使用全带宽和恒定存储的超立方体上的矩阵乘法

Ching-Tien Ho, Lennart Johnsson, Alan Edelman
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引用次数: 29

摘要

对于在积矩阵累积的超立方多处理器上进行矩阵乘法,处理器必须接收每个输入操作数的大约P2/n个元素,操作数的大小为P × P,均匀分布在n个处理器上。在所有端口上进行并发通信时,每个输入操作数的元素传输顺序可以减少到P2/fllog1\J。我们提出了矩阵的两级划分和矩阵的一种算法:最优数据乘法。运动和恒定存储。该算法的顺序算法复杂度为2P3,并行算法复杂度为2P3/N。该算法已在CM-2型连接机中实现。对于8K CM-2的性能,我们测量了大约1.6 Gflops,对于64K的完整机器,这将扩展到大约13 Gflops。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Matrix Multiplication on Hypercubes Using Full Bandwith and Constant Storage
For matrix multiplicatioln on hypercube multiprocessors with the product matrix accumulated in place a processor must receive albout P2/n elements of each input operand, with opeicands of size P x P distributed evenly over N processors. With concurrent communication on all ports, the number of element transfers in sequence can be reduced to P2/fllog1\J for each input operand. We present a two-level partitioning of the matrices and an algolrithm for the matrix: multiplication with optimal data. motion and constant storage. The algorithm has sequential arithmetic complexity 2P3, and parallel arithmetic complexity 2P3/N. The algorithm has been implemented oin the Connection Machine model CM-2. For the performance on the 8K CM-2, we measured iibout 1.6 Gflops, which would scale up to about 13 Gflops for a 64K full machine.
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