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- W2021993413 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%." @default.
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- W2021993413 date "2013-01-01" @default.
- W2021993413 modified "2023-10-18" @default.
- W2021993413 title "High-level Programming for Medical Imaging on Multi-GPU Systems Using the SkelCL Library" @default.
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- W2021993413 doi "https://doi.org/10.1016/j.procs.2013.05.239" @default.
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