Resource-aware parallel adaptive computation for clusters

Abstract

Smaller institutions can now maintain local cluster computing environments to support research and teaching in high-performance scientific computation. Researchers can develop, test, and run software on the local cluster and move later to larger clusters and supercomputers at an appropriate time. This presents challenges in the development of software that can be run efficiently on a range of computing environments from the (often heterogeneous) local clusters to the larger clusters and supercomputers. Meanwhile, the clusters are also valuable teaching resources. We describe the use of a heterogeneous cluster at Williams College and its role in the development of software to support scientific computation in such environments, including two summer research projects completed by Williams undergraduates. © Springer-Verlag Berlin Heidelberg 2005.