基于遗传算法的内存约束计算性能建模方法

M. Tikir, L. Carrington, E. Strohmaier, A. Snavely
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引用次数: 79

摘要

由于现代处理器的“冯·诺伊曼”瓶颈导致许多计算受到内存限制,测量内存带宽的基准测试,如STREAM、Apex-MAPS和MultiMAPS,越来越受欢迎。我们提出了一种基于此类基准测试结果预测HPC应用程序性能的方案。使用遗传算法方法“学习”带宽作为每台机器缓存命中率的函数,并使用MultiMAPS作为适应度测试。具体结果是56个单独的性能预测,包括3个在5种不同的现代HPC架构上运行的全面并行应用程序,具有不同的CPU计数和输入,相对于独立验证的运行时,预测在10%的平均差异内。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A genetic algorithms approach to modeling the performance of memory-bound computations
Benchmarks that measure memory bandwidth, such as STREAM, Apex-MAPS and MultiMAPS, are increasingly popular due to the "Von Neumann" bottleneck of modern processors which causes many calculations to be memory-bound. We present a scheme for predicting the performance of HPC applications based on the results of such benchmarks. A Genetic Algorithm approach is used to "learn" bandwidth as a function of cache hit rates per machine with MultiMAPS as the fitness test. The specific results are 56 individual performance predictions including 3 full-scale parallel applications run on 5 different modern HPC architectures, with various CPU counts and inputs, predicted within 10% average difference with respect to independently verified runtimes.
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