Predicting the Energy-Consumption of MPI Applications at Scale Using Only a Single Node
Predicting the Energy-Consumption of MPI Applications at Scale Using Only a Single Node
复制标题
仅使用单个节点大规模预测 MPI 应用程序的能耗
DOI：
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发表时间：
2017
期刊：
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通讯作者：
M. Quinson
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文献类型：
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作者：
F. C. Heinrich;Tom Cornebize;A. Degomme;Arnaud Legrand;Alexandra Carpen;S. Hunold;Anne;M. Quinson
监控和评估超级计算机和数据中心的能源效率对于限制和减少其能源消耗至关重要。来自高性能计算（HPC）领域的应用（诸如MPI应用）占HPC中心消耗的总能量的很大一部分。仿真是研究这些应用程序在各种场景中的行为的流行方法，因此能够在具有成本效益的，可控的和可再现的仿真环境中研究其能耗是有利的。遗憾的是，支持HPC应用程序的模拟器通常缺乏预测能耗的能力，特别是当目标平台由多核节点组成时。在这项工作中，我们的目标是通过模拟准确预测MPI应用程序的能耗。首先，我们介绍了有意义的模拟所需的模型：目标平台的计算模型，通信模型和能量模型。其次，我们证明，通过仔细校准这些模型在一个单一的节点上，HPC应用程序在更大的规模预测的能源消耗是非常接近（在几个百分点）的真实的实验。我们进一步展示了如何将这些模型集成到SimGrid仿真工具包中。为了在多核架构上获得良好的执行时间预测，我们还确定了在模拟中正确考虑内存效应至关重要。建议的模拟器进行了验证，通过广泛的实验与知名的HPC基准。最后，我们展示了该模拟器可用于大规模研究应用程序，与真实的实验相比，这使研究人员能够节省时间和资源。
Monitoring and assessing the energy efficiency of supercomputers and data centers is crucial in order to limit and reduce their energy consumption. Applications from the domain of High Performance Computing (HPC), such as MPI applications, account for a significant fraction of the overall energy consumed by HPC centers. Simulation is a popular approach for studying the behavior of these applications in a variety of scenarios, and it is therefore advantageous to be able to study their energy consumption in a cost-efficient, controllable, and also reproducible simulation environment. Alas, simulators supporting HPC applications commonly lack the capability of predicting the energy consumption, particularly when target platforms consist of multi-core nodes. In this work, we aim to accurately predict the energy consumption of MPI applications via simulation. Firstly, we introduce the models required for meaningful simulations: The computation model, the communication model, and the energy model of the target platform. Secondly, we demonstrate that by carefully calibrating these models on a single node, the predicted energy consumption of HPC applications at a larger scale is very close (within a few percents) to real experiments. We further show how to integrate such models into the SimGrid simulation toolkit. In order to obtain good execution time predictions on multi-core architectures, we also establish that it is vital to correctly account for memory effects in simulation. The proposed simulator is validated through an extensive set of experiments with wellknown HPC benchmarks. Lastly, we show the simulator can be used to study applications at scale, which allows researchers to save both time and resources compared to real experiments.