Applications in science and engineering often require huge computational
resources for solving problems within a reasonable time frame. Parallel
supercomputers provide the computational infrastructure for solving such
problems. A traditional application scheduler running on a parallel cluster
only supports static scheduling where the number of processors allocated to an
application remains fixed throughout the lifetime of execution of the job. Due
to the unpredictability in job arrival times and varying resource requirements,
static scheduling can result in idle system resources thereby decreasing the
overall system throughput. In this paper we present a prototype framework
called ReSHAPE, which supports dynamic resizing of parallel MPI applications
executed on distributed memory platforms. The framework includes a scheduler
that supports resizing of applications, an API to enable applications to
interact with the scheduler, and a library that makes resizing viable.
Applications executed using the ReSHAPE scheduler framework can expand to take
advantage of additional free processors or can shrink to accommodate a high
priority application, without getting suspended. In our research, we have
mainly focused on structured applications that have two-dimensional data arrays
distributed across a two-dimensional processor grid. The resize library
includes algorithms for processor selection and processor mapping. Experimental
results show that the ReSHAPE framework can improve individual job turn-around
time and overall system throughput.Comment: 15 pages, 10 figures, 5 tables Submitted to International Conference
on Parallel Processing (ICPP'07