Countdown Slack: A Run-Time Library to Reduce Energy Footprint in Large-Scale MPI Applications
Countdown Slack: A Run-Time Library to Reduce Energy Footprint in Large-Scale MPI Applications
复制标题
Countdown Slack：用于减少大规模 MPI 应用中能源足迹的运行时库
DOI：
10.1109/tpds.2020.3000418
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发表时间：
2019
影响因子：
5.3
通讯作者：
L. Benini
中科院分区：
文献类型：
--
作者：
Daniel Cesarini;Andrea Bartolini;Andrea Borghesi;C. Cavazzoni;M. Luisier;L. Benini
超级计算机的功耗是系统所有者、用户和社会面临的重大挑战。它限制了系统安装的容量，需要大型冷却基础设施，并且是导致大量碳足迹的原因。在不改变应用程序源代码或增加完成时间的情况下降低应用程序执行期间的功率在现实生活中的高性能计算场景中是非常期望的。在过去十年中提出的电源管理运行时框架是基于这样的假设，即MPI应用程序中的通信和应用阶段的持续时间可以预测并在运行时使用，以权衡通信松弛与功耗。在这篇文章中，我们首先表明，这种假设过于笼统，会导致错误预测，减缓应用程序，从而危及声称的好处。然后，我们提出了一种新方法，该方法基于（i）MPI调用期间通信阶段和间隙的分离，以及（ii）用于科普硬件电源管理延迟的超时算法，这两种方法共同使得在MPI应用程序中实现性能中立的节能成为可能，而不需要劳动密集型和有风险的应用程序源代码修改。我们在一级生产环境中验证了我们的方法，该环境具有广泛采用的科学应用程序。我们的方法的完成时间开销低于1%，同时它成功地利用了通信阶段的松弛，实现了10%的平均节能。如果我们专注于一个大规模的应用程序运行，所提出的方法实现了22%的节能，只有0.4%的开销。相对于最先进的方法，COUNTDOWN Slack是唯一一种总是以可忽略不计的开销（<3%）节省能源的方法。
The power consumption of supercomputers is a major challenge for system owners, users, and society. It limits the capacity of system installations, it requires large cooling infrastructures, and it is the cause of a large carbon footprint. Reducing power during application execution without changing the application source code or increasing time-to-completion is highly desirable in real-life high-performance computing scenarios. The power management run-time frameworks proposed in the last decade are based on the assumption that the duration of communication and application phases in an MPI application can be predicted and used at run-time to trade-off communication slack with power consumption. In this article, we first show that this assumption is too general and leads to mispredictions, slowing down applications, thereby jeopardizing the claimed benefits. We then propose a new approach based on (i) the separation of communication phases and slack during MPI calls and (ii) a timeout algorithm to cope with the hardware power management latency, which jointly makes it possible to achieve performance-neutral power saving in MPI applications without requiring labor-intensive and risky application source code modifications. We validate our approach in a tier-1 production environment with widely adopted scientific applications. Our approach has a time-to-completion overhead lower than 1 percent, while it successfully exploits slack in communication phases to achieve an average energy saving of 10 percent. If we focus on a large-scale application runs, the proposed approach achieves 22 percent energy saving with an overhead of only 0.4 percent. With respect to state-of-the-art approaches, COUNTDOWN Slack is the only that always leads to an energy saving with negligible overhead (<3 percent).