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Topology-aware task mapping for reducing communication contention on large parallel machines

机译:拓扑感知任务映射,可减少大型并行机上的通信争用

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Communication latencies constitute a significant factor in the performance of parallel applications. With techniques such as wormhole routing, the variation in no-load latencies became insignificant, i.e., the no-load latencies for far-away processors were not significantly higher (and too small to matter) than those for nearby processors. Contention in the network is then left as the major factor affecting latencies. With networks such as fat-trees of hypercubes, with number of wires growing as P log P, even this is not a very significant factor. However, for torus and grid networks now being used in large machines such as BlueGene/L and the Cray XT3, such contention becomes an issue. We quantify the effect of this contention with benchmarks that vary the number of hops traveled by each communicated byte. We then demonstrate a process mapping strategy that minimizes the impact of topology by heuristically minimizing the total number of hop-bytes communicated. This strategy, and its variants, are implemented in an adaptive runtime system in Charm++ and adaptive MPI, so it is available for a broad class of applications.
机译:通信延迟是并行应用程序性能的重要因素。通过诸如虫孔路由之类的技术,空载等待时间的变化变得微不足道,即,远处处理器的空载等待时间不会比附近处理器的空载等待时间显着更高(并且太小而无足轻重)。然后,网络中的争用将成为影响延迟的主要因素。对于诸如超立方体的胖树之类的网络,并且导线的数量以P log P的形式增长,即使这不是一个非常重要的因素。但是,对于如今在大型计算机(例如BlueGene / L和Cray XT3)中使用的环面和网格网络,这种争执成为一个问题。我们使用基准来量化此争用的效果,这些基准会改变每个通信字节传输的跃点数。然后,我们演示了一种进程映射策略,该策略通过启发式地最小化通信的跳字节总数来最小化拓扑的影响。该策略及其变体在Charm ++和自适应MPI的自适应运行时系统中实现,因此可用于广泛的应用程序。

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