  Equilibrium sampling of the configuration space in disordered systems
requires algorithms that bypass the glassy slowing down of the physical
dynamics. Irreversible Monte Carlo algorithms breaking detailed balance
successfully accelerate sampling in some systems. We first implement an
irreversible event-chain Monte Carlo algorithm in a model of 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 Monte Carlo algorithms. We show that these collective
swaps can also be used to prepare very dense jammed packings of disks.
