Parallel Processing for Imaging Applications

Papers submitted to this conference should explore parallel computational design principles and their implementations under physical constraints for imaging applications.

Recent business and technological trends have resulted in an explosion in the availability and types of digital imagery sources, data and display systems. At the same time, the scaling rate of the computational capability of individual processing units has slowed dramatically. Instead, there has been massive growth in the total number of connected computation devices and software parallel programming environments are rapidly evolving to enable these devices to work in concert and in parallel. From the localized processors of multi-core CPUs, many-core graphics processing unit ( GPUs) and high capacity FPGA fabrics to the more widely distributed processors of map-reduce programming and cloud computing systems, designers and researchers now can target a wide range of parallel architectures and parallel programming resources to efficiently solve computationally complex imaging problems.

While physical and business constraints including power, speed, and cost are aggressively pushing the compute and imaging infrastructure towards parallel deployment, imaging is both a key consumer and driver of the evolution of this infrastructure. The data throughput requirements and degree of data dependence of imaging applications indicate a good match between parallel architectures and imaging applications; similarly, the choice of parallel architectures often reflects the structure of the imaging problem targeted by the application. This complementary relationship suggests that measures of parallel efficiency can formalize the definition of many imaging problems. Thus, it is the objective of this conference to explore the intersection of imaging and parallelism through examination of new parallel designs, design tools and theoretical statements concerning the problem of mapping imaging problems onto parallel compute fabrics.

We expect papers in this conference to combine principles and techniques for parallelism, such as:

• Cloud computing
• GPU computing
• High-level parallel programming constructs
• Design tools for extracting parallelism
• Efficient, scalable architectures
• Memory hierarchy design for parallel systems
• Metrics for parallelism and capacity planning
• Efficient algorithm mapping onto parallel hardware
• Algorithmic classification by efficient parallel architecture
• Algorithms for parallel scheduling and resource allocation

Other novel parallel programming techniques, constructs, abstractions, and implementations with an understanding of imaging applications, such as:

• Teleconferencing
• Medical imaging
• Remote sensing
• Image fusion
• Spectral imaging
• Volumetric imaging
• Compression
• Halftoning
• Color rendering
• Raster image processing
• Image analysis
• Computer vision
• Document analysis
• Forensics
• Resampling
• Computational optics
• Other novel imaging applications

Deadline for Abstracts: 11 July 2011

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