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Study of parallel programming models on computer clusters with Intel MIC coprocessors

机译:使用Intel MIC协处理器的计算机集群上的并行编程模型研究

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摘要

Coprocessors based on the Intel Many Integrated Core (MIC) Architecture have been adopted in many high-performance computer clusters. Typical parallel programming models, such as MPI and OpenMP, are supported on MIC processors to achieve the parallelism. In this work, we conduct a detailed study on the performance and scalability of the MIC processors under different programming models using the Beacon computer cluster. Our findings are as follows. (1) The native MPI programming model on the MIC processors is typically better than the offload programming model, which offloads the workload to MIC cores using OpenMP. (2) On top of the native MPI programming model, multithreading inside each MPI process can further improve the performance for parallel applications on computer clusters with MIC coprocessors. (3) Given a fixed number of MPI processes, it is a good strategy to schedule these MPI processes to as few MIC processors as possible to reduce the cross-processor communication overhead. (4) The hybrid MPI programming model, in which data processing is distributed to both MIC cores and CPU cores, can outperform the native MPI programming model.
机译:在许多高性能计算机集群中都采用了基于Intel Many Integrated Core(MIC)架构的协处理器。 MIC处理器支持典型的并行编程模型,例如MPI和OpenMP,以实现并行性。在这项工作中,我们使用Beacon计算机集群对不同编程模型下MIC处理器的性能和可扩展性进行了详细研究。我们的发现如下。 (1)MIC处理器上的本机MPI编程模型通常比分流编程模型更好,分流编程模型使用OpenMP将工作负载分流到MIC内核。 (2)在本机MPI编程模型的基础上,每个MPI进程内部的多线程可以进一步提高具有MIC协处理器的计算机集群上并行应用程序的性能。 (3)给定固定数量的MPI进程,将这些MPI进程调度到尽可能少的MIC处理器是一个很好的策略,以减少跨处理器通信开销。 (4)混合MPI编程模型的性能优于本地MPI编程模型,在该模型中,数据处理同时分配给MIC内核和CPU内核。

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