分布式存储机器上感知分组的并行化

Cho-Li Wang, V. K. Prasanna, Y. Lim
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引用次数: 7

摘要

我们提出了独立于体系结构的并行算法来解决分布式存储机器上的感知分组任务。给定n/spl次/n图像,使用P处理器,我们证明这些任务可以在O(n/sup 2//P)计算时间和20/spl径向/(P)T/sub d/+8(logP)T/sub d/+(40n//spl径向/(P)+20P)/spl tau//sub d/通信时间内完成,其中T/sub d/为通信启动时间,/spl tau//sub d/为传输速率。我们的实现表明,给定从1K/spl times/1K图像中提取的7K线段,使用具有256个处理节点的CM-5分区可以在1.115秒内执行线分组任务,使用16个节点的Cray T3D可以在0.382秒内执行线分组任务。我们的代码是用C语言和MPI消息传递标准编写的,可以很容易地移植到其他高性能计算平台上。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Parallelization of perceptual grouping on distributed memory machines
We propose architecture independent parallel algorithms for solving perceptual grouping tasks on distributed memory machines. Given an n/spl times/n image, using P processors, we show that these tasks can be performed in O(n/sup 2//P) computation time and 20/spl radic/(P)T/sub d/+8(logP)T/sub d/+(40n//spl radic/(P)+20P)/spl tau//sub d/ communication time, where T/sub d/ is the communication startup time and /spl tau//sub d/ is the transmission rate. Our implementations show that, given 7K line segments extracted from a 1K/spl times/1K image, the line grouping task can be performed in 1.115 seconds using a partition of CM-5 having 256 processing nodes and in 0.382 seconds using a 16 node Cray T3D. Our code is written in C and MPI message passing standard and can be easily ported to other high performance computing platforms.
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