Hyper- and Ultra-SPARC processors (Ultra-1/140 and 170, and the Ultra-2/200). While workstations have, for the majority of scientific applications, demonstrated their cost-effectiveness against vector supercomputers, recent developments with PCs suggest that the position of the workstation as the desktop system of choice is now under threat. We thus include for the first time one of the most recent offerings from the PC marketplace, the 200 MHz Pentium Pro. Parallel, or ``novel architecture'' machines include the iPSC/860 from Intel, both transputer and i860-based Meiko Computing Surfaces, the KSR-2 from Kendall Square Research, the Cray T3D and IBM SP2. Machines featuring in this exercise, together with associated configurations, are given in the Appendix. We should stress from the outset that our access to much of the hardware evaluated herein has been at best short lived, and has often involved the temporary loan or donation of machines as part of one of the hardware evaluation exercises run at the Daresbury Laboratory. In many cases these machines were not optimally configured in terms of either memory, or high speed disk, and consideration of the results presented here should be viewed in that light. Following an introductory evaluation of hardware based on the Whetstone Benchmark, we present in Sections 2, 3, and 4 results using a variety of chemistry-oriented software. This 64-bit floating point precision FORTRAN-based code may be classified into three distinct categories, each designed to provide a pointer to the relative hardware capabilities in the discipline of computational chemistry; 1. The first category (the MATRIX Benchmark, section 2) reflects the dependence of many of the algorithms in the area of electronic structure calculations on matrix operations, and includes both matrix multiplication and matrix diagonalization; 2. The second category (the Computational Chemistry Kernels, section 3) includes four chemistry `kernels', each comprising less than a 1000 lines of FORTRAN code, and intended to be representative of the typical calculations undertaken in the area of computational chemistry. Described in more detail in section 3, these kernels include direct-SCF, molecular dynamics (MD), quantum monte carlo (QMC), and a Jacobi eigen solver (JACOBI); 3. Finally, we include complete quantum chemistry applications (section 4), using the GAMESS-UK electronic structure program [3]. Twelve typical applications are included, featuring both conventional Hartree Fock self-consistent field (SCF) and direct SCF, complete active Space SCF (CASSCF) and multiconfiguration SCF (MCSCF), configuration interaction calculations, both direct-CI and conventional table-driven MRD-CI, Moller Plesset perturbation theory (MP2), and both SCF and MP2 analytic 2nd derivatives. 2. The MATRIX Benchmark 2.1 Whetstone Benchmark A comparison of the single processor Whetstone performance on a variety of machines, including vector supercomputers, minisupers, superworkstations and workstations, together with that obtained on a single node of various novel architecture machines is given in Table 1. Data provided includes the Mflop performance on a variety of floating point vector loops (VL=1024), together with the total cpu time to execute the benchmark, and the MWips performance. The primary aim of this benchmark is to provide a performance measure of both floating point (FP) and integer arithmetic; thus while trends in the VL Mflop ratings are of interest, only a small part of the total CPU time is actually involved in these operations. The wide variety of standard functions exercised (abs, sqrt, exp, alog, sin, cos, atan etc.) consume a far larger fraction of the reported times; note the latter provide a close mapping onto the measured rate of instruction processing (MWips, million whetstones instructions per second). This benchmark was originally designed to monitor the performance of vector supercomputers, and an examination of Table 1 reveals that both the Cray Y-MP and YMP-C90 continue to outperform all other machines. As expected, the Cray YMP-J90 is less impressive, some four times slower than the C90, and slower than five of the leading workstation CPUs. The fastest CPU is seen to be the 195 MHz R10000 processor from SGI, some 1.5 times faster than the 333 MHz EV5 of the alpha 600/5. Both CPU time and MWips rate suggest that the latter processor is marginally faster than the recently released PA8000 processor of the HP PA/9000-K460, and 1.5 times the speed of the 200 MHz Ultra-2/200 from Sun. The performance of the K460 is far below that expected based on the published SPEC-fp95 ratings, and it is clear that the compiler, libraries etc on the machine under test (a single processor 160 Mhz prototype of the PA8000) were not optimal, with the results of this and subsequent benchmarks not reflecting the true potential of the PA8000. SGI's other new processor, the R5000 from mips, is also seen to perform well, and while about 1.8 times slower than the R10000, is faster than the Ultra-2 and alpha 266 Mhz EV5. In contrast the R8000-based machines from SGI appear to under-perform, 4 times slower than the R10000, three times slower than the AXP-600/5/333, and slower than both the HP PA/9000-735/125 and IBM Power 2 RS/6000-3CT. The R8000 is seen to outperform its predecessor, the R4400, by just a factor of 1.7, based on the CPU time for the complete benchmark. Sun's latest processor, the 200 MHz Ultra-2/200 is found to be more than twice the speed of the 125 Mhz HyperSPARC, and 8.5 times the 40 MHz SuperSPARC processor in the Sun SPARC 10/41. Considering the MPP single node performance of Table 1, the KSR-2 custom processor is seen to be comparable with mid-range workstation CPUs (IBM RS/6000-360 and DEC AXP/3000-500), while the quality of the FORTRAN compiler on the Cray T3D is at least in part responsible for the Whetstone timing of 121 seconds, 1.8 times slower than the DEC AXP/3000-500 (68.7 secs) which does, of course, house the same CPU. The IBM-SP2 TN2 processor is by far the dominant CPU, twice the speed of the KSR-2 and 3.5 times that of the Cray T3D. Finally, we note that the performance of the Pentium Pro is broadly in line with expectations. It outperforms the R8000-based SGI and Power2-based IBM workstations, while slower than the leading CPUs from SGI, Digital, HP and Sun, by factors of 3.8, 2.5, 2.3 and 1.7 respectively. It is important to realise that this level of performance is only achieved using "commercial" Fortran compilers (in this case from Intel). Using public domain f2c and a variety of c-compilers produced far inferior figures, some 3-4 times slower than those of Table 1. 2.2 Sparse Matrix Multiply Benchmark The matrix multiply operation (MMO) is central to the efficient operation of modern QC codes on vector processors [1], it being possible both to extract near peak performance for this kernel and to formulate many QC steps around this operation. A comparison of the single processor sparse MMO performance on a variety of machines, is given in Table 2. In this benchmark a series of MMOs (R = A X B) involving matrices of order 10, 20, 30, ... 150 were performed. Each MMO was conducted a number of times, this number being inversely proportional to the order of the matrices, so that the summed CPU times of Table 2. refer to 150 MMOs of order 10 matrices, 140 of order 20 matrices, and so on up to 10 MMOs for matrices of order 150. Figures are presented for both `full' (0% sparse) and 50%-sparse B matrices, with the performance figures referring to code written entirely in Fortran. The potential of the PA8000 processor is clearly seen in the figures of Table 2, with the HP/PA9000-K460 recording the same time as the Cray Y-MP/C98, and marginally faster than the R10000/195 processor. Both processors outperform the Cray Y-MP/8128, with the HP/PA9000-K460 twice as fast as the R8000-based SGI machines and Power2 RS/6000-3CT. The latter machines exhibit identical performance on this benchmark (with 0% sparsity timings of 1.8 seconds). Both CPUs are marginally faster than the EV5 600/5/333 and 84000/5/300, 1.3 times faster than the Ultra-2/200 and 1.4 times faster than the HP PA/9000-735/125. Both exhibit comparable timings to that on the Y-MP/8128 (1.5 seconds), achieve some 50% of the YMP-C98 rating, and outperform the Cray YMP-J90 by a factor of 1.4. The performance of the R5000 processor is perhaps disappointing, four times slower than the R10000. The R1000-based Power Onyx is seen to outperform the corresponding R8000 machine by a factor of 1.6, which in turn outperforms the R4400 Challenge L by a factor of 3.9, and the earlier 50 Mhz R4000 Challenge by a factor of 5.6. Much of the speed of the R8000 may be attributed to the KAP pre-processor, which enhances FORTRAN performance by a factor of 1.8. The performance of the Pentium Pro/200 is again seen to be impressive, recording exactly the same benchmark time as the DEC Alpha 600/5-266 and Sun Ultra-2/200. Of historical note is the timing of 448.6 seconds recorded on the T800 20MHz Transputer, some 500 times slower than the PA8000 CPU and the Cray YMP-C98. Considering the MPP nodes, the IBM-SP2 TN2 node outperforms the Cray AXP node of the T3D by a factor of 4.7, and the KSR-2 by a factor of 6.6. Note that the T3D node remains 1.5 times slower than the DEC AXP/3000-500. One additional feature of the MMO benchmark not apparent in Table 2 is the significantly enhanced performance found on all machines when comparing assembly language MMO to Fortran MMO. Historically this has often involved the user having to code key routines in assembly language, for few vendors initially provided optimized mathematical libraries; improvement factors of 3.2 (Cray X-MP), 4.2 (IBM 3090/VF), 3.1 (Convex C-220) and 3.4 (FPS-M64/60) have previously been reported with assembly language implementations of the sparse MMO routine, MXMB. Optimized BLAS libraries are now fairly commonplace on the majority of workstation platforms, the most notable exception, until recently, being the SPARC offerings