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Performance evaluation of hybrid programming patterns for large CPU/GPU heterogeneous clusters

机译：大型CPU / GPU异构集群的混合编程模式的性能评估

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摘要

The CPU/GPU heterogeneous clusters are important platforms for high performance computing applications. However, there are many challenges for efficiently performing the scientific and engineering legacy code on these heterogeneous systems. In this paper, we endeavor to address the programming-model issue by combining the existing models (i.e., MPI, OpenMP and CUDA). First, two hybrid programming patterns are presented, namely the MPI+CUDA and MPI+OpenMP/CUDA. Second, three kernels (i.e., EP, CG and MG) of the NAS parallel benchmarks (NPBs), which are abstracted from many legacy computational fluid dynamics applications, are implemented with the above two patterns. Third, these hybrid implementations are executed on the TianHe-1A supercomputer, and the corresponding experimental results show that significant performance improvement can be achieved with the above patterns. Finally, a detailed performance analysis about the two hybrid patterns is performed and some guidelines for porting the legacy code onto large-scale heterogeneous CPU/GPU clusters are also given.
机译：CPU / GPU异构集群是高性能计算应用程序的重要平台。然而，在这些异构系​​统上有效执行科学和工程遗留代码存在许多挑战。在本文中，我们努力通过结合现有模型（即MPI，OpenMP和CUDA）来解决编程模型问题。首先，提出了两种混合编程模式，即MPI + CUDA和MPI + OpenMP / CUDA。其次，使用上述两种模式实现了NAS并行基准测试（NPB）的三个内核（即EP，CG和MG），这些内核是从许多传统计算流体动力学应用程序中抽象出来的。第三，这些混合实现是在TianHe-1A超级计算机上执行的，相应的实验结果表明，使用上述模式可以显着提高性能。最后，对这两种混合模式进行了详细的性能分析，并给出了一些将遗留代码移植到大规模异构CPU / GPU集群上的准则。

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