Application power profiling on IBM Blue Gene/Q

The power consumption of state of the art supercomputers, because of their complexity and unpredictable workloads, is extremely difficult to estimate. Accurate and precise results, as are now possible with the latest generation of IBM Blue Gene/Q, are therefore a welcome addition to the landscape. Only recently have end users been afforded the ability to access the power consumption of their applications. However, just because it’s possible for end users to obtain this data does not mean it’s a trivial task. This emergence of new data is therefore not only understudied, but also not fully understood. In this paper, we describe our open source power profiling library called MonEQ, built on the IBM provided Environmental Monitoring (EMON) API. We show that it’s lightweight, has extremely low overhead, is incredibly flexible, and has advanced features which end users can take advantage. We then integrate MonEQ into several benchmarks and show the data it produces and what analysis of this data can teach us. Going one step further we also describe how seemingly simple changes in scale or network topology can have dramatic effects on power consumption. To this end, previously well understood applications will now have new facets of potential analysis.

由于最先进的超级计算机的复杂性和不可预测的工作负载，其功耗极难估算。因此，像最新一代IBM蓝色基因/Q现在所能做到的那样准确和精确的结果，是这一领域令人欣喜的补充。直到最近，终端用户才具备获取其应用程序功耗的能力。然而，仅仅因为终端用户有可能获取这些数据，并不意味着这是一项简单的任务。因此，这些新数据的出现不仅研究不足，而且也未被充分理解。 在本文中，我们描述了我们的开源功耗剖析库MonEQ，它基于IBM提供的环境监测（EMON）应用程序编程接口构建。我们表明它是轻量级的，具有极低的开销，极其灵活，并且具有终端用户可以利用的高级特性。然后，我们将MonEQ集成到几个基准测试中，并展示它所产生的数据以及对这些数据的分析能给我们带来什么启示。更进一步，我们还描述了规模或网络拓扑看似简单的变化如何对功耗产生巨大影响。为此，以前被充分理解的应用程序现在将有潜在分析的新方面。