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Performance evaluation and comparison of parallel conjugate gradient on modern multi-core accelerator and massively parallel systems

机译:现代多核加速器和大规模并行系统上并行共轭梯度的性能评估和比较

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Two parallel computer paradigms available today are multi-core accelerators such as the Sony, Toshiba and IBM Cell or Graphics Processing Unit (GPUs), and massively parallel message-passing machines such as the IBM Blue Gene (BG). The solution of systems of linear equations is one of the most central processing unit-intensive steps in engineering and simulation applications and can greatly benefit from the multitude of processing cores and vectorisation on today's parallel computers. We parallelise the conjugate gradient (CG) linear equation solver on the Cell Broadband Engine and the IBM Blue Gene/L machine. We perform a scalability analysis of CG on both machines across 1, 8 and 16 synergistic processing elements and 1-32 cores on BG with heptadiagonal matrices. The results indicate that the multi-core Cell system outperforms by three to four times the massively parallel BG system due to the Cell's higher communication bandwidth and accelerated vector processing capability.
机译:当今可用的两种并行计算机范例是:多核加速器,例如Sony,东芝和IBM Cell或图形处理单元(GPU),以及大规模并行消息传递机器,例如IBM Blue Gene(BG)。线性方程组的解决方案是工程和仿真应用程序中最集中的处理单元密集型步骤之一,并且可以从当今的并行计算机上大量的处理核心和矢量化中受益匪浅。我们在Cell Broadband Engine和IBM Blue Gene / L机器上并行化了共轭梯度(CG)线性方程求解器。我们对两台机器上的CG进行可伸缩性分析,这些机器跨越1、8和16个协同处理元件以及具有七角矩阵的BG上的1-32个内核。结果表明,由于Cell更高的通信带宽和加速的矢量处理能力,多核Cell系统的性能比大规模并行BG系统高出三到四倍。

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