Evaluating Multiple Streams on Heterogeneous Platforms

Jianbin Fang, Peng Zhang, Zhaokui Li, T. Tang, Xuhao Chen, Cheng Chen, Canqun Yang
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引用次数: 5

Abstract

Using multiple streams can improve the overall system performance by mitigating the data transfer overhead on heterogeneous systems. Prior work focuses a lot on GPUs but little is known about the performance impact on (Intel Xeon) Phi. In this work, we apply multiple streams into six real-world applications on Phi. We then systematically evaluate the performance benefits of using multiple streams. The evaluation work is performed at two levels: the microbenchmarking level and the real-world application level. Our experimental results at the microbenchmark level show that data transfers and kernel execution can be overlapped on Phi, while data transfers in both directions are performed in a serial manner. At the real-world application level, we show that both overlappable and non-overlappable applications can benefit from using multiple streams (with an performance improvement of up to 24%). We also quantify how task granularity and resource granularity impact the overall performance. Finally, we present a...
异构平台上的多流评估
使用多流可以通过减少异构系统上的数据传输开销来提高系统的整体性能。先前的工作主要集中在gpu上,但对(Intel Xeon) Phi的性能影响知之甚少。在这项工作中,我们将多个流应用到Phi上的六个实际应用中。然后,我们系统地评估了使用多个流的性能优势。评估工作在两个级别上执行:微基准测试级别和实际应用程序级别。我们在微基准级别的实验结果表明,数据传输和内核执行可以在Phi上重叠,而两个方向的数据传输以串行方式执行。在实际应用程序级别,我们展示了可重叠和不可重叠的应用程序都可以从使用多个流中受益(性能提高高达24%)。我们还量化了任务粒度和资源粒度如何影响整体性能。最后,我们提出……
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