Malleable parallelism with minimal effort for maximal throughput and maximal hardware load.

Abstract

In practice, standard scheduling of parallel computing jobs almost always leaves significant portions of the available hardware unused, even with many jobs still waiting in the queue. The simple reason is that the resource requests of these waiting jobs are fixed and do not match the available, unused resources. However, with alternative but existing and well-established techniques it is possible to achieve a fully automated, adaptive parallelism that does not need pre-set, fixed resources. Here, we demonstrate that such an adaptively parallel program can run productively on a machine that is traditionally considered "full" and thus can indeed fill in all such scheduling gaps, even in real-life situations on large supercomputers in which a fixed-size job could not have started.