High-level Programming for Medical Imaging on Multi-GPU Systems Using the SkelCL Library

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Abstract

Application development for modern high-performance systems with Graphics Processing Units (GPUs) relies on low-level programming approaches like CUDA and OpenCL, which leads to complex, lengthy and error-prone programs.

In this paper, we present SkelCL – a high-level programming model for systems with multiple GPUs and its implementa- tion as a library on top of OpenCL. SkelCL provides three main enhancements to the OpenCL standard: 1) computations are conveniently expressed using parallel patterns (skeletons); 2) memory management is simplified using parallel container data types; 3) an automatic data (re)distribution mechanism allows for scalability when using multi-GPU systems.

We use a real-world example from the field of medical imaging to motivate the design of our programming model and we show how application development using SkelCL is simplified without sacrificing performance: we were able to reduce the code size in our imaging example application by 50% while introducing only a moderate runtime overhead of less than 5%.

Keywords

SkelCL
Multi-GPU Computing
Algorithmic Skeletons
LM OSEM Algorithm
Image Reconstruction

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Selection and peer review under responsibility of the organizers of the 2013 International Conference on Computational Science.