重组和分区开销都依赖于问题的并行递归计算

A. Saha, M. D. Wagh
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引用次数: 1

摘要

并行递归计算包含不可避免的和重要的并行计算开销,包括各种各样的应用程序,可以建模为T(n) =左{{mathop {min}limits_{0 le r le n}^{t_{(),}}}右。left{{max left{{T(n - r),T(r) + k(r)} right} + mathop {mathop {lambda (n,r)}limits_{否则}}limit ^{forn le n_{(),}} right}其中k(r)和X(n,r)分别表示分区开销和重组开销。最优分区大小(上述最小最大递归关系的r的解)是非平凡的,并且与通常使用的n/2值非常不同。在递归的每个阶段使用最优分区大大提高了性能。在本文中,我们解决了并行递归模型的一个具有挑战性的情况,其中开销函数是问题相关的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Parallel Recursive Computations where Both Recombination and Partition Overheads are Problem-Dependent
Parallel recursive computations incorporating the unavoidable and significant parallel computing overheads, encompassing a wide variety of applications, can be modeled as T(n) = left{ {mathop {min }limits_{0 le r le n}^{t_{(),} } } right.left{ {max left{ {T(n - r),T(r) + k(r)} right} + mathop {mathop {lambda (n,r)}limits_{otherwise} }limits^{forn le n_{(),} } } right} where k(r) and X(n,r) represent the partition and recombination overheads respectively. The optimal partition size (solution to r of the above minmax recurrence relation) is nontrivial and is very different from the n/2 value conventionally used. Using the optimal partitions at every stage of the recursion enhances the performance greatly. In this paper we solve a challenging case of our parallel recursive model where the overhead functions are problem-dependent.
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