1 research outputs found
Adjusting process count on demand for petascale global optimization⋆
There are many challenges that need to be met before efficient and reliable computation at the
petascale is possible. Many scientific and engineering codes running at the petascale are likely to
be memory intensive, which makes thrashing a serious problem for many petascale applications.
One way to overcome this challenge is to use a dynamic number of processes, so that the total
amount of memory available for the computation can be increased on demand. This paper
describes modifications made to the massively parallel global optimization code pVTdirect in
order to allow for a dynamic number of processes. In particular, the modified version of the
code monitors memory use and spawns new processes if the amount of available memory is
determined to be insufficient. The primary design challenges are discussed, and performance
results are presented and analyzed