  In this paper, we introduce DICOD, a convolutional sparse coding algorithm
which builds shift invariant representations for long signals. This algorithm
is designed to run in a distributed setting, with local message passing, making
it communication efficient. It is based on coordinate descent and uses locally
greedy updates which accelerate the resolution compared to greedy coordinate
selection. We prove the convergence of this algorithm and highlight its
computational speed-up which is super-linear in the number of cores used. We
also provide empirical evidence for the acceleration properties of our
algorithm compared to state-of-the-art methods.
