James C Phillips, Yanhua Sun, Nikhil Jain, Eric J Bohm, Laxmikant V Kalé
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引用次数: 31
Abstract
Currently deployed petascale supercomputers typically use toroidal network topologies in three or more dimensions. While these networks perform well for topology-agnostic codes on a few thousand nodes, leadership machines with 20,000 nodes require topology awareness to avoid network contention for communication-intensive codes. Topology adaptation is complicated by irregular node allocation shapes and holes due to dedicated input/output nodes or hardware failure. In the context of the popular molecular dynamics program NAMD, we present methods for mapping a periodic 3-D grid of fixed-size spatial decomposition domains to 3-D Cray Gemini and 5-D IBM Blue Gene/Q toroidal networks to enable hundred-million atom full machine simulations, and to similarly partition node allocations into compact domains for smaller simulations using multiple-copy algorithms. Additional enabling techniques are discussed and performance is reported for NCSA Blue Waters, ORNL Titan, ANL Mira, TACC Stampede, and NERSC Edison.
目前部署的千万亿次超级计算机通常使用三维或多维的环形网络拓扑结构。虽然这些网络在几千个节点上对拓扑无关的代码表现良好,但拥有20000个节点的领导机器需要拓扑感知,以避免通信密集型代码的网络争用。由于输入/输出节点专用或硬件故障,导致节点分配形状不规则,存在孔洞,拓扑适应非常复杂。在流行的分子动力学程序NAMD的背景下,我们提出了将固定大小的空间分解域的周期性3d网格映射到3-D Cray Gemini和5-D IBM Blue Gene/Q环形网络的方法,以实现数亿原子的全机器模拟,并使用多副本算法将节点分配到紧凑的域以进行较小的模拟。讨论了NCSA Blue Waters、ORNL Titan、ANL Mira、TACC Stampede和NERSC Edison的其他使能技术,并报告了其性能。