from Sun (note that the Cray-optimised libraries are now available on both Hyper- and UltraSPARC machines). The impact of optimized library routines continues to be evident on current workstations, with the 0% sparsity timings of Table 2 improving by factors of 1.8, 1.9, 2.3, 2.5, 2.7 and 3.1 for the SGI R10000/195, IBM RS/6000-3CT, SGI R8000 Indigo2, HP PA/9000-735/125, DEC Alpha 600/5/333 and Alpha 8400/5-300 respectively when using the BLAS dgemm routine. A corresponding factor of 3.8 is found when using the Kuck Library on the i860, and perhaps somewhat surprisingly, a factor of 4.5 when using SCILIB on the Cray YMP-C98. The availability and degree of functionality of library software are, we believe, important issues when considering cost-effective performance. This effect is evident from the second benchmark which, given in Table 3, involves performing a series of similarity transforms (Q*HQ) using both a scalar and vector algorithm. The scalar code collapses the matrix transposition and multiplications to yield an algorithm with fewer FLOPS than the vector code, which adopts a brute force approach by explicitly performing the transposition and two matrix multiplications. In the latter case we utilize the BLAS library routine DGEMM (where available) for performing the requisite MMOs, in the former case the dot product BLAS routine, DDOT. Considering the scalar algorithm, the SGI R10000 and HP PA/9000-K460 exhibit the optimum performance, with the R10000 1.1 times the speed of the Sun Ultra-2/200 and 1.2 times that of the IBM RS/6000-3CT. The Power2 CPU is marginally faster than the DEC 600/5/333, and 1.2 times faster than the R8000-based SGI machines and the Sun Ultra-1/170. With a couple of notable exceptions. this ordering is similar to that found for the vector case, where the R10000 just outperforms the Sun Ultra-2/200 and the DEC 600/5/333, which are in turn superior to both the IBM RS/6000-590 and R8000-based SGI machines. The performance of the R10000 in the scalar algorithm is first class, faster than the Cray Y-MP/J90, Cray Y-MP/8128 and Cray YMP C98/4256 by factors of 4.3, 2.3 and 1.2 respectively. The Pentium Pro/200 again fares well, recording comparable scalar timings to the DEC Alpha 600/5-266, Sun Ultra-1/140, and DEC Alpha 2100/5-250, although 1.7 times slower than the R10000. The notably poor performance on the vector algorithm for the HP PA/9000-K460, SGI Indy 5000 and Pentium Pro/200 may be attributed to either use of poorly tuned maths libraries (as on the HP and SGI machine), or to reliance on straight Fortran code (as on the Pentium). A similar effect was seen on the RS/6000-3CT, where the relatively unimpressive timings from the vector algorithm were caused by the un-tuned library DGEMM routine available through -lblas; the corresponding ESSL routine (-lessl) improves performance by a factor of 1.52 on the IBM SP2. The Cray YMP-C98 at last justifies its supercomputer tag, outperforming the SGI R10000, R8000 and RS/6000-3CT by factors of 2.6, 3.2 and 3.6 respectively; the same cannot be said for the Cray Y-MP/J90, which remains a factor of 1.7 slower than the R10000. The latter outperforms the R5000 by factors of 3.4 (scalar) and 5.3 (vector). As noted above all of the more recent CPUs (with the exception of the PA/9000-K460 and Pentium Pro) exhibit superior performance on the vector algorithm, with average factors of 2.1 (DEC Alpha 600/5-333), 2.0 (SUN Ultra-1/170), 1.8 (DEC Alpha 8400/300 and HP PA/9000-735), 1.7 (SGI R10000, SUN Ultra-2/200 and SGI R8000), and 1.6 (IBM RS/6000, with the exception of the 590). Much higher factors are found with the vector CPUs; the figure of 4.2 on the Cray J90 leads to the Cray being competitive on the vector algorithm, but slower than the leading 24 workstations on the scalar code. Note again the inadequacies of the FORTRAN compiler on the Cray T3D; the unexceptional scalar algorithm timing of 79.3 secs. (to be compared with that of 45.1 secs. on the DEC AXP/3000-500), improves significantly with the vector algorithm, where use of the BLAS routines produces timings of 27.5, to be compared with 23.9 secs on the AXP/3000-500. This effect is also seen in comparison with the KSR-2 and IBM SP2 timings; the T3D is slower by a factor of 1.4 on the scalar algorithm, and faster by a factor of 1.3 in the vector case compared to the KSR-2, while the differential with the SP2 is reduced from 5.8 in the scalar case to 3.3 for the vector algorithm. 2.3 Diagonalization Benchmark Table 4 presents the results of a matrix diagonalization benchmark intended to supplement the previous analysis conducted by Dunning and co-workers [4]. We consider a similar benchmark, based on diagonalizing a series of real symmetric matrices, with rank 10, 20, 30, ... 100, using 64-bit floating point arithmetic. Again the CPU time was measured for the diagonalization of each size matrix, with the summed times used as the benchmark execution time. Results are presented for a range of compiler options available on the depicted hardware. While the previous analysis was restricted to the EISPACK RS routine, we consider below the performance of eight diagonalization routines available in various mathematical libraries and quantum chemistry codes: (i) EIGRS, an unoptimized FORTRAN version of the library routine RS (available in SCILIB on the Cray) from the IMSL library of routines [5]. (ii) F02ABF from the NAG library [6]. (iii) HQRII [7], as implemented in the semiempirical MOPAC program [8]. (iv) GIVENS, adapted from the QCPE program exchange (number 62.1). (v) SDIAG2, as implemented in the MUNICH system of programs. (vi) JACOBI, from the ATMOL system of programs [9]. (vii) JACO, the diagonalization routine from the direct-SCF program DISCO [10]. (viii) ERDUW, as taken from the Berkeley System of Quantum Chemistry codes. The first four routines are all based on the Householder QR method, whilst the last four use the Jacobi method. Note that the only optimization performed involved inserting calls to the BLAS for two-dimensional rotations (DROT) and vector interchange (DSWAP). The timings of Table 4 suggest that the SGI R10000/195 is again the fastest CPU, just ahead of the HP PA/9000-K460 and DEC Alpha 600/5-333, and 1.5 times faster than the Sun Ultra-2/200 and DEC Alpha 8400/5-300. The R8000-based machines, HP/9000-735/125 and IBM RS/6000-3CT are seen to exhibit comparable timings (8.0-8.1 secs.), a factor of 2.5 times slower than the R10000. This benchmark tends to be dominated by the slowest of the diagonalization routines in use, JACO, which typically accounts for > 40% of the total CPU time. Significantly the leading twenty workstations are seen to outperform the Cray YMP/C98, while the Cray Y-MP/J90 is bettered by the leading 44 workstation CPUs. The Pentium Pro is seen to be five times faster than the J90, and is only outperformed by the leading six workstation CPUs. The performance of the Cray T3D node is comparable to the IBM RS/6000-350, and again significantly slower than the IBM-SP2 TN2 node (21.7 secs. vs. 9.3 secs.) 2.4 Relative Performance on Matrix Operations To summarize the performance of the various workstations on the matrix multiply and diagonalization benchmarks detailed above, we show in Table 5 the performance of each relative to the SGI R10000/195. It is clear from these figures that the SGI R10000 and PA8000 processor (in the HP PA/9000-K460) lie ahead of the competition, with the R10000 1.3 times faster than the DEC Alpha 600/5-333 and 1.4 times faster than the Sun Ultra-2/200. While the PA8000 is marginally slower than the R10000, we belief that the arrival of tuned maths libraries on the former will reverse this order to provide a picture more consistent with the SPECfp95 ratings. We see that the relative ordering of processors within a given family are broadly in line with clock speeds. Considering the EV5-based CPUs, we find the DEC Alpha 600/5-333, 8400/5-300, 600/5-266 and 2100/5-250 to be slower than the R10000 by factors of 1.25, 1.49, 1.82 and 1.67 respectively. For the Sun Ultra, the Sun Ultra-2/200, Sun Ultra-1/170, and Sun Ultra-1/140 are slower than the R10000 by factors of 1.43, 1.69 and 2.05 respectively. We also note the following: - the 266 MHz EV5-based CPU outperforms the corresponding EV4 by a factor of 1.33; - the R10000 exhibits a speed up of 1.7 against its predecessor, the R8000 in the Power Onyx, and a speed up of 3.4 against the R5000 (due in the main to the poor library performance on the R5000); - the R8000 exhibits a speed up of 3.3 against its predecessor, the R4400 in both the Indigo2 and Challenge L, and, - the position of the Pentium Pro as the 14th fastest CPU of those considered will undoubtedly improve given the advent of optimised libraries (not available in this current exercise). 3. Computational Chemistry Kernels One of the crucial requirements in evaluating the increasingly broad range of hardware platforms, whether these be parallel machines (true MIMD message-passing machines, workstation clusters, shared-memory multiprocessors etc), or simply the lastest workstation, is the availability of portable benchmarking codes that are representative of the application area under consideration. In an attempt to provide such capabilities in computational chemistry, we have described previously four representative codes, each of which is less than 1000 lines of FORTRAN, and is sufficiently portable that migration to any hardware platform can typically be achieved in a matter of hours. In this report we limit our discussion to the performance of these codes on a variety of single CPUs. The benchmarking kernels comprise the following programs that are realistic models of actual chemical applications or algorithms; 1. Self Consistent Field (SCF); This Self Consistent Field (SCF) electronic structure kernel uses distributed primitive 1s gaussian functions as a basis (thus emulating use of s,p,... functions) and computes integrals to essentially full accuracy. It is a direct SCF code, with an atomic density used for a starting guess. There are two available problem sizes, corresponding to 60 basis functions Be4 and 240 basis functions (Be16). The timings of Table 6 refer to the former. 