Converting data-parallelism to task-parallelism by rewrites: purely functional programs across multiple GPUs

Bo Joel Svensson, Michael Vollmer, Eric Holk, T. L. McDonell, Ryan Newton
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引用次数: 3

Abstract

High-level domain-specific languages for array processing on the GPU are increasingly common, but they typically only run on a single GPU. As computational power is distributed across more devices, languages must target multiple devices simultaneously. To this end, we present a compositional translation that fissions data-parallel programs in the Accelerate language, allowing subsequent compiler and runtime stages to map computations onto multiple devices for improved performance---even programs that begin as a single data-parallel kernel.
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通过重写将数据并行性转换为任务并行性:跨多个gpu的纯功能程序
用于GPU上的数组处理的高级领域特定语言越来越普遍,但它们通常只在单个GPU上运行。随着计算能力分布在更多的设备上,语言必须同时针对多个设备。为此,我们提出了一种组合转换,它在Accelerate语言中分解数据并行程序,允许随后的编译器和运行时阶段将计算映射到多个设备上以提高性能——甚至是作为单个数据并行内核开始的程序。
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