Subject: Commodity Cluster Computing for Computational Chemistry

From: Eugene Leitl <[email-masked]>
Date: Mon, 14 Feb 2000 19:59:18 -0800 (PST)

DHPC Technical Report DHPC-073: Commodity Cluster Computing for
Computational Chemistry

http://dhpc.adelaide.edu.au/reports/073/html/dhpc-073.html

K.A. Hawick1, D.A. Grove1, P.D. Coddington1, M.A. Buntine2 

1 Department of Computer Science, University of Adelaide, Adelaide, SA
5005, Australia
  
2 Department of Chemistry, University of Adelaide, Adelaide, SA 5005,
Australia

[email-masked], [email-masked] 

                       21 January 2000 

Keywords: Computational chemistry, Beowulf cluster, cluster computing,
parallel computing, performance benchmarks

Outline:

     Abstract 
     1. Introduction 
     2. Beowulf Clusters 
     3. System Requirements for Computational Chemistry 
     4. Designing a Beowulf Cluster 
         4.1. Processor Architecture 
         4.2. Network Infrastructure 
         4.3. Disk Configuration 
         4.4. Operating System 
         4.5. Cluster Management Software 
         4.6. Other Issues 
     5. Performance Benchmarks 
     6. Conclusions 
     Acknowledgments 
     References 

Abstract:

Access to high-performance computing power remains crucial for many
computational chemistry problems. Unfortunately, traditional
supercomputers or cluster computing solutions from commercial vendors
remain very expensive, even for entry level configurations, and are
therefore often beyond the reach of many small to medium-sized
research groups and universities. Clusters of networked commodity
computers provide an alternative computing platform that can offer
substantially better price/performance than commercial
supercomputers. We have constructed a networked PC cluster, or
Beowulf, dedicated to computational chemistry problems using standard
ab initio molecular orbital software packages such as Gaussian and
GAMESS-US.

This paper introduces the concept of Beowulf computing clusters and
outlines the requirements for running the ab initio software packages
used by computational chemists at the University of Adelaide. We
describe the economic and performance trade-offs and design choices
made in constructing the Beowulf system, including the choice of
processors, networking, storage systems, operating system and job
queuing software. Other issues such as throughput, scalability,
software support, maintenance, and future trends are also discussed.

We present some benchmark results for the Gaussian 98 and GAMESS-US
programs, in order to compare the processor performance (and
price/performance) with other computing platforms. We also analyse the
efficiency and scalability of the parallel versions of these programs
on a commodity Beowulf cluster. We believe that the Beowulf cluster we
have constructed offers the best price/performance ratio for our
computational chemistry applications, and that commodity clusters can
now provide dedicated supercomputer performance within the budget of
most university departments.

[ rest of paper snipped for brevity. consult above URL for the whole hog ]