2. Molecular Dynamics (MD); This program bounces a few thousand argon atoms around in a box with periodic boundary conditions. Pairwise interactions (Leonard-Jones) are used with a simple integration of the Newtonian equations of motion. 3. Monte Carlo (MC); This code evaluates the energy of the simplest explicitly correlated electronic wavefunction for the He atom ground state using a variational monte-carlo method without importance sampling. 4. Jacobi iterative linear equation solver (JACOBI)]; Uses a naive jacobi iterative algorithm to solve a linear equation. All the time is spent in a large matrix vector product. Total CPU timings for the SCF, MD and MC benchmarks are presented in Table 6, together with the Mflop ratings from the JACOBI benchmark. These results present a somewhat confusing picture, with the processor ordering very much a function of the particular chemistry kernel under examination. With the exception of the JACOBI benchmark, the SGI R10000/195, DEC Alpha 600/5-333 and 8400/5-300, and Sun Ultra-2/200 processors are seen to be the superior CPUs, although the processor ordering in the SCF, MD and MC benchmarks varies significantly. Thus for the SCF kernel, the SGI R10000 and Alpha 600/5-333 are 1.5 times the speed of the HP PA/9000-K460, twice the speed of the Sun Ultra-2/200 and three times that of the POWER2 RS/6000. The HP/9000-735/125 exhibits good relative performance - it is 1.3-1.4 times faster than the R8000 and POWER2 RS/6000 (TN2 and 3CT). The Sun SS20/HS21 is also seen to perform well on this kernel, with the same execution time as the latter CPUs. The performance of the Pentium Pro/200 matches that of the SGI R8000 and and POWER2 RS/6000. The SGI R10000 is clearly the optimum processor in the MD benchmark, followed by the Sun Ultra-2/200 and Alpha 600/5-333. The POWER2 is seen to perform very poorly in the MD Benchmark where it is apparently almost 4.5 times as slow as the R10000, with the 3CT 3.8 times slower than the Sun Ultra-2/200. In fact the whole family of RS6000 processors remains consistently unimpressive on this benchmark. The relative processor ordering found in the MD and SCF kernels is seen to be markedly different to that suggested by the JACOBI benchmark. The Mflop ratings of Table 6 suggest that the Power2 3CT is the optimum CPU, with the 81 Mflop rating 2.1 times that achieved on the SGI R10000, 2.5 times that found on the R8000 (32.9 Mflop), and 2.6 times that recorded on the HP PA/9000-K460 (31.0 Mflop). The Sun Ultra performance on JABOBI is also impressive, with the Ultra-2/200, and Ultra-1/170 and Ultra-1/140 all surpassing the MFlop rate on the EV5 and R10000 processors. In the MC benchmark, the Alpha 600/5-333 and SGI R10000 are the fastest processors, twice the speed of the RS/6000 POWER2 and Ultra-2/200. Comparing the R10000/195 and R8000 processors, the R10000 is 3.2 times faster in the SCF kernel, 2.3 in the MC kernel, but only 1.2 times faster in JACOBI. The Indy R5000 demonstrates comparable performance to the R8000 - it is slower than the R10000 in the SCF, MD, MC, and JACOBI benchmarks by factors of 2.9, 2.1. 1,9 and 3.1 respectively. The performance of the Pentium Pro/200 is perhaps somewhat less impressive than that found in the matrix benchmarks. Its overall performance is similar to that shown by the DEC Alpha 250/4-266 and Indy R5000, approximately one half the speed of the R10000. 4. The Quantum Chemistry Benchmark The benchmark described below (and summarized in Table 7) is designed to highlight the typical range of calculations commonly performed by the ab initio quantum chemist. It includes 12 calculations carried out using the GAMESS-UK electronic structure code, and includes the following functionality; - Conventional SCF Calculations, on morphine and 2,4,6 tri-nitro-toluene (Calculations 1. and 2. respectively); - Valence-only ECP calculations, with a geometry optimization of Na7Mg+ in an ECP-DZ+D(Mg) basis of 70 GTOs (calculation 3). - A Direct-SCF calculation on cytosine (82 GTOs, 6-31G basis) Calculation 4); - Multi-configuration SCF calculations, with a CASSCF geometry optimization on H2CO (Calculation 5), and a larger CASSCF calculation, also on H2CO (Calculation 6); - Configuration interaction calculations, both Direct-CI on the H2CO/H2+CO transition state (Calculation 7), and conventional table driven-CI on TiCl4 (Calculation 8); - Moller Plesset calculations, with a MP2 geometry optimization of H3SiNCO (Calculation 9); - Analytic second derivatives, at both the SCF (pyridine) molecule in a 6-31G basis, Calculation 10) and MP2 level (C4) in a 6-31G* basis, Calculation 11); - A Direct-MP2 calculation on pyridine in a DZ + D(N) basis set (Calculation 12). 4.1 QC Benchmarks - Single Processor Results The data presented in Table 8 is collected under control of the UNIX command time where available, and includes CPU time (both user and system), total elapsed time and Efficiency, measured as CPU versus elapsed. While our original aim was to base comparisons strictly on elapsed times, such timings could not be consistently gathered over the range of machines considered. For example, the range of disk configurations varies enormously, from primitive SCSI disks to striped high-speed raid disks, and the loading on the machines varies, from effectively single-user loading on many of the workstations, to multi-user environments, such as on the Convex. Thus while reporting the elapsed times, we use such figures to identify, where appropriate, the requirement for enhanced disk configurations, rather than as any definite criticism of the machine in question. The total user CPU timings of Table 8 suggest that the DEC Alpha 600-5/333 and SGI R10000/195 are the optimum machines, with summed timings of 23.4 and 23.1 minutes for all 12 calculations. These are significantly less than those for the leading machines from Sun and IBM which exhibit summed timings of 31.5 and 40.1 for the Sun Ultra-2/200 and RS/6000-3CT respectively. A somewhat different picture emerges when considering the system CPU and Elapsed times. With the exception of the AXP and SGI R10000, all machines exhibit a system CPU time of the order of 10-15% of the user time; this percentage increases significantly, particularly on the AXP, to between 20-40% on the systems of Table 8. Considering the elapsed times and associated efficiences. the R8000 based machines from SGI appear to be the most balanced. Both the R8000 Power Onyx and Indigo2 exhibit efficiences of > 95%, to be compared with figures of 76%, 64% and 59% for the RS/6000-3CT, 9000-735/125 and Alpha 600/5-266. What is noticeable is the significant improvements in these efficiency ratios on the more recent Sun (Ultra-2/200) and Digital hardware (both the DEC Alpha 600/5/333 and DEC Alpha 2100/5/250) compared to the figures recorded in the past. Initial experience with the HP PA/9000-K460 suggested that the compiler was too unreliable to even attempt to perform the GAMESS-UK Benchmark, with numerous routines mis-compiling. The poor elapsed time on the Sun Ultra-1/140 was due in part to the limited memory on the machine (32 MByte). Overall factors of 10.6 (in total CPU) and 13.1 (in elapsed times) are found when comparing the ``slowest'' (Sun SPARCstation 10/30) and ``fastest'' (SGI R10000 Power Onyx) machines in Table 8. The corresponding CPU factors in both the Matrix and Chemistry Kernels are somewhat higher, 18.9 and 11.2 respectively. Considering machines from a given vendor, we find that the EV5-based Alpha 600/5/266 outperforms the corresponding EV4 system, the 250/4/266 by factors of 1.5 (CPU) and 1.4 (elapsed). The R10000 from SG is found to be approximately twice the speed of the R8000-based machines, and 3.2 times as fast as the Indy R5000. The R8000-based machines in turn are found to perform some 1.9 times faster than the corresponding Power Challenge L and R4400 Indigo2. Significantly higher ratios are found in some of the individual calculations of Table 7, with ratios of 2.5 - 2.8 found in the MCSCF, direct-CI, MP2 and 2nd derivative calculations. Two of the 12 benchmark calculations impact seriously on the final ratios, the ECP and MRDCI calculations exhibiting negligible speedups of 1.13 and 1.18 respectively. 5. Summary As a summary of this work, we present in Table 9 the relative performance of 34 of the leading workstations against the DEC Alpha 600/5/333 in terms of quoted SPEC (``Systems Performance Evaluation Cooperative'') benchmarks, and those from the present Matrix, Chemistry Kernels and GAMESS-UK benchmarks. Note that this analysis has been somewhat complicated by the inconsistent approach adopted by some vendors to providing a smooth transition between the older SPECfp92 and SPECint92 values, and the more recent SPECfp95 and SPECint95 benchmark. SGI in particular appear to provide only SPECfp95 values for the R10000 and R5000, and only SPECfp92 values for their older processors. In the following discussion we have used the SPEC-95 results when available (see Table 9) The SPEC benchmark suite contains non-tuned application-based code to measure processor speed for both integer (SPECint) and floating point (SPECfp) arithmetic. Based on the published SPECfp ratings, and normalising with respect to the DEC Alpha 600/5/333, we would expect the PA8000 processor of the HP PA/9000-K460 (124%) to be the fastest CPU, followed by the DEC Alpha 600/5/333 itself, the SGI R10000/195 (94%) and DEC Alpha 8400/5/300 (94%), the DEC Alpha 600/5/266 CPU (89%), the 200 MHz Sun Ultra-2/200 (84%), and the IBM Power2 RS/6000-3CT (77%) (where the %-values in parentheses indicate performance relative to the Alpha 600/5/333). Based on these relative SPECfp values given in the table, we expect a factor of 12 between the fastest and slowest processor, the Sun SPARC/10-30. A somewhat different processor ordering is revealed by the SPECint ratings, with the processors from SGI (notably