基于仿真的MPI应用优化和敏感性分析：可变性很重要
Simulation-based Optimization and Sensibility Analysis of MPI Applications: Variability Matters
摘要：
微调MPI应用程序和理解关键参数（进程数量、粒度、集体操作算法、虚拟拓扑和进程布局）的影响对于在超级计算机上获得良好性能至关重要。由于大规模运行应用程序的高消耗，仅为了优化其性能而这样做的成本特别高。对预期的性能进行廉价但可靠的预测对研究人员和系统管理员来说是一个很大的帮助。我们提出的方法学通过模拟应用程序和跳过代码的常规非MPI部分，将平台的复杂性与自适应应用程序的复杂性相结合，平台的复杂性是通过其主要组件（MPI通信，BLAS操作）的性能的统计模型捕获的。我们证明了我们的方法与高性能Linpack（HPL），用于排名超级计算机在top500，这需要仔细调整的基准的能力。我们简要介绍（1）如何对HPL的开源版本进行轻微修改，以允许在一台商用服务器上以一台小型计算机的规模进行快速模拟。然后，我们提出了（2）一个广泛的（在）验证研究，比较模拟与真实的实验，并证明我们的能力，预测性能的HPL在百分之几一致。这项研究使我们能够识别主要的建模陷阱（例如，空间和时间节点可变性或网络异质性和不规则行为）。最后，我们展示了（3）我们的“代理”如何允许研究几个微妙的HPL参数优化问题，同时考虑平台上的不确定性。
英文摘要：
Finely tuning MPI applications and understanding the influence of keyparameters (number of processes, granularity, collective operationalgorithms, virtual topology, and process placement) is critical toobtain good performance on supercomputers. With the high consumptionof running applications at scale, doing so solely to optimize theirperformance is particularly costly. Havinginexpensive but faithful predictions of expected performance could bea great help for researchers and system administrators. Themethodology we propose decouples the complexity of the platform, whichis captured through statistical models of the performance of its maincomponents (MPI communications, BLAS operations), from the complexityof adaptive applications by emulating the application and skippingregular non-MPI parts of the code. We demonstrate the capability of our method with High-PerformanceLinpack (HPL), the benchmark used to rank supercomputers in theTOP500, which requires careful tuning. We briefly present (1) how theopen-source version of HPL can be slightly modified to allow a fastemulation on a single commodity server at the scale of asupercomputer. Then we present (2) an extensive (in)validation studythat compares simulation with real experiments and demonstrates our ability to predict theperformance of HPL within a few percent consistently. This study allows us toidentify the main modeling pitfalls (e.g., spatial and temporal nodevariability or network heterogeneity and irregular behavior) that needto be considered. Last, we show (3) how our "surrogate" allowsstudying several subtle HPL parameter optimization problems whileaccounting for uncertainty on the platform.
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