多网格代码的目标特定细化

Richard Membarth, P. Slusallek, M. Köster, Roland Leißa, Sebastian Hack
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引用次数: 3

摘要

本文应用部分求值方法将模板代码领域特定语言(DSL)过渡到函数式命令式编程语言。特定于平台的原语(如调度或向量化)和算法变体(如边界处理)被分解到组成该DSL元素的库中。我们将展示部分求值如何消除这种关注点分离的所有开销,并创建类似于为特定目标平台手工制作版本的代码。我们通过实现v循环多网格迭代的DSL来评估我们的技术。我们的方法为AMD和NVIDIA gpu(通过SPIR和NVVM)以及使用AVX/AVX2的cpu从相同的高级DSL程序生成代码。首先,通过向量化多网格组件,我们在CPU上实现了高达3倍的加速;通过合并多网格组件的计算,我们在GPU上实现了高达2倍的加速。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Target-Specific Refinement of Multigrid Codes
This paper applies partial evaluation to stage a stencil code Domain-Specific Language (DSL) onto a functional and imperative programming language. Platform-specific primitives such as scheduling or vectorization, and algorithmic variants such as boundary handling are factored out into a library that make up the elements of that DSL. We show how partial evaluation can eliminate all overhead of this separation of concerns and creates code that resembles hand-crafted versions for a particular target platform. We evaluate our technique by implementing a DSL for the V-cycle multigrid iteration. Our approach generates code for AMD and NVIDIA GPUs (via SPIR and NVVM) as well as for CPUs using AVX/AVX2 alike from the same high-level DSL program. First results show that we achieve a speedup of up to 3x on the CPU by vectorizing multigrid components and a speedup of up to 2x on the GPU by merging the computation of multigrid components.
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