Characterization of MPI Usage on a Production Supercomputer
Characterization of MPI Usage on a Production Supercomputer
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DOI：
10.1109/sc.2018.00033
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发表时间：
2018-11
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影响因子：
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通讯作者：
Sudheer Chunduri;Scott Parker;P. Balaji;K. Harms;Kalyan Kumaran
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文献类型：
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作者：
Sudheer Chunduri;Scott Parker;P. Balaji;K. Harms;Kalyan Kumaran
MPI是当今科学计算中使用的最突出的编程模型。但是，尽管MPI很重要，但是科学计算应用如何在生产中使用它。缺乏理解主要归因于这样一个事实，即生产系统通常要纳入自动分析工具，因为人们担心潜在的性能额头。在这项研究中，我们使用了称为Autoperf的轻质分析工具来记录大型IBM BG/Q超级计算系统（MIRA）及其相应开发系统（CETUS）上生产应用的MPI使用特性。 Autoperf限制了它记录的信息量，以便将开销保持在最低限度，同时仍存储足够的数据以获取有用的见解。已收集了MPI使用统计数据，用于在两年内经营的100,000多个工作，并在本文中进行了分析。该分析旨在为MPI开发人员和网络硬件开发人员提供有用的见解，以提供下一代改进，并为他们的下一个系统采购提供超级计算中心操作员。
MPI is the most prominent programming model used in scientific computing today. Despite the importance of MPI, however, how scientific computing applications use it in production is not well understood. This lack of understanding is attributed primarily to the fact that production systems are often wary of incorporating automatic profiling tools that perform such analysis because of concerns about potential performance over-heads. In this study, we used a lightweight profiling tool, called Autoperf, to log the MPI usage characteristics of production applications on a large IBM BG/Q supercomputing system (Mira) and its corresponding development system (Cetus). Autoperf limits the amount of information that it records, in order to keep the overhead to a minimum while still storing enough data to derive useful insights. MPI usage statistics have been collected for over 100K jobs that were run within a two-year period and are analyzed in this paper. The analysis is intended to provide useful insights for MPI developers and network hardware developers for their next generation of improvements and for supercomputing center operators for their next system procurements.