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Degree of scalability: scalable reconfigurable mesh algorithms for multiple addition and matrix-vector multiplication

机译:可扩展性:可扩展的可重构网格算法,用于多重加法和矩阵矢量乘法

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The usual concern when scaling an algorithm on a parallel model of computation is preserving efficiency while increasing or decreasing the number of processors. Many algorithms for reconfigurable models, however, attain constant time at the expense of an inefficient algorithm. For these algorithms, scaling down the number of processors while preserving inefficiency is no benefit once constant time execution is lost. In fact, one can often accelerate the efficiency of these algorithms while reducing the number of processors. To quantify this improvement in efficiency, this paper introduces the measure of degree of scalability to complement the insight obtained from efficiency for such algorithms. Demonstrating the utility of this measure, we present new reconfigurable mesh (R-Mesh) algorithms for multiple addition and matrix-vector multiplication, improving both the number of processors and the degree of scalability compared to previous algorithms. We also extend these results to floating point number operands, which have previously received little attention on the R-Mesh.
机译:在并行计算模型上缩放算法时,通常的关注点是在增加或减少处理器数量的同时保持效率。但是,许多用于可重配置模型的算法都获得了恒定的时间,但却以效率低下的算法为代价。对于这些算法,一旦丢失了恒定时间的执行力,在保持低效率的同时缩减处理器数量就没有好处。实际上,人们通常可以在减少处理器数量的同时提高这些算法的效率。为了量化效率的提高,本文介绍了可伸缩性程度的度量,以补充从此类算法的效率中获得的见解。为了证明这一措施的实用性,我们提出了新的可重配置网格(R-Mesh)算法,用于多重加法和矩阵向量乘法,与以前的算法相比,改进了处理器数量和可扩展性。我们还将这些结果扩展到浮点数操作数,这些运算数以前在R-Mesh上很少受到关注。

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