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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 manyproblems in this area. In this paper, we introduce a family of implementationsof the Brouwer-Zimmermann algorithm for distributed-memory architectures forcomputing the minimum distance of a random linear code over F2. Both currentcommercial and public-domain software only work on either unicore architecturesor shared-memory architectures, which are limited in the number ofcores/processors employed in the computation. Our implementations focus ondistributed-memory architectures, thus being able to employ hundreds or eventhousands of cores in the computation of the minimum distance. Our experimentalresults show that our implementations are much faster, even up to severalorders of magnitude, than current implementations widely used nowadays.

Parallel Implementations for Computing the Minimum Distance of a Random Linear Code on Multicomputers