蓝色基因/L超级计算机上全对全通信的优化

Sameer Kumar, Yogish Sabharwal, R. Garg, P. Heidelberger
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引用次数: 70

摘要

对于许多应用程序(例如使用快速傅里叶变换(FFT)算法的应用程序)来说,全对全通信是一个众所周知的性能瓶颈。我们分析了BlueGene/L环面互连中存在链路争用的全对全通信性能,即使是短消息的全对全操作。我们观察到,全对全的性能取决于处理器分区的形状。我们对各种形状的分区进行了全对全的性能分析。然后,我们提出了优化方案,大大提高了短消息和大消息的所有对所有的性能。特别是,在Lawrence Livermore National Lab的65,536(64乘以32乘以32)节点Blue Gene/L机器上,吞吐量从峰值的64%提高到99%以上。我们在1-D和3-D FFT基准测试中展示了所有对所有性能优化的影响。通过优化的全对全,我们在HPC挑战1D FFT基准测试中实现了超过2.8 TF的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimization of All-to-All Communication on the Blue Gene/L Supercomputer
All-to-all communication is a well known performance bottleneck for many applications, such as the ones that use the Fast-Fourier-transform (FFT) algorithm. We analyze the performance of all-to-all communication on the BlueGene/L torus interconnect that has link contention even for all-to-all operations with short messages. We observed that the performance of all-to-all depends on the shape of the processor partition. We present a performance analysis of all-to-all on partitions of various shapes. We then present optimization schemes that substantially improve the performance of all-to-all with short and large messages.In particular, throughput improved from 64% to over 99% of peak on the 65,536 (64 times 32 times 32) node Blue Gene/L machine at the Lawrence Livermore National Lab. We show the impact of the all-to-all performance optimizations in 1-D and 3-D FFT benchmarks. We achieved a performance of over 2.8 TF for the HPC Challenge 1D FFT benchmark with our optimized all-to-all.
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