Equilibrium sampling of the configuration space in disordered systems requires algorithms that bypass the glassy slowing down of the physical dynamics. Irreversible MonteCarlo algorithms breaking detailed balance successfully accelerate sampling in some systems. We first implement an irreversible event-chain MonteCarlo algorithm in a model of continuously polydisperse hard disks. The effect of collective translational moves marginally affects the dynamics and results in a modest speedup that decreases with density. We then propose an irreversible algorithm performing collective particle swaps which outperforms all known MonteCarlo algorithms. We show that these collective swaps can also be used to prepare very dense jammed packings of disks.