避免通信的广义最小残差法在多核心平台上的陀螺动力学五维欧拉码中的应用

Y. Idomura, Takuya Ina, Akie Mayumi, S. Yamada, Kazuya Matsumoto, Y. Asahi, Toshiyuki Imamura
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引用次数: 7

摘要

将避免通信的广义最小残差(CA-GMRES)方法应用于陀螺动力学环面五维欧拉码GT5D,并在JAEA ICEX (Haswell)、等离子体模拟器(FX100)和Oakforest-PACS (KNL)上采用广义共轭残差(GCR)方法与原始码进行性能比较。CA-GMRES方法虽然大大减少了数据约简通信的数量,但与GCR方法相比,计算量大大增加。为了解决这个问题,我们提出了一种改进的CA- gmres方法,该方法在保持与原始CA- gmres方法相同的CA属性的情况下,将计算量和内存访问减少了约30%。改进的CA-GMRES算法的算法强度比GCR算法高3.8倍,适用于未来内存和网络带宽有限的超大规模架构。CA- gmres求解器使用混合CA方法实现,其中我们将CA应用于数据缩减通信,并使用通信重叠进行halo数据通信,并对KNL上的分布式缓存进行了高度优化。结果表明,与GCR求解器相比,其计算内核速度提高了1.47 ~ 2.39倍,1280个节点的数据约简通信成本从总成本的5% ~ 13%降低到1%。
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Application of a communication-avoiding generalized minimal residual method to a gyrokinetic five dimensional eulerian code on many core platforms
A communication-avoiding generalized minimal residual (CA-GMRES) method is applied to the gyrokinetic toroidal five dimensional Eulerian code GT5D, and its performance is compared against the original code with a generalized conjugate residual (GCR) method on the JAEA ICEX (Haswell), the Plasma Simulator (FX100), and the Oakforest-PACS (KNL). Although the CA-GMRES method dramatically reduces the number of data reduction communications, computation is largely increased compared with the GCR method. To resolve this issue, we propose a modified CA-GMRES method, which reduces both computation and memory access by ~ 30% with keeping the same CA property as the original CA-GMRES method. The modified CA-GMRES method has ~ 3.8X higher arithmetic intensity than the GCR method, and thus, is suitable for future Exa-scale architectures with limited memory and network bandwidths. The CA-GMRES solver is implemented using a hybrid CA approach, in which we apply CA to data reduction communications and use communication overlap for halo data communications, and is highly optimized for distributed caches on KNL. It is shown that compared with the GCR solver, its computing kernels are accelerated by 1.47X ~ 2.39X, and the cost of data reduction communication is reduced from 5% ~ 13% to ~ 1% of the total cost at 1,280 nodes.
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