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 article, 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 𝔽₂. 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.

线性码的最小距离是信息论中的一个关键概念。因此，其计算所需的时间对于该领域的许多问题都非常重要。在本文中，我们介绍了用于分布式内存架构的 Brouwer–Zimmermann 算法的一系列实现，用于计算 𝔽 2 上随机线性代码的最小_距离. 当前的商业和公共领域软件都只能在单核架构或共享内存架构上运行，这些架构在计算中使用的内核/处理器数量受到限制。我们的实现侧重于分布式内存架构，因此能够在最小距离的计算中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当前实现要快得多，甚至快几个数量级。