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Collective Algorithms for Sub-communicators

机译:子通信器的集体算法

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摘要

Collective communication over a group of processors is an integral and time consuming component in many HPC applications. Many modern day supercomputers are based on torus interconnects. On such systems, for an irregular communicator comprising of a subset of processors, the algorithms developed so far are not contention free in general and hence non-optimal. In this paper, we present a novel contention-free algorithm to perform collective operations over a subset of processors in a torus network. We also extend previous work on regular communicators to handle special cases of irregular communicators that occur frequently in parallel scientific applications. For the generic case where multiple node disjoint sub-communicators communicate simultaneously in a loosely synchronous fashion, we propose a novel cooperative approach to route the data for individual sub-communicators without contention. Empirical results demonstrate that our algorithms outperform the optimized MPI collective implementation on IBM's Blue Gene/P supercomputer for large data sizes and random node distributions.
机译:在许多HPC应用程序中,通过一组处理器进行的集体通信是不可或缺的耗时组件。许多现代超级计算机都基于环形互连。在这样的系统上,对于包括处理器子集的不规则通信器,到目前为止开发的算法通常不是没有竞争的,因此不是最优的。在本文中,我们提出了一种新颖的无竞争算法,可以在环形网络中的处理器子集上执行集体操作。我们还扩展了有关常规传播者的先前工作,以处理在并行科学应用中经常发生的非常规传播者的特殊情况。对于多个节点不相交的子通信器以松散同步方式同时通信的一般情况,我们提出了一种新颖的协作方法来路由各个子通信器的数据而无争用。实证结果表明,对于大数据量和随机节点分布,我们的算法在IBM Blue Gene / P超级计算机上的性能优于优化的MPI集体实现。

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