the R8000) and IBM (the Power2) significantly slower than those from DEC, Sun and the HP (PA8000). It is perhaps worth noting that SGI have not as yet published the SPECint95 rating for either the R10000 or R5000. We note also that the Specint95 ratings suggest that Pentium Pro/200 is some 88% of the DEC Alpha 600/5/333, to be compared with the smaller rating of 51% based on SPECfp95. When considering the results, there are several factors we wish to consider based on the present evaluation exercise: 1. Do the SPECfp values provide a reliable metric for evaluating the capabilities of hardware in computational chemistry? If this so, we would expect to find a close mapping of the ratios for the various chemistry benchmarks onto the SPECfp ratios (note that the CPU values for the GAMESS-UK benchmark are used, since SPEC does not adequately incorporate I/O in its evaluation); 2. Does any particular CPU consistently ``underperform'' based on the SPECfp criteria? - this would manifest itself as the ratios from the chemistry benchmarks falling below the SPECfp ratios; 3. Do the ``simple'' Matrix and Chemistry Kernel benchmarks lead to the same conclusions as the GAMESS-UK benchmarks? In terms of relative speed, we find that all the chemistry benchmarks are broadly in line with the SPECfp predictions, the only notable exceptions being summarised below: - The poor performance of the HP PA/9000-K460 - while the matrix kernels are in line with the SPECfp ratio, the chemistry kernels would appear to be running at around half the expected speed. - The impressive performance of the SGI R10000 on all the benchmarks, with figures of 130%, 104% and 116% on the matrix, kernels and GAMESS-UK benchmarks, as against the value of 94% based on the SPECfp95 figures. Indeed, the R10000 would certainly appear to be the optimum CPU based on the benchmarks conducted in this report. The R5000 appears, however, to follow closely the SPECfp95 ratings. - There is some evidence from Table 9 that neither the DEC EV5-based Alpha 600/266 or the 8400/300 are performing as well as the SPEC ratings might suggest, with all benchmark ratios some 10% lower than expected based on SPECfp alone. In contrast the DEC Alpha 2100/5/250 is performing consistently above its SPEC rating. - The UltraSPARC systems from Sun appear to be performing exactly in line with the SPECfp95 ratings. - The R8000-based systems perform well on the matrix benchmarks, but are closer to the SPECfp ratings on the kernels and GAMESS-UK. - All IBM Power1 RS/6000 CPUs exhibit enhanced performance on the chemistry codes relative to the SPEC rankings e.g.. the SPECfp ratio of the RS/6000-370 to DEC 600/5 is 22%, to be compared with the Matrix, Kernels and GAMESS-UK ratios of 30%, 37% and 36% respectively. - A similar effect is shown with the HP PA/9000-735/125, where the SPECfp ratio of 35% increases to 54%, 40% and 52% for the chemistry benchmarks. - The potential of the Pentium Pro is evident, with the performance in the Matrix benchmarks exceeding the SPECfp95 figures. References [1] M.F. Guest and S. Wilson, Daresbury Laboratory Preprint, DL/SCI/P290T; Supercomputers in Chemistry, ed. P.Lykos and I.Shavitt, A.C.S. Symposium series 173 (1981) 1; V.R. Saunders and M.F. Guest, Comp.Phys.Comm., 26 (1982) 389: M.F. Guest, in ``Supercomputer Simulations in Chemistry'', Ed. M. Dupuis, Lecture Notes in Chemistry, 44, Springer Verlag (1986) 98. [2] M.F. Guest, R.J. Harrison, J.H. van Lenthe and L.C.H. van Corler, ``Computational Chemistry on the FPS-X64 Scientific Computers: Experience on single- and multi-processor systems'', Theoret. Chim. Acta 71 (1987) 117. [3] GAMESS-UK is a package of ab initio programs written by M.F. Guest, J.H. van Lenthe, J. Kendrick, K. Schoeffel and P. Sherwood, with contributions from R.D. Amos, R.J. Buenker, M. Dupuis, N.C. Handy, I.H. Hillier, P.J. Knowles, V. Bonacic-Koutecky, W. von Niessen, R.J. Harrison, A.P. Rendell, V.R. Saunders, and A.J. Stone. The package is derived from the original GAMESS code due to M. Dupuis, D. Spangler and J. Wendoloski, NRCC Software Catalog, Vol. 1, Program No. QG01 (GAMESS), 1980. [4] R. Shepard, R.A. Bair, R.A. Eades, A.F. Wagner, M.J. Davis, C.B. Harding and T.H. Dunning Jr., Int. J. Quant. Chem. , (1983) 17; R.A. Bair and T.H. Dunning Jr., J.Comp.Chem., 5 (1984) 44; R. Bair, ``FPS-164 Matrix Multiplication Subroutine Guide'', Argonne National Laboratory, (1984); T.H. Dunning Jr. and R.A. Bair, ``Advanced Theories and Computational Approaches to the Electronic Structure of Molecules'', NATO ASI Series, D. Reidel, 1984, p1. [5] IMSL Program Library, 1978. [6] NAG Fortran Library, Numerical Algorithms Group Ltd, 1984. [7] Y. Beppu, Computers and Chemistry, 6 (1982). [8] J.P. Stewart, ``MOPAC - A General Molecular Orbital Package'', QCPE 455, 1984. [9] V.R. Saunders and M.F. Guest, ``ATMOL3 Part 9'', RL-76-106, 1976; M.F. Guest and V.R. Saunders, Mol.Phys., 28 (1974) 819; D. Moncrieff and V.R. Saunders, ``ATMOL-Introduction Notes'', UMRCC, May, 1986; Cyber-205 Note Number 32, UMRCC, September, 1985. [10] J. Almlof et al, J.Comp.Chem., 3 (1982) 385. Performance of Various Computers in Computational Chemistry Table 1. Vector Whetstone Benchmark. Total CPU times (seconds), Mflop ratings (see text) and MWIP ratings ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Machine Mflop Ratings (VL=1024) Total CPU MWIPS N2 N3 N8 (seconds) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ iPSC/860 (RX) 4.6 4.4 5.3 282.7 15.0 Cray T3D (AXP) 9.8 8.4 7.3 120.5 33.5 KSR-2 32.8 31.3 29.7 68.6 59.6 IBM SP2 (TN2 node) 49.2 38.2 49.3 35.0 116.5 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Sun 4/370 2.0 1.8 1.0 642.8 6.3 SOLBOURNE S4000 1.4 1.3 0.9 629.3 6.4 Sun SPARCstation 2/GS 4.3 3.2 1.8 366.7 11.0 DEC S5000/120 4.5 4.0 2.7 351.3 11.5 IBM RS/6000-320 9.4 2.4 1.3 305.3 13.3 SGI 4D/220 5.6 4.4 2.9 289.5 14.0 Stardent 1520 6.3 5.9 6.0 283.2 14.5 DEC S5000/200 6.4 5.4 3.4 274.7 14.8 SGI R3000 Indigo 6.8 3.7 3.7 233.8 17.3 SGI 4D/320 7.6 5.9 3.6 227.0 18.0 Sun SPARC/5-85 3.3 3.1 4.3 205.5 19.7 Stardent VISTRA-800 4.7 4.4 5.3 196.5 19.0 SGI 4D/420 9.4 6.9 4.4 188.0 21.6 Sun SPARC/10-30 7.0 3.4 4.0 177.2 21.0 Sun SPARC/10-41 9.4 8.1 5.3 145.0 26.5 SGI IRIS Crimson 21.8 14.3 6.1 121.7 33.7 IBM RS/6000-540 15.1 7.7 5.7 120.3 33.8 HP PA/9000-720 11.6 11.1 9.3 119.3 34.0 IBM RS/6000-340 17.9 4.0 6.1 115.9 34.8 Sun SPARCserver 1000 12.5 10.9 6.5 111.6 33.8 SGI R4000 Indigo 21.8 13.6 6.4 105.8 38.1 IBM RS/6000-530H 16.4 10.4 6.8 102.0 39.9 RS/6000 PowerPC-250 15.1 8.4 5.4 95.0 38.7 IBM PowerPC-25T 15.1 8.4 6.4 93.2 41.1 HP PA/9000-730 14.0 14.2 11.5 93.4 43.4 SGI Challenge L/100 21.8 13.6 9.1 90.8 43.7 IBM RS/6000-550 21.8 10.8 7.9 86.8 46.6 IBM RS/6000-350 19.7 8.0 7.7 84.8 46.9 DEC AXP/3000-300 19.4 15.7 10.3 79.1 50.9 IBM RS/6000-360 24.6 9.0 9.2 70.7 56.1 DEC AXP/3000-500 20.8 16.9 10.7 68.7 58.7 DEC AXP/3000-600 26.2 19.8 12.7 64.4 62.8 HP PA/9000-750 15.1 27.5 24.3 64.1 63.6 SGI R4400 Indigo^2 32.7 19.9 13.1 62.3 63.8 SGI Challenge L/150 32.8 20.5 14.2 62.9 63.3 IBM RS/6000-370 32.8 12.0 11.5 56.5 70.2 HP PA/9000-715/100 32.8 29.9 15.8 54.5 73.3 Sun SPARCstation-20/HS21 30.0 25.5 18.2 49.8 81.6 HP PA/9000-J200 39.3 32.8 33.9 41.0 99.2 HP PA/9000-755 39.3 44.4 30.9 38.9 104.7 DEC AXP/3000-700 34.1 25.6 23.6 36.5 111.1 RS/6000 PowerPC-43P 21.9 9.8 24.2 36.4 92.2 SGI R8000 Indigo^2 27.0 26.2 109.2 36.4 112.4 SGI R8000 PowerOnyx 27.1 26.2 109.6 36.2 112.8 IBM RS/6000-590 32.8 31.3 44.2 34.8 117.3 IBM RS/6000-3CT 39.3 37.2 46.8 34.4 117.6 IBM RS/6000-3BT 39.2 37.2 47.2 34.2 118.9 Pentium Pro/200 41.8 35.2 21.7 32.3 123.7 HP PA/9000-735/125 39.3 57.3 38.9 30.8 132.0 DEC Alpha 250/4-266 36.6 34.8 72.5 26.9 150.5 Sun Ultra-1/140 19.9 19.3 170.9 26.3 155.1 Sun Ultra-2/200 29.8 27.0 239.6 18.7 218.2 DEC Alpha 2100/5-250 143.9 39.4 107.4 17.0 238.1 DEC Alpha 600/5-266 83.9 83.9 126.3 15.8 256.1 DEC Alpha 8400/5-300 94.4 97.9 133.5 14.2 286.7 HP PA/9000-K460 196.6 196.6 190.5 13.8 298.3 DEC Alpha 600/5-333 201.4 52.4 138.7 12.8 311.0 SGI PowerOnyx 10000/195 118.6 76.0 139.2 8.5 476.8 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Convex C-220 21.4 17.7 15.7 59.6 70.0 Convex C-3860 54.0 44.3 37.8 23.0 185.2 CRAY YMP/J90 115.9 117.9 145.9 16.6 260.2 CRAY Y-MP/8128 190.0 193.5 297.2 7.9 552.0 CRAY YMP-C98/4256 542.1 539.3 668.7 3.9 1114.0 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Table 2. Sparse MMO Benchmark: Total CPU times (seconds) for a series of sparse MMOs (R = A X B, see text) implemented in Fortran. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Machine Sparsity in B-Matrix 0% 50% ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ iPSC/860 RX-node 21.2 11.0 KSR-2 11.3 5.9 Cray T3D AXP-node 8.5 4.5 IBM SP2 TN2-node 1.8 1.1 Solbourne S4000 74.4 38.4 Sun 4/370 57.4 30.6 DEC S5000/120 50.7 25.9 HP/Apollo DN10020 44.2 22.8 SGI 4D/220 39.4 20.1 DEC S5000/200 33.1 17.1 SGI R3000 Indigo 31.0 14.3 SGI 4D/320 27.9 14.4 Sun SPARCstation 2/GS 27.5 14.4 Stardent 1520 26.6 17.0 SGI 4D/420 23.7 12.1 Stardent VISTRA-800 20.4 10.6 Sun SPARCstation 10/30 16.1 7.9 Sun SPARCstation 5/85 14.9 7.8 Stardent 3020 14.7 9.1 IBM RS/6000-320 14.6 7.1 SGI R4000 Indigo 12.6 6.4 SPARCstation 10/41 11.8 6.1 SGI IRIS Crimson 11.0 5.6 SGI Challenge L/100 10.0 4.9 IBM RS/6000-530 9.3 4.9 Sun SPARCserver 1000 9.3 4.7 IBM PowerPC-25T 9.2 4.3 RS/6000 PowerPC-250 9.1 4.4 IBM RS/6000-340 8.9 4.1 HP PA/9000-720 8.5 4.8 IBM RS/6000-530H 7.8 4.1 IBM RS/6000-350 7.6 3.6 SGI R4400 Indigo^2 7.1 3.4 SGI Challenge L/150 7.1 3.4 HP PA/9000-730 6.7 3.5 IBM RS/6000-360 6.4 3.1 DEC AXP/3000-300 6.1 3.3 DEC AXP/3000-500 5.7 2.9 IBM RS/6000-550 5.6 3.1 HP PA/9000-750 5.1 2.7 IBM RS/6000-370 5.1 2.4 DEC AXP/3000-600 5.0 2.6 RS/6000 PowerPC-43P 4.3 2.0 HP PA/9000-715/80 3.7 2.0 DEC AXP/3000-700 3.7 1.8 Sun SPARCstation 20/HS21 3.5 1.8 Sun Ultra-1/140 3.4 1.7 DEC Alpha 250/4/266 3.2 1.6 HP PA/9000-755 3.1 1.7 HP PA/9000-715/100 3.0 1.6 Sun Ultra-1/170 2.8 1.4 DEC Alpha 2100/5/250 2.6 1.3 HP PA/9000-735/125 2.5 1.3 Sun Ultra-2/200 2.3 1.2 DEC Alpha 600/5-266 2.3 1.2 Pentium Pro/200 2.3 