新兴多核并行编程模型的评价

Matt Martineau, Simon McIntosh-Smith, M. Boulton, W. Gaudin
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引用次数: 43

摘要

在这项工作中,我们直接评估了几种新兴的并行编程模型:Kokkos, RAJA, OpenACC和OpenMP 4.0,以及成熟的CUDA和OpenCL api。每个模型都用于移植TeaLeaf,这是一个微型代理应用程序,或迷你应用程序,它可以解决热传导方程,属于Mantevo应用程序套件。我们发现,最佳性能是通过设备调优实现实现的,但在许多情况下,性能可移植模型能够在5-20%的性能损失范围内解决相同的问题。这些模型向开发人员展示了不同程度的复杂性,它们都表现出合理的性能。我们认为,复杂性将成为长期采用此类模型的主要影响因素。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Evaluation of Emerging Many-Core Parallel Programming Models
In this work we directly evaluate several emerging parallel programming models: Kokkos, RAJA, OpenACC, and OpenMP 4.0, against the mature CUDA and OpenCL APIs. Each model has been used to port TeaLeaf, a miniature proxy application, or mini-app, that solves the heat conduction equation, and belongs to the Mantevo suite of applications. We find that the best performance is achieved with device-tuned implementations but that, in many cases, the performance portable models are able to solve the same problems to within a 5-20% performance penalty. The models expose varying levels of complexity to the developer, and they all present reasonable performance. We believe that complexity will become the major influencer in the long-term adoption of such models.
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