A large-scale cross-architecture evaluation of thread-coarsening
A large-scale cross-architecture evaluation of thread-coarsening
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DOI：
10.1145/2503210.2503268
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发表时间：
2013-11
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通讯作者：
A. Magni;Christophe Dubach;M. O’Boyle
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文献类型：
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作者：
A. Magni;Christophe Dubach;M. O’Boyle
OpenCL已经成为当今高性能超级计算机中并行设备事实上的数据并行编程模型。OpenCL的设计目标是保证程序在不同厂商的硬件之间的可移植性。然而，实现良好的性能是困难的，需要手动调整程序和每个目标设备的专业知识。在本文中，我们考虑了一种数据并行编译器转换-线程粗化-并通过开发基于LLVM的源代码到源代码的OpenCL编译器来评估其在一系列设备上的效果。我们在17个基准和5个具有不同粗化参数的平台上进行了43,000多个不同的实验，对这种转换进行了彻底的评估。我们在单个应用程序上实现了超过9倍的加速，平均加速范围从Nvidia Kepler GPU的1.15倍到AMD Cypress GPU的1.50倍。最后，我们使用统计回归来分析和解释基于硬件的性能计数器方面的程序性能。
OpenCL has become the de-facto data parallel programming model for parallel devices in today's high-performance supercomputers. OpenCL was designed with the goal of guaranteeing program portability across hardware from different vendors. However, achieving good performance is hard, requiring manual tuning of the program and expert knowledge of each target device. In this paper we consider a data parallel compiler transformation - thread-coarsening - and evaluate its effects across a range of devices by developing a source-to-source OpenCL compiler based on LLVM. We thoroughly evaluate this transformation on 17 benchmarks and five platforms with different coarsening parameters giving over 43,000 different experiments. We achieve speedups over 9x on individual applications and average speedups ranging from 1.15x on the Nvidia Kepler GPU to 1.50x on the AMD Cypress GPU. Finally, we use statistical regression to analyse and explain program performance in terms of hardware-based performance counters.