xxx算法:分布式存储器结构上计算随机线性码最小距离的并行实现

IF 2.7 1区 数学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING
G. Quintana-Ortí, Fernando Hernando, F. D. Igual
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引用次数: 0

摘要

线性码的最小距离是信息论中的一个关键概念。因此,它的计算所需的时间对该领域的许多问题都非常重要。在本文中,我们介绍了分布式存储器体系结构的Brouwer-Zimmermann算法的一系列实现,用于计算\(\mathbb)上随机线性码的最小距离{F}_{2} \)。当前的商业和公共领域软件都只在unicore架构或共享内存架构上工作,这在计算中使用的内核/处理器数量方面受到限制。我们的实现专注于分布式内存架构,因此能够在计算最小距离时使用数百甚至数千个内核。我们的实验结果表明,我们的实现比目前广泛使用的实现快得多,甚至高达几个数量级。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Algorithm xxx: Parallel Implementations for Computing the Minimum Distance of a Random Linear Code on Distributed-memory Architectures
The minimum distance of a linear code is a key concept in information theory. Therefore, the time required by its computation is very important to many problems in this area. In this paper, we introduce a family of implementations of the Brouwer-Zimmermann algorithm for distributed-memory architectures for computing the minimum distance of a random linear code over \(\mathbb {F}_{2} \) . Both current commercial and public-domain software only work on either unicore architectures or shared-memory architectures, which are limited in the number of cores/processors employed in the computation. Our implementations focus on distributed-memory architectures, thus being able to employ hundreds or even thousands of cores in the computation of the minimum distance. Our experimental results show that our implementations are much faster, even up to several orders of magnitude, than current implementations widely used nowadays.
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来源期刊
ACM Transactions on Mathematical Software
ACM Transactions on Mathematical Software 工程技术-计算机:软件工程
CiteScore
5.00
自引率
3.70%
发文量
50
审稿时长
>12 weeks
期刊介绍: As a scientific journal, ACM Transactions on Mathematical Software (TOMS) documents the theoretical underpinnings of numeric, symbolic, algebraic, and geometric computing applications. It focuses on analysis and construction of algorithms and programs, and the interaction of programs and architecture. Algorithms documented in TOMS are available as the Collected Algorithms of the ACM at calgo.acm.org.
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