1.2 HP PA/9000-J200 2.1 1.1 DEC Alpha 8400/5-300 2.1 1.1 IBM RS/6000-3BT 2.1 1.1 IBM RS/6000-590 1.9 1.0 DEC Alpha 600/5-333 1.9 1.0 SGI R8000 Indigo^2 1.8 1.1 SGI R8000 Power Onyx 1.8 1.1 IBM RS/6000-3CT 1.8 1.0 SGI PowerOnyx 10000/195 1.1 0.6 HP PA/9000-K460 0.9 0.5 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Alliant FX2808 (1CE) 17.2 9.3 FPS-M64/60 17.1 8.9 Convex C-220 10.6 6.5 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ IBM 3090-600-E/VF 11.3 6.0 Convex C-3860 5.2 3.3 Cray X-MP/416 2.8 1.8 Cray YMP/J90 2.6 1.8 Cray Y-MP/8128 1.5 0.9 CRAY YMP C98/4256 0.9 0.6 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Table 3. Sparse MMO Benchmark: Total CPU times (seconds) for a series of Similarity Transformations (H=Q*HQ, see text) using both Scalar and Vector Algorithms. Machine Algorithm Scalar Vector ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ T800-20 5176.0 4439.0 iPSC/2 SX-node 3809.6 4456.1 Meiko MK086 node 240.1 262.2 iPSC/860 RX-node 118.8 60.1 KSR-2 55.3 34.8 Cray T3D AXP-node 79.3 27.5 IBM SP2 TN2-node 13.8 8.5 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ SOLBOURNE S4000 711.9 750.2 Dec S5000/120 710.2 724.6 Sun 4/370 615.9 609.6 Dec S5000/200 368.3 445.1 SGI 4D/220 344.8 440.3 Sun SPARCstation 2/GS 333.3 394.0 SGI R3000 Indigo 285.0 356.4 SGI 4D/320 245.5 329.5 HP/Apollo DN10020 273.8 245.9 Stardent 1520 236.9 252.8 SGI 4D/420 200.1 273.8 Stardent VISTRA-800 177.0 209.4 Stardent 3020 160.4 144.2 Sun SPARCstation 10/30 141.5 162.3 Sun SPARCstation 10/41 111.3 119.3 Sun SPARCstation 5/85 105.9 151.6 Sun SPARCserver 1000 88.8 92.6 SGI Indigo R4000 98.2 78.7 SGI IRIS Crimson 94.2 78.7 IBM RS/6000-320 139.0 73.3 IBM PowerPC-25T 88.2 68.9 RS/6000 PowerPC-250 92.5 68.7 HP PA/9000-720 124.7 66.5 SGI Challenge L/100 74.9 58.2 HP PA/9000-730 89.9 48.4 IBM RS/6000-530 96.3 46.9 IBM RS/6000-340 70.4 43.4 HP PA/9000-750 58.6 41.5 SGI Indigo^2 R4400 52.3 39.8 SGI Challenge L/150 56.5 39.0 IBM RS/6000-540 66.9 38.4 IBM PowerPC-43P 44.3 36.4 Sun SPARCstation 20/HS21 42.2 35.1 IBM RS/6000-350 55.8 34.8 HP PA/9000-715/80 52.7 34.4 DEC AXP/3000-300 61.0 34.0 IBM RS/6000-530H 58.8 34.0 SGI Indy R5000 34.7 32.1 HP PA/9000-715/100 47.0 29.5 IBM RS/6000-360 48.0 29.3 IBM RS/6000-550 48.3 27.7 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ DEC AXP/3000-500 45.1 23.9 HP PA/9000-735 34.8 20.0 IBM RS/6000-370 37.6 23.3 DEC AXP/3000-600 38.5 20.2 HP PA/9000-755 35.6 20.1 Pentium Pro/200 17.1 23.2 HP PA/9000-J200 33.5 16.2 IBM RS/6000-3BT 30.8 16.2 HP PA/9000-735/125 29.2 16.2 DEC AXP/3000-700 28.3 16.2 DEC Alpha 250/4-266 24.2 13.1 HP PA/9000-K460 10.9 12.0 DEC Alpha 600/5-266 17.7 10.2 IBM RS/6000-3CT 12.6 9.7 Sun Ultra-1/140 17.1 9.5 SGI R8000 Indigo^2 15.2 8.7 SGI R8000 Power Onyx 15.1 8.7 DEC Alpha 2100/5-250 17.7 8.2 Sun Ultra-1/170 15.5 7.9 DEC Alpha 8400/5-300 13.8 7.9 Sun Ultra-2/200 11.5 6.8 DEC Alpha 600/5-333 13.2 6.2 SGI PowerOnyx 10000/195 10.2 6.1 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Alliant FX2800 (1CE) 243.1 86.3 FPS-M64/60 141.2 50.8 CONVEX C-220 128.6 49.0 CONVEX C-3860 55.1 20.1 Cray YMP/J90 44.1 10.6 Cray Y-MP/8128 23.2 5.7 Cray YMP C98/4256 12.6 2.6 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Table 4. Matrix Diagonalization Benchmark. Total CPU times (seconds) for a series of Matrix Diagonalizations (see text) using eight Different Routines. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Machine CPU Time Compiler (seconds) Options ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ T800-20 1100.5 iPSC/2 (SX) 1059.0 -OLM MK086 i860 66.8 -OLM iPSC/860 (RX) 62.3 -O3 KSR-2 27.3 -O2 Cray T3D (AXP) 21.7 -O IBM SP2 (TN2) 9.3 -O3 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Stardent 1520 255.0 -O2 Sun 4/370 219.9 -O SOLBOURNE S4000 169.0 -O3 DEC S5000/120 145.8 -O2 Sun SPARCstation 2/GS 103.3 -O SGI 4D/220 97.4 -O2 Apollo DN10020 96.2 -O DEC S5000/200 90.6 -O2 SGI R3000 Indigo 78.8 -O2 Stardent 3020 68.1 -O2 SGI 4D/320 67.2 -O2 Stardent VISTRA 59.2 -O3 SGI 4D/420 58.1 -O2 IBM RS/6000-320 46.4 -O Sun SPARCstation 10/30 42.2 -O -dalign Sun SPARCstation 10/41 35.9 -O -dalign IBM RS/6000-530 35.6 -O Sun SPARCstation 5/85 32.4 -O -dalign HP PA/9000-720 31.9 -O IBM RS/6000-540 30.8 -O SGI IRIS Crimson 27.6 -sopt IBM RS/6000-340 27.6 -O IBM RS/6000-530H 26.0 -O SGI R4000 Indigo 26.0 -O -mips2 RS6000 PowerPC-250 25.1 -O Sun SPARCserver 1000 24.7 -O -dalign HP PA/9000-730 24.3 -O IBM PowerPC-25T 24.0 -O3 IBM RS/6000-550 22.2 -O IBM RS/6000-350 21.3 -O HP PA/9000-750 20.8 +O3 SGI Challenge L/100 20.4 -O -mips2 IBM RS/6000-360 17.7 -O HP PA/9000-715/80 15.9 +O3 SGI Challenge L/150 15.6 -O -mips2 DEC AXP/3000-300 15.5 -O SGI R4400 Indigo^2 15.2 -O -mips2 DEC AXP/3000-500 14.9 -O IBM RS/6000-370 14.2 -O3 HP PA/9000-715-100 12.8 +O3 Sun SS20/HS21 12.4 -O -dalign Sun SPARCstation 20/HS21 12.4 -O -dalign DEC AXP/3000-600 12.1 -O IBM PowerPC-43P 11.2 -O3 qarch=ppc HP PA/9000-735 10.0 +O3 HP PA/9000-755 10.2 +O3 HP PA/9000-J200 9.6 +O4 IBM RS/6000-3BT 9.4 -O3 qarch=pwr2 IBM RS/6000-590 9.0 -O3 qarch=pwr2 DEC AXP/3000-700 8.7 -O IBM RS/6000-3CT 8.1 -O3 qarch=pwr2 HP PA/9000-735/125 8.1 +O3 SGI R8000 Indigo^2 8.0 -O3 -mips4 SGI R8000 Power Onyx 8.0 -O3 -mips4 SGI Indy R5000 7.7 -O3 -mips4 DEC Alpha 250/4-266 7.3 -fast Sun Ultra-1/140 6.2 -fast -O4 DEC Alpha 600/5-266 5.6 -fast Sun Ultra-1/170 5.4 -fast -O4 Pentium Pro/200 5.3 -G6 -O2 DEC Alpha 2100/5-250 5.2 -fast DEC Alpha 8400/5-300 4.6 -fast Sun Ultra-2/200 4.5 -fast -O4 DEC Alpha 600/5-333 3.9 -fast HP PA/9000-K460 3.5 +Oaggressive SGI PowerOnyx 10000/195 3.1 -O3 -mips4 etc ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Convex C-220 86.5 -O2 Alliant FX2808 99.5 -Og FPS-M64/60 80.7 OPT3 Convex C-3860 36.0 -O2 Cray X-MP4/16 21.3 Cray YMP/J90 26.7 Cray Y-MP4/64 16.0 Cray YMP C98/4256 9.9 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Table 5. The Matrix Benchmark: Performance relative to the SGI Power Onyx R10000/195 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Machine MMO MMO Diagonal Total (FORTRAN) (Q*HQ) -ization ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ SGI R10000/195 100% 100% 100% 100% HP PA/9000-K460 123% 57% 90% 90% DEC Alpha 600/5-333 61% 99% 80% 80% Sun Ultra-2/200 50% 90% 70% 70% DEC Alpha 8400/5 300 55% 78% 68% 67% DEC Alpha 2100/5 250 44% 75% 60% 60% Sun Ultra-1/170 41% 78% 58% 59% SGI R8000 Power Onyx 63% 71% 39% 58% SGI R8000 Indigo^2 63% 71% 39% 58% IBM SP2 (TN2 node) 63% 72% 35% 57% DEC Alpha 600/5-266 50% 60% 56% 55% IBM RS/6000-3CT 63% 64% 38% 55% Sun Ultra-1/140 33% 64% 50% 49% Pentium Pro/200 50% 36% 59% 48% HP/9000-J200 55% 38% 32% 42% IBM RS/6000-3BT 54% 38% 33% 42% DEC Alpha 250/4-266 36% 47% 43% 42% HP PA/9000-735/125 46% 38% 39% 41% Cray YMP/J90 44% 58% 12% 38% DEC AXP/3000-700 31% 38% 36% 35% HP PA/9000-755 37% 31% 31% 33% SGI Indy R5000 25% 22% 41% 29% HP PA/9000-715/100 38% 21% 34% 28% DEC AXP/3000-600 23% 30% 26% 26% Sun SparcStation-20/HS21 33% 17% 25% 25% IBM PowerPC-43P 27% 17% 28% 24% IBM RS/6000-370 22% 26% 22% 24% HP PA/9000-715/80 31% 18% 20% 23% DEC AXP/3000-500 20% 26% 21% 22% IBM RS/6000-360 20% 21% 18% 20% DEC AXP/3000-300 19% 18% 20% 19% IBM RS/6000-550 20% 21% 14% 19% HP PA/9000-750 22% 15% 15% 18% SGI R4400 Indigo^2 16% 15% 21% 17% SGI Challenge L/150 16% 15% 20% 17% Cray T3D AXP 13% 22% 14% 16% IBM RS/6000-350 15% 18% 15% 16% IBM RS/6000-340 13% 14% 11% 13% SGI Challenge L/100 11% 11% 15% 12% IBM PowerPC-25T 12% 9% 13% 12% Sun SPARCserver-1000 12% 7% 13% 11% HP PA/9000-720 13% 9% 10% 11% SGI R4000 Indigo 9% 8% 12% 10% Sun SparcStation-10/41 10% 6% 9% 8% Sun SparcStation-5/85 8% 6% 10% 8% IBM RS/6000-320 8% 8% 7% 8% Sun SparcStation-10/30 7% 4% 7% 6% Stardent VISTRA 6% 3% 5% 5% SGI 4D/420 5% 3% 5% 4% SGI 4D/320 4% 3% 5% 4% SGI R3000 Indigo 4% 2% 4% 3% Sun SPARCstation-2 4% 2% 3% 3% DEC S5000/200 3% 2% 3% 3% Stardent 1520 4% 3% 1% 3% Apollo DN10020 3% 2% 3% 3% SGI 4D/220 3% 2% 3% 3% DEC S5000/120 2% 1% 2% 2% Solbourne S4000 2% 1% 2% 1% ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Table 6. Computational Chemistry Kernels ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Machine Total CPU time (secs) Mflop ===================== SCF MD MC Jacobi ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Sun SPARCstation 10/30 758 2214 299 6.9 Sun SPARCstation 10/41 665 1788 266 6.6 Sun SPARCstation 5/85 1036 1082 195 6.7 Sun SPARCserver 1000 441 783 148 6.7 RS6000 Power-PC 250 603 1322 149 15.6 IBM RS/6000-530H 649 1594 176 26.7 IBM RS/6000-340 692 1608 187 21.2 IBM Power-PC 25T 409 1322 145 15.6 SGI Challenge L/100 400 794 130 9.5 SGI R4000 Indigo 375 809 112 9.5 DEC AXP/3000-300 427 716 98 12.0 IBM RS/6000-350 523 1288 141 27.4 HP PA/9000-750 237 554 163 11.5 %IBM RS/6000-550 563 1266 149 39.9 IBM RS/6000-360 433 1071 117 35.1 HP PA/9000-715/80 220 427 133 17.1 SGI Challenge L/150 233 517 78 13.3 SGI R4400 Indigo^2 234 523 78 13.7 DEC AXP/3000-600 314 541 79 21.6 HP 9000/715-100 177 354 107 17.3 IBM Power-PC 43P 135 470 56 14.8 IBM RS/6000-370 344 854 93 44.2 HP PA/9000-J200 121 292 92 12.4 Sun SPARCstation 20/HS21 132 319 84 16.3 HP PA/9000-755 131 284 102 18.6 DEC AXP/3000-700 146 411 54 21.7 HP PA/9000-735/125 103 236 91 15.0 SGI Indy R5000 124 219 42 12.5 SGI R8000 Indigo^2 139 218 49 23.7 Pentium Pro/200 142 321 38 28.4 SGI Power Onyx R8000 138 218 49 32.9 DEC Alpha 250/4/266 79 272 37 20.9 IBM/RS6000-3BT 140 389 43 44.8 Sun Ultra-1/140 110 186 61 45.2 IBM RS/6000-590 146 453 43 86.0 Sun Ultra-1/170 98 146 58 47.3 DEC Alpha 2100/5/250 59 187 28 27.3 IBM SP2 TN2-node 138 383 43 68.6 HP PA/9000-K460 68 138 31 31.0 DEC Alpha 600/5/266 57 210 31 40.9 IBM RS/6000-3CT 138 462 42 81.1 Sun Ultra-2/200 82 121 49 56.5 DEC Alpha 8400/5/300 50 183 27 43.9 DEC Alpha 600/5/333 46 137 21 44.9 SGI PowerOnyx 10000 43 106 22 39.1 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ CONVEX C-220 1015 1017 329 33.1 CONVEX C-3860 453 342 141 69.9 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Table 7. The GAMESS-UK Single Processor Benchmark Number Module Basis (GTOs) Details Molecular Species ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 