In this paper, we present a new distributed algorithm for minimizing a sum of non-necessarily differentiable convex
functions composed with arbitrary linear operators. The overall cost function is assumed strongly convex.
Each involved function is associated with a node of a hypergraph having the ability to communicate with neighboring nodes sharing the same hyperedge. Our algorithm relies on a primal-dual splitting strategy with established convergence guarantees. We show how it can be efficiently implemented to take full advantage of a multicore architecture. The good numerical performance of the proposed approach is illustrated in a problem of video sequence denoising, where a significant speedup is achieved.