组在批量同步并行计算

J. González, C. León, F. Piccoli, A. M. Printista, J. R. García, C. Rodríguez, F. D. Sande
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引用次数: 3

摘要

提出了对批量同步并行模型(BSP)的一种扩展,允许使用异步BSP处理器组。在这个称为嵌套BSP的模型中,可以划分处理器组,并且组中的处理器通过组相关的集体操作进行同步,从而推广了屏障同步的概念。给出了涉及其并行输入输出分布的问题和算法的分类。对于其中的一类问题,我们提出了一种通用的策略来推导高效的并行算法。这类算法允许对处理器子集进行任意划分,使底层BSP软件能够将网络划分为独立的子网络,从而最大限度地减少网络中其余部分的流量对预测成本的影响。通过三个分而治之的程序来说明模型的表现力。这些程序在六台高性能超级计算机上的计算结果表明,对于所考虑的这类问题,模型是准确的,加速是最优的。
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Groups in bulk synchronous parallel computing
An extension to the Bulk Synchronous Parallel Model (BSP) to allow the use of asynchronous BSP groups of processors is presented. In this model, called Nested BSP, processor groups can be divided and processors in a group synchronize through group dependent collective operations generalizing the concept of barrier synchronization. A classification of problems and algorithms attending to their parallel input-output distribution is provided. For one of these problem classes, the called common-common class, we present a general strategy to derive efficient parallel algorithms. Algorithms belonging to this class allow the arbitrary division of the processor subsets, easing the opportunities of the underlying BSP software to divide the network in independent sub networks, minimizing the impact of the traffic in the rest of the network in the predicted cost. The expressiveness of the model is exemplified through three divide and conquer programs. The computational results for these programs in six high performance supercomputers show both the accuracy of the model and the optimality of the speedups for the class of problems considered.
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