Time-Optimal Multiple Rank Computations on Meshes with Multiple Broadcasting

D. Bhagavathi, V. Bokka, H. Gurla, R. Lin, S. Olariu, J. Schwing, W. Shen, L. Wilson
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引用次数: 2

Abstract

Consider arbitrary collections A = a_1,a_2,.. .,a_n of items and Q = q_1,q_2,...,q_m (1 leqslant mn leqslant n) of queries from a totally ordered universe. The multiple rank problem involves computing for every query qi the number of items in A that have a lesser value. Our contribution is to show that the problem at hand can be solved time-optimally on meshes with multiple broadcasting. More specifically, if the collection A is siored in some order one item per processor and if Q is stored one query per processor in the leftmost frac{m} {{sqrt n }} columns of a mesh with multiple broadcasting of size sqrt n x /sqrt n, the corresponding instance of the multiple rank problem can be solved in Theta left( {m^{frac{1} {3}} n^{frac{1} {6}} } right) time. As an application we present a time-optimal algorithm to compute the histogram of a m-level gray image of size sqrt n x sqrt n in Theta left( {m^{frac{1} {3}} n^{frac{1} {6}} } right) time.
多重广播网格的时间最优多重秩计算
考虑任意集合A = a_1,a_2,…,a_n的项目和Q = q_1,q_2,…,q_m (1 leqslant mn leqslant n)来自全有序宇宙的查询。多重排序问题涉及为每个查询qi计算A中具有较小值的项的数量。我们的贡献是表明手头的问题可以在多重广播的网格上以时间优化的方式解决。更具体地说,如果集合A按某种顺序每个处理器存储一个项,如果Q在一个大小为sqrt n x /sqrt n的多重广播网格的最左边的frac{m} {{sqrt n}}列中每个处理器存储一个查询,那么多重秩问题的相应实例可以在Theta左({m^{frac{1} {3}} n^{frac{1}{6}}}右)时间内解决。作为一个应用,我们提出了一种时间优化算法来计算大小为sqrt n x sqrt n的m级灰度图像在Theta左({m^{frac{1} {3}} n^{frac{1}{6}}}右)时间的直方图。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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