重计算快速矩阵乘法的I/ o复杂度

Roy Nissim, O. Schwartz
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引用次数: 6

摘要

处理器之间和内存层次结构之间的通信成本通常支配着算法的运行时间。我们可以用这些成本换取重新计算吗?大多数算法没有为此目的使用重新计算,并且大多数通信成本下限假设没有重新计算,因此没有解决这个基本问题。最近,Bilardi和De Stefani(2017)以及Bilardi、Scquizzato和Silvestri(2018)表明,在Strassen的快速矩阵乘法和快速傅里叶变换中,重新计算不能降低通信成本。我们扩展了前一个边界,并证明了重复计算不能降低其他一些快速矩阵乘法算法的通信开销。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Revisiting the I/O-Complexity of Fast Matrix Multiplication with Recomputations
Communication costs, between processors and across the memory hierarchy, often dominate the runtime of algorithms. Can we trade these costs for recomputations? Most algorithms do not utilize recomputation for this end, and most communication cost lower bounds assume no recomputation, hence do not address this fundamental question. Recently, Bilardi and De Stefani (2017), and Bilardi, Scquizzato, and Silvestri (2018) showed that recomputations cannot reduce communication costs in Strassen's fast matrix multiplication and in fast Fourier transform. We extend the former bound and show that recomputations cannot reduce communication costs for a few other fast matrix multiplication algorithms.
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