Task Bench: A Parameterized Benchmark for Evaluating Parallel Runtime Performance-猫眼课题宝

DOI：

10.1109/sc41405.2020.00066

发表时间：

2019-08

期刊：

SC20: International Conference for High Performance Computing, Networking, Storage and Analysis

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其他

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作者：

Elliott Slaughter;Wei Wu;Yuankun Fu;Legend Brandenburg;N. Garcia;Wilhem Kautz;Emily Marx;Kaleb S. Morris;Wonchan Lee;Qinglei Cao;G. Bosilca;S. Mirchandaney;Sean Treichler;P. McCormick;A. Aiken

我们提出Task Bench，这是一个参数化基准，旨在探索分布式编程系统在各种应用场景下的性能。Task Bench通过使给定系统的实现与基准测试本身正交，极大地降低了基准测试和比较多个编程系统的障碍：用Task Bench构建的每个基准测试都运行在每个Task Bench实现上。此外，Task Bench的参数化支持各种各样的基准测试场景，这些场景可以提炼出大型应用程序的关键特征。为了评估被测试系统的有效性和开销，我们引入了一个新的度量，最小有效任务粒度（METG）。我们在Cori超级计算机的多达256个Haswell节点上对15个编程系统进行了全面的研究。在规模上运行，100$\mu$s长的任务是当前技术下任何系统有效运行的最细粒度。我们还研究了每个系统的可扩展性、隐藏通信和减轻负载不平衡的能力。

We present Task Bench, a parameterized benchmark designed to explore the performance of distributed programming systems under a variety of application scenarios. Task Bench dramatically lowers the barrier to benchmarking and comparing multiple programming systems by making the implementation for a given system orthogonal to the benchmarks themselves: every benchmark constructed with Task Bench runs on every Task Bench implementation. Furthermore, Task Bench’s parameterization enables a wide variety of benchmark scenarios that distill the key characteristics of larger applications.To assess the effectiveness and overheads of the tested systems, we introduce a novel metric, minimum effective task granularity (METG). We conduct a comprehensive study with 15 programming systems on up to 256 Haswell nodes of the Cori supercomputer. Running at scale, 100$\mu$s-long tasks are the finest granularity that any system runs efficiently with current technologies. We also study each system’s scalability, ability to hide communication and mitigate load imbalance.

Task Bench: A Parameterized Benchmark for Evaluating Parallel Runtime Performance

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