1. SCF STO-3G (124) Morphine 2. SCF 6-31G (154) C6H3(NO2)3 3. ECP Geometry ECPDZ (70) Na7Mg+ Optimization 4. Direct-SCF 6-31G (82) Cytosine 5. CASSCF TZVP (52) 480 csf H2CO Geometry Opt. 6. MCSCF (5s3p2d/3s1p/f(O) 5608 H2CO (74) 7. Direct-CI (5s3p2d/3s1p) 3M/167194 H2CO/H2+CO TS (64) 8. Table-CI (26M/6R) ECP (59) 2301815/4097 TiCl4 9. MP2 Geometry 6-31G* (70) H3SiNCO Optimization 10. SCF Second 6-31G (64) C5H5N Derivatives 11. MP2 Second 6-31G* (60) C4 Derivatives 12. Direct-MP2 DZ+D(N) (76) C5H5N ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Table 8. The GAMESS-UK Benchmark: Total CPU time (user and system) Elapsed time (minutes) and Efficiency (%) for Calculations 1 - 12 (see text) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Machine CPU Time Elapsed Efficiency User System Time (%) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ DEC Alpha 600/5/333 23.4 7.6 34.4 91% SGI PowerOnyx 10000 23.1 3.6 36.7 73% Sun Ultra-2/200 31.5 3.0 38.1 91% DEC Alpha 2100/5/250 30.2 8.2 43.6 88% SGI R8000 Power Onyx 48.1 5.6 55.2 97% Sun Ultra-1/170 40.5 4.9 56.3 81% IBM RS/6000-3CT 40.1 4.4 58.3 76% SGI Indigo^2 R8000 50.8 7.2 60.6 96% IBM RS/6000-590 46.6 4.7 74.8 68% DEC Alpha 600/5/266 30.6 6.4 62.2 59% DEC Alpha 250/4/266 46.0 9.7 85.7 65% DEC Alpha 8400/5/300 26.9 10.4 92.8* 40% HP PA/9000-735/125 51.6 8.4 93.3 64% HP PA/9000-755 64.9 8.8 98.2 75% SGI Indy R5000 72.9 11.3 101.0 83% DEC AXP/3000-700 53.4 11.5 105.4 62% DEC AXP/3000-600 72.1 14.1 107.8 80% IBM RS/6000-370 78.7 8.4 108.8 80% SGI R4400 Challenge L/150 93.3 10.3 110.6 94% SGI R4400 Indigo^2 94.3 10.0 120.6 86% HP 9000/715-100 81.4 14.3 124.7 77% Sun Ultra-1/140 45.3 5.7 139.8* 36% HP PA/9000-715/80 93.7 15.9 140.1 78% DEC AXP/3000/500 93.0 40.1 148.6 90% HP PA/9000-750 111.1 17.4 140.2 92% IBM RS/6000-550 117.6 11.2 146.4 88% SGI Challenge L/100 140.8 17.1 178.0 89% DEC AXP/3000-300 129.8 39.6 184.0 92% Sun SS-20/HS21 82.5 16.2 202.7 49% SGI R4000 Indigo 153.2 17.7 206.0 83% Sun SPARCserver 1000 175.1 50.8 445.6 51% Sun SPARCstation 10/41 201.4 21.0 260.4 85% Sun SPARCstation 5/85 240.5 25.8 299.0 89% Sun SPARCstation 10/30 251.4 31.7 306.3 92% ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ CONVEX C-3860 124.3 3.5 210.9 61% Cray Y-MP/8128 56.8 6.0 188.4 33% Cray YMP-C98/4256 37.9 2.0 41.2 97% ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ * see footnotes to Table 9 Table 9. The Chemistry Benchmark: Performance relative to the DEC Alpha 600/5/333 (see text) Machine SPECfp SPECint Matrix CC GAMESS-UK Kernels CPU Wall ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ DEC Alpha 600/5/333 100 100 100 100 100 100 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ HP PA/9000-K460 124 113 124 76 SGI R10000/195 94 130 104 116 93 DEC Alpha 8400/5/300 94 81 85 85 83 37 * DEC Alpha 600/5/266 89 86 71 76 84 55 Sun Ultra-2/200 84 83 87 86 90 90 IBM RS/6000-3CT 77 37 72 73 70 59 Sun Ultra-1/170 69 60 73 70 68 61 DEC Alpha 2100/5/250 64 65 74 71 81 79 Sun Ultra-1/140 60 50 61 63 61 25 + SGI Power Onyx $ 57 26 75 53 58 62 Pentium Pro/200 51 88 64 48 SGI Indigo^2 R8000 $ 49 27 75 48 54 57 DEC Alpha 250/4/266 48 56 53 53 56 40 DEC 3000/700 AXP $ 43 40 45 38 48 32 SGI Indy R5000 36 39 44 37 34 HP 9000/735-125 35 43 54 40 52 37 HP 9000/755 $ 31 26 43 36 42 35 DEC 3000/600 AXP $ 30 28 33 29 36 17 Sun SS-20/HS21 $ 28 32 34 35 31 17 DEC 3000/500 AXP 28 23 28 19 23 23 HP 9000/715-100 26 31 38 31 32 27 HP 9000/715-80 $ 23 23 31 27 28 24 IBM RS/6000-370 $ 22 17 30 37 36 31 DEC 3000/300 AXP $ 17 16 25 20 18 19 SGI Challenge L/150 $ 16 22 22 26 30 31 SGI Indigo^2 R4400 $ 16 22 23 26 30 28 SPARC SS-1000 $ 15 14 14 14 8 HP 9000/750 $ 14 12 24 21 24 24 IBM RS/6000-550 $ 13 9 24 31 24 23 SGI Challenge L/100 $ 12 15 16 17 20 19 SGI Indigo R4000 $ 11 14 13 17 18 17 Sun SS-10/41 10 12 11 9 14 13 Sun SS-5/85 $ 10 16 10 11 12 11 Sun SS-10/30 $ 10 11 8 9 11 11 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ $ - SPECfp and SPECint rations based on SPEC92 values. * - multi-user environment on DEC Alpha 8400/5/300. + - small memory configuration (32 MByte) on Sun Ultra-1/140. Table 10. APPENDIX I: Machine Configurations under Evaluation ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Machine Configuration Location ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Workstations ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Solbourne S4000 DL (loan) Sun 4/370 DL Sun SPARCstation 2/GS SPARC/40 Mhz DL (loan) Sun SPARCstation 5/85 MicroSPARC II/85 MHz DL (loan) Sun SPARCstation 10/30 SuperSPARC/36 MHz DL (loan) Sun SPARCstation 10/41 SuperSPARC/40 MHz PNL Sun SPARCserver 1000 SuperSPARC/50 MHz DL (loan) Sun SPARCstation 20/HS21 HyperSPARC/125 MHz DL(loan) Sun Ultra-1 Model 140 UltraSPARC-1/143 MHz DL (loan) Sun Ultra-1 Model 170 UltraSPARC-1/167 MHz DL Sun Ultra-2 Model 200 UltraSPARC-2/200 MHz DL (loan) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ HP/Apollo DN10020 PRISM DL HP PA/9000-720 PA7000/50 MHz DL HP PA/9000-730 PA7000/66 MHz MCC HP PA/9000-750 PA7000/66 MHz DL HP PA/9000-715/100 PA7100LC/100 MHz DL (loan) HP PA/9000-715/80 PA7100LC/80 MHz DL (loan) HP PA/9000-755 PA7100/99 MHz DL HP PA/9000-735/125 PA7150/125 MHz PNL HP PA/9000-J200 PA7200/100 MHz DL (loan) HP PA/9000-K460 PA8000/160 MHz DL (loan) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ DEC S5000/120 R3000A/R3010A 20 Mhz DL (loan) DEC S5000/200 R3000A/R3010A 25 MHz DL (loan) DEC AXP/3000-300 AXP A21064/150 MHz PNL DEC AXP/3000-500 AXP A21064/150 MHz DL (loan) DEC AXP/3000-600 AXP A21064/175 MHz PNL DEC AXP/3000-700 AXP A21064A/225 MHz DL (loan) DEC Alpha 250/4/266 AXP A21064A/266 MHz DL (loan) DEC Alpha 600/5/266 AXP A21164/266 MHz DL (loan) DEC Alpha 2100/5/250 AXP A21164/250 MHz DL (loan) DEC Alpha 8400/5/300 AXP A21164/300 MHz RAL DEC Alpha 600/5/333 AXP A21164/333 MHz DL (loan) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ SGI 4D/220 GTX DL SGI 4D/320 R3000A/R3010A 33 MHz DL (loan) SGI 4D/420 DL (loan) SGI IRIS Crimson R4000/R4010 100 Mhz DL (loan) SGI R3000 Indigo R3000A/R3010A 33 MHz DL (loan) SGI R4000 Indigo R4000/R4010 100 MHz DL (loan) SGI Challenge L/100 R4400/R4010 100 MHz Utrecht SGI R4400 Indigo^2 R4400/R4010 150 MHz DL (loan) SGI Challenge L/150 R4400/R4010 150 MHz Southampton SGI R8000 Indigo^2 R8000/R8010 75 MHz DL (loan) SGI R8000 Power Onyx R8000/R8010 75 MHz DL (loan) SGI Indy R5000 R5000/R5000 180 Mhz DL (loan) SGI PowerOnyx 10000 R10000/R10010 195 MHz DL (loan) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Stardent 1520 DL Stardent 3020 DL (loan) Stardent VISTRA-800 DL (loan) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ IBM Power1 RS/6000-320 DL (loan) IBM Power1 RS/6000-530 DL (loan) IBM Power1 RS/6000-530H RS6000/33 MHz DL IBM Power1 RS/6000-540 Perugia IBM PowerPC-25T MPC601 66 MHz DL IBM PowerPC-43P MPC604 100 MHz DL IBM Power1 RS/6000-340 RS6000/33 MHz DL (loan) IBM Power1 RS/6000-350 RS6000/41.6 MHz DL (loan) IBM Power1 RS/6000-550 RS6000/41.6 MHz Perugia IBM Power1 RS/6000-360 RS6000/50 MHz DL (loan) IBM Power1 RS/6000-370 RS6000/62.5 MHz DL IBM Power2 RS/6000-590 RS6000/66 MHz IBM IBM Power2 RS/6000-3BT RS6000/67 MHz DL (loan) IBM Power2 RS/6000-3CT RS6000/72 MHz DL (loan) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ PCs ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Netpower PC Pentium Pro/200Mhz DL (loan) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ SuperMinis ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Alliant FX2808 DL FPS-M64/60 DL Convex C-220 DL ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ SuperComputers ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Convex C-3860 ULCC IBM 3090-600-E VF RAL Cray YMP J90/10 EPCC Cray Y-MP/8128 SARA Cray YMP C98/4256 SARA ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Novel architecture Node ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Meiko Computing Surface T800-20 DL iPSC/2 SX-node DL iPSC/2 VX-node DL iPSC/860 RX-i860 node (40 MHz) DL KSR-2 KSR-2 node (80 MHz) PNL Cray T3D AXP node (150 MHz) Edinburgh IBM SP2 TN2 node (67 MHz) DL ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ========= ========= ========= ======== ****** ****** _______________________________________________________________________ Carles Colominas Dept. of Org. Chem. E-mail ccolo@mompou.iqs.url.es Institut Quimic de Sarria. TEL: (34-3)-203.89.00 Universitat Ramon Llull. FAX: (34-3)-205.62.66 Via Augusta 390. 08017-Barcelona. CATALONIA. _______________________________________________________________________ From toukie@zui.unizh.ch Tue Oct 22 09:16:37 1996 Received: from zzmkgtw.zzmk.unizh.ch for toukie@zui.unizh.ch by www.ccl.net (8.8.0/950822.1) id JAA05614; Tue, 22 Oct 1996 09:04:55 -0400 (EDT) Received: by zzmkgtw.zzmk.unizh.ch; (5.65v3.2/1.3/10May95) id AA13072; Tue, 22 Oct 1996 14:09:16 +0100 Message-Id: <1.5.4.32.19961022130546.0066ecd0@zui.unizh.ch> X-Sender: toukie@zui.unizh.ch (Unverified) X-Mailer: Windows Eudora Light Version 1.5.4 (32) Mime-Version: 1.0 Content-Type: text/plain; charset="us-ascii" Date: Tue, 22 Oct 1996 14:05:46 +0100 To: chemistry@www.ccl.net From: "Hr. Dr. S. Shapiro" Subject: Seeking CCDB data Dear Colleagues; I wish to search the Cambridge Crystallographic Database for struc- tures that might have been deposited there which meet all of the following criteria: (i) they are phenolic; and (ii) they have an intramolecular hydrogen bonds between the phenolic -OH moiety and some other group (e.g., an oxygen atom or a C=O moiety ortho to the phenolic -OH moiety); and (iii) their structures have been determined by neutron diffraction (I need experimental values for the positions of the hydrogen atoms as well as for the heavy atoms). I applied to the Pittsburgh Supercomputer Centre to access their CCDB mirror, but I was turned down because I am not affilated with an Ameri- can institution. Therefore, if anyone out there in CCL-Land would be will- ing to undertake a search for me in some mirror of the CCDB for compounds meeting the above criteria, I would greatly appreciate hearing from you. Yours most respectfully, (Dr.) S. Shapiro Inst. f. orale Mikrobiol. u. allg. Immunol. Zent. f. Zahn-, Mund- u. Kieferheilkd. der Univ. ZH Plattenstr. 