A genetic algorithms approach to modeling the performance of memory-bound computations
A genetic algorithms approach to modeling the performance of memory-bound computations
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对内存限制计算性能进行建模的遗传算法方法
DOI：
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发表时间：
2007
期刊：
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通讯作者：
A. Snavely
中科院分区：
文献类型：
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作者：
M. Tikir;L. Carrington;Erich Strohmaier;A. Snavely
衡量内存带宽的基准测试，如STREAM、Apex-map和MultiMAPS，由于现代处理器的“冯·诺伊曼”瓶颈而越来越受欢迎，这导致许多计算受到内存的限制。我们提出了一种基于这些基准测试的结果来预测高性能计算应用程序性能的方案。使用遗传算法方法来学习作为每台机器缓存命中率的函数的带宽，并使用MultiMAPS作为适应性测试。具体结果是56个单独的性能预测，包括在5个不同的现代HPC架构上运行的3个完整规模的并行应用程序，具有不同的CPU计数和输入，预测相对于独立验证的运行时的平均差异在10%以内。
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.