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Faster Topology-aware Collective Algorithms Through Non-minimal Communication

机译:通过非最小通信实现更快的拓扑感知集体算法

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Known algorithms for two important collective communication operations, allgather and reduce-scatter, are minimal-communication algorithms; no process sends or receives more than the minimum amount of data. This, combined with the data-ordering semantics of the operations, limits the flexibility and performance of these algorithms. Our novel non-minimal, topology-aware algorithms deliver far better performance with the addition of a very small amount of redundant communication. We develop novel algorithms for Clos networks and single or multi-ported torus networks. Tests on a 32k-node BlueGene/P result in allgather speedups of up to 6× and reduce-scatter speedups of over 11× compared to the native IBM algorithm. Broadcast, reduce, and allreduce can be composed of allgather or reduce-scatter and other collective operations; our techniques also improve the performance of these algorithms.
机译:最小通信算法是用于两个重要的集体通信操作(聚集和减少分散)的已知算法。没有任何进程发送或接收的数据超过最小数据量。这与操作的数据排序语义相结合,限制了这些算法的灵活性和性能。我们新颖的非最小拓扑感知算法通过添加少量冗余通信即可提供更好的性能。我们为Clos网络和单端口或多端口环形网络开发了新颖的算法。与本机IBM算法相比,在32k节点的BlueGene / P上进行的测试使全集合的速度提高了6倍,减少分散速度提高了11倍以上。广播,减少和减少可以由聚集,减少分散和其他集体操作组成;我们的技术还提高了这些算法的性能。

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