  The minimum distance of a code is an important concept in information theory.
Hence, computing the minimum distance of a code with a minimum computational
cost is a crucial process to many problems in this area. In this paper, we
present and evaluate a family of algorithms and implementations to compute the
minimum distance of a random linear code over $\mathbb{F}_{2}$ that are faster
than different current implementations. In addition to the basic sequential
implementations, we present parallel and vectorized implementations that render
high performances on modern architectures. The attained performance results
show the benefits of the developed optimized algorithms, which obtain
remarkable performance improvements compared with state-of-the-art
implementations widely used nowadays.
