  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 indeed fill in all such scheduling gaps, even
in real-life situations on large supercomputers.