11 Postfach CH-8028 Zuerich 7 Switzerland Internet: toukie@zui.unizh.ch FAX-nr: ( ... + 1) 261'56'83 From kate_holloway@Merck.Com Tue Oct 22 10:16:29 1996 Received: from igw2 for kate_holloway@Merck.Com by www.ccl.net (8.8.0/950822.1) id JAA05916; Tue, 22 Oct 1996 09:59:32 -0400 (EDT) Received: from igw2.merck.com; Tue, 22 Oct 1996 09:53:56 -0400 Message-Id: <199610221353.JAA08980@igw2> Received: from mailhost.merck.com(54.3.1.99) by ngatekeeper.merck.com via smap (g3.0.1) id sma008612; Tue, 22 Oct 96 09:53:24 -0400 Date: Tue, 22 Oct 1996 09:59:46 -0500 From: kate_holloway@Merck.Com (Kate Holloway) Subject: Unsubsribe To: chemistry@www.ccl.net Content-transfer-encoding: 7BIT Unsubsribe I would like to unsubscribe from this list. I'm sorry to send this message out to everyone but I can't seem to find my copy of the instructions which give the appropriate address for subscriptions/unsubscriptions, etc. Thanks. -- Kate Holloway From bru@fcu.um.es Tue Oct 22 11:28:00 1996 Received: from unimur.um.es for bru@fcu.um.es by www.ccl.net (8.8.0/950822.1) id KAA06190; Tue, 22 Oct 1996 10:41:53 -0400 (EDT) Received: from fcu.um.es (gaia.fcu.um.es) by unimur.um.es (4.1/SMI-4.1) id AA05656; Tue, 22 Oct 96 16:44:55 +0200 Received: from roquebru.bio.um.es by fcu.um.es (5.x/SMI-SVR4) id AA09994; Tue, 22 Oct 1996 16:44:26 +0100 Message-Id: <9610221544.AA09994@fcu.um.es> X-Sender: bru@gaia.fcu.um.es Mime-Version: 1.0 Content-Type: text/plain; charset="us-ascii" Date: Tue, 22 Oct 1996 16:43:12 +0100 To: chemistry@www.ccl.net From: bru@fcu.um.es (Roque Bru Martinez) Subject: CCL:formulations freeware or software X-Mailer: Dear netters: I am looking for freeware or software to desing formulations to attain emulsions with specific desired properties, in particular droplet size, viscosity, stability, and so on. May someone give me some clue? Thanx ---------------------------------------------------------------------------- Dr. Roque Bru-Martinez Phone: (+34 68) 30.71.00 ext 2945 Dpto. Bioquimica y Biol Mol. (A) Fax.....: (+34 68) 36.41.47 and 36.39.63 Fac. Veterinaria e-mail: bru@fcu.um.es Unidad Docente de Biologia UNIVERSIDAD DE MURCIA Sent using Eudora 1.4 Apto. 4021 E-30071 Murcia Spain ---------------------------------------------------------------------------- From morokuma@euch4e.chem.emory.edu Tue Oct 22 12:16:35 1996 Received: from euch4e.chem.emory.edu for morokuma@euch4e.chem.emory.edu by www.ccl.net (8.8.0/950822.1) id MAA07324; Tue, 22 Oct 1996 12:08:24 -0400 (EDT) From: Received: by euch4e.chem.emory.edu (AIX 3.2/UCB 5.64/4.03) id AA15038; Tue, 22 Oct 1996 12:08:25 -0400 Message-Id: <9610221608.AA15038@euch4e.chem.emory.edu> Subject: icqc To: chemistry@www.ccl.net Date: Tue, 22 Oct 1996 12:08:25 -0400 (EDT) X-Mailer: ELM [version 2.4 PL24] Mime-Version: 1.0 Content-Type: text/plain; charset=US-ASCII Content-Transfer-Encoding: 7bit 9th International Congress of Quantum Chemistry Emory Conference Center Hotel Emory University Atlanta, Georgia June 9-14, 1997 SECOND CIRCULAR is now available at http://www.emerson.emory.edu/conferences/icqc97.html From cbonner@vger.nsu.edu Tue Oct 22 13:16:30 1996 Received: from mail.secorp.com for cbonner@vger.nsu.edu by www.ccl.net (8.8.0/950822.1) id MAA07523; Tue, 22 Oct 1996 12:22:17 -0400 (EDT) Received: by mail.secorp.com from localhost (router,SLmailFW V2.0); Tue, 22 Oct 1996 09:24:03 PDT Received: by mail.secorp.com from mail.secorp.com (205.217.32.204::mail daemon; unverified,SLmailFW V2.0); Tue, 22 Oct 1996 09:24:02 PDT Received: from www.ccl.net (www.ccl.net [128.146.36.5]) by emf.emf.net (EMF-K/K) with ESMTP id JAA23433 for ; Tue, 22 Oct 1996 09:02:16 -0700 Received: for chemistry-request@www.ccl.net by www.ccl.net (8.8.0/950822.1) id LAA06653; Tue, 22 Oct 1996 11:24:20 -0400 (EDT) Received: from vger.nsu.edu for cbonner@vger.nsu.edu by www.ccl.net (8.8.0/950822.1) id KAA06178; Tue, 22 Oct 1996 10:41:00 -0400 (EDT) Received: from cbonner.nsu.edu by vger.nsu.edu with SMTP ; Tue, 22 Oct 96 10:40:38 EST Message-ID: <326CDC4C.6277@vger.nsu.edu> Date: Tue, 22 Oct 1996 10:38:04 -0400 From: "Carl E. Bonner" Organization: Norfolk State University X-Mailer: Mozilla 2.02Gold (Win95; I) To: chemistry@www.ccl.net CC: cbonner@vigyan.nsu.edu Subject: CCL:X-ray structures for gemstone crystals e.g. YAG, YLF, etc. X-URL: http://www.ccl.net/ccl/welcome.html#3 Sender: Computational Chemistry List Errors-To: ccl@www.ccl.net Precedence: bulk X-UIDL: b27ada4b86f288b3b436f6e186932a83 Netters, I am trying to perform calculations on the energy levels of an rare earth dopant in a gemstone crystal such as YAG or YLF. In particular I am looking for the crystal structure in some electronic data format. I have checked both Brookhaven and the Cambridge structures archive. They both contain organic or biorganic molecule structures. Is there an equivalent database for these inorganic structures. I will summarize the responses Thanks in advance Carl -- Carl E. Bonner, Jr. Ph.D | Departmen of Chemistry and Center for Materials Research, | You either do or you don't, there is no try. Norfolk State University, 2401 Corprew Ave., Norfolk, Va 23504 | -- Yoda -- Email: cbonner@vger.nsu.edu (757) 683-2381/2296/9054 (o/l/f) | -------This is added Automatically by the Software-------- -- Original Sender Envelope Address: cbonner@vger.nsu.edu -- Original Sender From: Address: cbonner@vger.nsu.edu CHEMISTRY@www.ccl.net: Everybody | CHEMISTRY-REQUEST@www.ccl.net: Coordinator MAILSERV@www.ccl.net: HELP CHEMISTRY or HELP SEARCH | Gopher: www.ccl.net 73 Anon. ftp: www.ccl.net | CHEMISTRY-SEARCH@www.ccl.net -- archive search Web: http://www.ccl.net/chemistry.html From chpajt@bath.ac.uk Tue Oct 22 13:16:36 1996 Received: from mail.secorp.com for chpajt@bath.ac.uk by www.ccl.net (8.8.0/950822.1) id MAA07779; Tue, 22 Oct 1996 12:51:13 -0400 (EDT) Received: by mail.secorp.com from localhost (router,SLmailFW V2.0); Tue, 22 Oct 1996 09:53:05 PDT Received: by mail.secorp.com from mail.secorp.com (205.217.32.204::mail daemon; unverified,SLmailFW V2.0); Tue, 22 Oct 1996 09:53:00 PDT Received: from www.ccl.net (www.ccl.net [128.146.36.5]) by emf.emf.net (EMF-K/K) with ESMTP id JAA24697 for ; Tue, 22 Oct 1996 09:24:59 -0700 Received: for chemistry-request@www.ccl.net by www.ccl.net (8.8.0/950822.1) id LAA06760; Tue, 22 Oct 1996 11:31:10 -0400 (EDT) Received: from goggins.bath.ac.uk for chpajt@bath.ac.uk by www.ccl.net (8.8.0/950822.1) id KAA06035; Tue, 22 Oct 1996 10:22:16 -0400 (EDT) Received: from bath.ac.uk (actually host midge.bath.ac.uk) by goggins.bath.ac.uk with SMTP (PP); Tue, 22 Oct 1996 14:55:06 +0100 Date: Tue, 22 Oct 1996 14:55:00 +0100 (BST) From: A J Turner To: chemistry@www.ccl.net Subject: CCL:function only fitting methods Message-ID: Sender: Computational Chemistry List Errors-To: ccl@www.ccl.net Precedence: bulk X-UIDL: ca96b4a5334976956b7b5d9e2cd22726 Hi! Does anyone know of a really good (public domain) algorithum for minimisation without analytical gradients where my number of degrees of freedom are many less than my error function? Ie I want to reduce the error function to as close to zero as posible. I have had a look at a flavour of Bartel's method - but I would appreciate any other ideas - or even a general pd implementation of Bartel's method. Thanks for any help Alex ------------------------------------------------------------------- |Alexander J Turner |A.J.Turner@bath.ac.uk | |Post Graduate |http://www.bath.ac.uk/~chpajt/home.html| |School of Chemistry |+144 1225 8262826 ext 5137 | |University of Bath | | |Bath, Avon, U.K. |Field: QM/MM modeling | ------------------------------------------------------------------- -------This is added Automatically by the Software-------- -- Original Sender Envelope Address: chpajt@bath.ac.uk -- Original Sender From: Address: chpajt@bath.ac.uk CHEMISTRY@www.ccl.net: Everybody | CHEMISTRY-REQUEST@www.ccl.net: Coordinator MAILSERV@www.ccl.net: HELP CHEMISTRY or HELP SEARCH | Gopher: www.ccl.net 73 Anon. ftp: www.ccl.net | CHEMISTRY-SEARCH@www.ccl.net -- archive search Web: http://www.ccl.net/chemistry.html From uscfampl@cesga.es Tue Oct 22 14:16:29 1996 Received: from ds for uscfampl@cesga.es by www.ccl.net (8.8.0/950822.1) id NAA08221; Tue, 22 Oct 1996 13:48:26 -0400 (EDT) Received: by ds (4.1/1.34) id AA14805; Tue, 22 Oct 96 19:47:01 +0100 Date: Tue, 22 Oct 1996 19:47:01 +0200 (MET) From: Manuel Pereiro Lopez To: CHEMISTRY@www.ccl.net Subject: Subscribe me Message-Id: Mime-Version: 1.0 Content-Type: TEXT/PLAIN; charset=US-ASCII Content-Transfer-Encoding: QUOTED-PRINTABLE I=B4ll like studing Chemistry then I want to subscribe me. My=20 favourite topic is the Density-functional theory. =09=09=09=09=09=09Thanks: =09=09=09=09 =09MANUEL PEREIRO From ap16390@ggr.co.uk Tue Oct 22 14:16:34 1996 Received: from mail.secorp.com for ap16390@ggr.co.uk by www.ccl.net (8.8.0/950822.1) id NAA08231; Tue, 22 Oct 1996 13:49:08 -0400 (EDT) From: Received: by mail.secorp.com from localhost (router,SLmailFW V2.0); Tue, 22 Oct 1996 10:51:02 PDT Received: by mail.secorp.com from mail.secorp.com (205.217.32.204::mail daemon; unverified,SLmailFW V2.0); Tue, 22 Oct 1996 10:51:02 PDT Received: from www.ccl.net (www.ccl.net [128.146.36.5]) by emf.emf.net (EMF-K/K) with ESMTP id KAA28149 for ; Tue, 22 Oct 1996 10:20:35 -0700 Received: for chemistry-request@www.ccl.net by www.ccl.net (8.8.0/950822.1) id MAA07495; Tue, 22 Oct 1996 12:19:58 -0400 (EDT) Received: from gate.ggr.co.uk for ap16390@ggr.co.uk by www.ccl.net (8.8.0/950822.1) id MAA07382; Tue, 22 Oct 1996 12:16:05 -0400 (EDT) Received: from mailhub.ggr.co.uk (uk0x07.ggr.co.uk [147.184.146.69]) by gate.ggr.co.uk; Tue, 22 Oct 1996 17:14:06 +0100 (BST) Received: from itsg02.italy.glaxo (itsg02.italy.glaxo [161.147.83.59]) by mailhub.ggr.co.uk; Tue, 22 Oct 1996 17:04:52 +0100 (BST) Received: by itsg02.italy.glaxo (8.7.5/imd160294) id RAA15569; Tue, 22 Oct 1996 17:14:16 GMT Date: Tue, 22 Oct 1996 17:14:16 GMT Message-Id: <199610221714.RAA15569@itsg02.italy.glaxo> To: CHEMISTRY@www.ccl.net Subject: CCL:Roto-matrix Sender: Computational Chemistry List Errors-To: ccl@www.ccl.net Precedence: bulk X-UIDL: 3467b4cddabc53670c23a11eccabb114 Dear Netters, I would like to ask for an expertise about liner transformations performed via rotational matrixes. Given the 3D coordinates of three points P1(xyz) P2(xyz) and P3(xyz) in their initial (T0) and final (T1) position. Which is the best way for calculating the rotational matrix and translation capable of such transformation ? (distances between the three points are constants so we can consider it "rigid" between each others) Any help and or information is very welcome and I will summarize your contributions. Yours sincerely -Alfonso Pozzan >>>>>>>>>>>>Alfonso Pozzan Ph.D, GlaxoWellcome e-mail: ap16390@ggr.co.uk, fax: +39-45-9218196 via Fleming 2, 37100 Verona ITALY <<<<<<<<<<<< -------This is added Automatically by the Software-------- -- Original Sender Envelope Address: ap16390@ggr.co.uk -- Original Sender From: Address: ap16390@ggr.co.uk CHEMISTRY@www.ccl.net: Everybody | CHEMISTRY-REQUEST@www.ccl.net: Coordinator MAILSERV@www.ccl.net: HELP CHEMISTRY or HELP SEARCH | Gopher: www.ccl.net 73 Anon. ftp: www.ccl.net | CHEMISTRY-SEARCH@www.ccl.net -- archive search Web: http://www.ccl.net/chemistry.html From M.Biggs@surrey.ac.uk Tue Oct 22 14:16:38 1996 Received: from mail.secorp.com for M.Biggs@surrey.ac.uk by www.ccl.net (8.8.0/950822.1) id NAA08011; Tue, 22 Oct 1996 13:20:13 -0400 (EDT) Received: by mail.secorp.com from localhost (router,SLmailFW V2.0); Tue, 22 Oct 1996 10:22:07 PDT Received: by mail.secorp.com from mail.secorp.com (205.217.32.204::mail daemon; unverified,SLmailFW V2.0); Tue, 22 Oct 1996 10:22:06 PDT Received: from www.ccl.net (www.ccl.net [128.146.36.5]) by emf.emf.net (EMF-K/K) with ESMTP id JAA26530 for ; Tue, 22 Oct 1996 09:55:53 -0700 Received: for chemistry-request@www.ccl.net by www.ccl.net (8.8.0/950822.1) id MAA07563; Tue, 22 Oct 1996 12:26:02 -0400 (EDT) Received: from mailc.surrey.ac.uk for M.Biggs@surrey.ac.uk by www.ccl.net (8.8.0/950822.1) id MAA07348; Tue, 22 Oct 1996 12:11:42 -0400 (EDT) Message-Id: <199610221611.MAA07348@www.ccl.net> Received: from surrey.ac.uk by mailc.surrey.ac.uk id <23613-0@mailc.surrey.ac.uk>; Tue, 22 Oct 1996 17:10:26 +0100 Subject: CCL:Database of polycyclic and hetrocyclic compounds To: chemistry@www.ccl.net Date: Tue, 22 Oct 1996 17:10:25 +0100 (BST) X-Mailer: ELM [version 2.4 PL25] From: Dr Mark J Biggs X-Original-Sender: M.Biggs@surrey.ac.uk Sender: Computational Chemistry List Errors-To: ccl@www.ccl.net Precedence: bulk X-UIDL: 870d719bd29cd4fe270677e7776598f8 I recently asked the following question: > I am trying to find a database that lists > atleast all the possible polycyclic/hetrocyclic > compounds upto about 12 rings and hetroatoms of > oxygen, sulphur and nitrogen. Of these hetroatoms, > the most important is oxygen as I guess the > number of permutations will become large with > sulphur included. > > I have looked in our library and am a little > daunted by the 50+ volumes of possible compounds > with no apparent global index or heirachy - I am > hoping a computer database exists or a good > hardcopy index with some sort of listing that > starts at 1 ring and no heteroatoms and goes down > from there (i.e. something that has done the sorting > for a non-organic chemist). I recieved several replies: 2 asking for the results from this query, 3 with words of advice. It appears as if computer databases do exist but they cost #$. We have the RING INDEX in our library and this was useful. In summary: (a) RING INDEX (books with "nice" index). (b) Beilstein database - part of STN International information network. (c) Chapman and Hall, Derwent, Index Chemicus, Current Drugs etc 3D databases available in UNITY format (look at http://www.tripos.com for more info). (d) CAST-3D subsets tailored to your own specification from Chemical Abstracts. If you want any more details on these, please do not hesitate to contact me. Many thanks for all those who responded. Mark M.Biggs@surrey.ac.uk -------This is added Automatically by the Software-------- -- Original Sender Envelope Address: M.Biggs@surrey.ac.uk -- Original Sender From: Address: M.Biggs@surrey.ac.uk CHEMISTRY@www.ccl.net: Everybody | CHEMISTRY-REQUEST@www.ccl.net: Coordinator MAILSERV@www.ccl.net: HELP CHEMISTRY or HELP SEARCH | Gopher: www.ccl.net 73 Anon. ftp: www.ccl.net | CHEMISTRY-SEARCH@www.ccl.net -- archive search Web: http://www.ccl.net/chemistry.html From chemtw@showme.missouri.edu Tue Oct 22 19:23:45 1996 Received: from mail.secorp.com for chemtw@showme.missouri.edu by www.ccl.net (8.8.0/950822.1) id TAA10524; Tue, 22 Oct 1996 19:08:17 -0400 (EDT) Received: by mail.secorp.com from localhost (router,SLmailFW V2.0); Tue, 22 Oct 1996 16:10:02 PDT Received: by mail.secorp.com from mail.secorp.com (205.217.32.204::mail daemon; unverified,SLmailFW V2.0); Tue, 22 Oct 1996 16:10:01 PDT Received: from www.ccl.net (www.ccl.net [128.146.36.5]) by emf.emf.net (EMF-K/K) with ESMTP id PAA21044 for ; Tue, 22 Oct 1996 15:53:25 -0700 Received: for chemistry-request@www.ccl.net by www.ccl.net (8.8.0/950822.1) id SAA10292; Tue, 22 Oct 1996 18:22:56 -0400 (EDT) Received: from mail.missouri.edu for chemtw@showme.missouri.edu by www.ccl.net (8.8.0/950822.1) id SAA10179; Tue, 22 Oct 1996 18:08:49 -0400 (EDT) Received: from black.missouri.edu (black.missouri.edu [128.206.2.222]) by mail.missouri.edu (8.7.3/8.7.3) with ESMTP id RAA91764 for ; Tue, 22 Oct 1996 17:08:37 -0500 Received: from localhost (chemtw@localhost) by black.missouri.edu (8.7.3/8.7.3) with SMTP id RAA40872 for ; Tue, 22 Oct 1996 17:08:47 -0500 X-Authentication-Warning: black.missouri.edu: chemtw owned process doing -bs Date: Tue, 22 Oct 1996 17:08:46 -0500 (CDT) From: Troy Wymore X-Sender: chemtw@black.missouri.edu To: chemistry@www.ccl.net Subject: CCL:Running CHARMM in Parallel on Power Challenge Message-ID: Sender: Computational Chemistry List Errors-To: ccl@www.ccl.net Precedence: bulk X-UIDL: c1fed71609fa05be13b1f1d105a92a31 Dear CCLers, I am having some problem getting CHARMM to run in parallel on a SGI Power Challenge L. Is there anyone who has compiled and ran a job on the SGI Power Challenge? If so maybe you could tell me what you did and I could see if our procedure was different. Of course we followed the directions in the install.doc but I am getting this error message when I run my job. libpvm [pid17348] : mksocs() connect: Connection refused libpvm [pid17348] : pvm_mytid() : Can't contact local daemon So it looks as though something is incompatible with PVM. Any help would be much appreciated. ********************************************************************* Troy Wymore e-mail: chemtw@showme.missouri.edu * Department of Chemistry http://www.showme.missouri.edu/ * University of Missouri-Columbia /~chemtw/troy.html * Columbia, MO 65211 * * * ********************************************************************* -------This is added Automatically by the Software-------- -- Original Sender Envelope Address: chemtw@showme.missouri.edu -- Original Sender From: Address: chemtw@showme.missouri.edu CHEMISTRY@www.ccl.net: Everybody | CHEMISTRY-REQUEST@www.ccl.net: Coordinator MAILSERV@www.ccl.net: HELP CHEMISTRY or HELP SEARCH | Gopher: www.ccl.net 73 Anon. ftp: www.ccl.net | CHEMISTRY-SEARCH@www.ccl.net -- archive search Web: http://www.ccl.net/chemistry.html From mac@elara.tripos.com Tue Oct 22 21:16:33 1996 Received: from mail.secorp.com for mac@elara.tripos.com by www.ccl.net (8.8.0/950822.1) id VAA11077; Tue, 22 Oct 1996 21:04:06 -0400 (EDT) Received: by mail.secorp.com from localhost (router,SLmailFW V2.0); Tue, 22 Oct 1996 18:05:59 PDT Received: by mail.secorp.com from mail.secorp.com (205.217.32.204::mail daemon; unverified,SLmailFW V2.0); Tue, 22 Oct 1996 18:05:58 PDT Received: from www.ccl.net (www.ccl.net [128.146.36.5]) by emf.emf.net (EMF-K/K) with ESMTP id RAA26761 for ; Tue, 22 Oct 1996 17:35:56 -0700 Received: for chemistry-request@www.ccl.net by www.ccl.net (8.8.0/950822.1) id UAA10897; Tue, 22 Oct 1996 20:20:34 -0400 (EDT) Received: from gatekeeper.tripos.com for mac@elara.tripos.com by www.ccl.net (8.8.0/950822.1) id TAA10709; Tue, 22 Oct 1996 19:38:16 -0400 (EDT) Received: (from news@localhost) by gatekeeper.tripos.com (8.6.12/8.6.12) id SAA18320 for <@gatekeeper.tripos.com:chemistry@www.ccl.net>; Tue, 22 Oct 1996 18:39:00 -0500 Received: from elara.tripos.com(192.160.145.60) by gatekeeper.tripos.com via smap (V1.3) id sma018318; Tue Oct 22 18:38:55 1996 Received: from neptune by elara via ESMTP (951211.SGI.8.6.12.PATCH1042/951211.SGI.AUTO) for id SAA26446; Tue, 22 Oct 1996 18:38:14 -0500 From: mac@elara.tripos.com (Malcolm Cline) Received: by neptune (951211.SGI.8.6.12.PATCH1042) id XAA08245; Tue, 22 Oct 1996 23:38:12 GMT Date: Tue, 22 Oct 1996 23:38:12 GMT Message-Id: <199610222338.XAA08245@neptune> To: chemistry@www.ccl.net Subject: CCL:Chemistry on the WWW Sender: Computational Chemistry List Errors-To: ccl@www.ccl.net Precedence: bulk X-UIDL: 801934ece724b4721a20deeb8cf06b72 Intranet solutions for chemical discovery can play a critical role in your research. Check out the applets and servers available in the Discovery.Net club, available from Tripos, Inc. These servers and applets have been developed while creating our own web applications, so they are tools with proven usefulness-- the kind you will need in your drug discovery solutions. Find our more about the Discovery.Net Club by going to the Tripos home page at URL http://www.tripos.com/. mac Malcolm A. Cline WebMaster Tripos, Inc. -------This is added Automatically by the Software-------- -- Original Sender Envelope Address: mac@elara.tripos.com -- Original Sender From: Address: mac@elara.tripos.com CHEMISTRY@www.ccl.net: Everybody | CHEMISTRY-REQUEST@www.ccl.net: Coordinator MAILSERV@www.ccl.net: HELP CHEMISTRY or HELP SEARCH | Gopher: www.ccl.net 73 Anon. ftp: www.ccl.net | CHEMISTRY-SEARCH@www.ccl.net -- archive search Web: http://www.ccl.net/chemistry.html