分析GPU架构中内存引用的局部性

Saurabh Gupta, Ping Xiang, Huiyang Zhou
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引用次数: 9

摘要

本文提倡对GPGPU内核的内存引用进行形式化局部性分析。我们研究了在内存层次结构中不同缓存级别上引用的局部性。在L1缓存级别,我们研究了warp、线程块和流多处理器级别的局部性行为。使用矩阵乘法作为案例研究,我们表明我们的局部性分析准确地捕获了内存访问的一些有趣的和反直觉的行为。我们相信这样的分析将为理解内存访问行为和优化GPU架构中的内存层次结构提供非常有用的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analyzing locality of memory references in GPU architectures
In this paper we advocate formal locality analysis on memory references of GPGPU kernels. We investigate the locality of reference at different cache levels in the memory hierarchy. At the L1 cache level, we look into the locality behavior at the warp-, the thread block- and the streaming multiprocessor-level. Using matrix multiplication as a case study, we show that our locality analysis accurately captures some interesting and counter-intuitive behavior of the memory accesses. We believe that such analysis will provide very useful insights in understanding the memory accessing behavior and optimizing the memory hierarchy in GPU architectures.
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