Exascale computing systems (ECS) are anticipated to perform at Exaflop speed (10 ¹⁸ operations per second) using power consumption <20 MW. This ultrascale performance requires the speedup in the system by thousand-fold enhancement in current Petascale. For future high-performance computing (HPC), power consumption is one of the vital challenges faced to achieve Exaflops through the traditional way of increasing clock-speed. One standard way to attain such significant performance is through massive parallelism. In the early stages, it is hard to decide the promising parallel programming approach that can provide massive parallelism to attain ExaFlops. This article commences with a short description and implementation of algorithms of various hybrid parallel programming models (PPMs) for homogeneous and heterogeneous cluster systems. Furthermore, the authors evaluated performance and power consumption in these hybrid models by implementing in two HPC benchmarking applications such as square matrix multiplication and Jacobi iterative solver for two-dimensional Laplace equation. The results demonstrated that the hybrid of heterogeneous (MPI + X ) outperformed to homogeneous parallel programming (MPI + OpenMP) model. This empirical investigation of hybrid PPMs is a leading step for researchers and development communities to select a promising model for emerging ECS.

中文翻译：

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实证研究：百亿亿次计算系统的基于性能和功耗的双层模型

百亿亿次计算系统（ECS）预计将以百亿美元的速度运行（10 ¹⁸耗电量<20 MW。这种超大规模性能需要通过当前Petascale的数千倍增强来加快系统速度。对于未来的高性能计算（HPC），功耗是通过传统的提高时钟速度的方法来实现Exaflop所面临的重大挑战之一。获得如此出色性能的一种标准方法是通过大规模并行处理。在早期阶段，很难确定有前途的并行编程方法，该方法可以提供大量的并行性来获得ExaFlops。本文首先简要介绍并介绍了适用于同类和异构集群系统的各种混合并行编程模型（PPM）的算法。此外，作者通过在两个HPC基准测试应用程序（例如，平方矩阵乘法和用于二维Laplace方程的Jacobi迭代求解器）中实现的方法，评估了这些混合模型的性能和功耗。结果表明，杂种（MPI +X ）的性能优于同类并行编程（MPI + OpenMP）模型。混合PPM的这一经验研究是研究人员和开发社区为新兴的ECS选择有希望的模型的第一步。