This work summarizes the results of a set of executions completed on three fat-tree network supercomputers: Stampede at TACC (USA), Helios at IFERC (Japan) and Eagle at PSNC (Poland). Three MPI-based, communication-intensive scientific applications compiled for CPUs have been executed under weak-scaling tests: the molecular dynamics solver LAMMPS; the finite element-based mini-kernel miniFE of NERSC (USA); and the three-dimensional fast Fourier transform mini-kernel bigFFT of LLNL (USA). The design of the experiments focuses on the sensitivity of the applications to rather different patterns of task location, to assess the impact on the cluster performance. The accomplished weak-scaling tests stress the effect of the MPI-based application mappings (concentrated vs. distributed patterns of MPI tasks over the nodes) on the cluster. Results reveal that highly distributed task patterns may imply a much larger execution time in scale, when several hundreds or thousands of MPI tasks are involved in the experiments. Such a characterization serves users to carry out further, more efficient executions. Also researchers may use these experiments to improve their scalability simulators. In addition, these results are useful from the clusters administration standpoint since tasks mapping has an impact on the cluster throughput.

中文翻译：

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执行通信密集型并行算法时的性能下降

这项工作总结了在三台胖树网络超级计算机上完成的一组执行结果：TACC（美国）的 Stampede、IFERC（日本）的 Helios 和 PSNC（波兰）的 Eagle。为 CPU 编译的三个基于 MPI 的通信密集型科学应用程序已在弱缩放测试下执行：分子动力学求解器 LAMMPS；NERSC（美国）基于有限元的mini-kernel miniFE；以及LLNL（美国）的三维快速傅里叶变换小核bigFFT。实验的设计侧重于应用程序对相当不同的任务定位模式的敏感性，以评估对集群性能的影响。已完成的弱扩展测试强调了基于 MPI 的应用程序映射（节点上 MPI 任务的集中与分布式模式）对集群的影响。结果表明，当实验中涉及数百或数千个 MPI 任务时，高度分布式的任务模式可能意味着更大的执行时间。这种表征服务于用户进行进一步、更有效的执行。研究人员也可以使用这些实验来改进他们的可扩展性模拟器。此外，这些结果从集群管理的角度来看很有用，因为任务映射对集群吞吐量有影响。