A partitioning advisory system for networked data-parallel processing

With the increased performance capabilities of desktop computers, networked computing has become a popular vehicle for using parallelism to solve a variety of computationally intense problems. However, node heterogeneity and high communication costs may limit performance unless the problem space is carefully partitioned across the network in a way that considers both the capabilities of the machines and the high network communication costs. We describe an advisory system that is designed to help the programmer, compiler, or run-time environment choose the best decomposition strategy for partitioning speciic data-parallel applications across a given collection of machines. The system includes provisions for assessing the capabilities of the participating machines and the network in light of the current workload. Given information about the problem space, the machine speeds, and the network, the system provides a ranking of three standard partitioning methods. We test the validity of our system by comparing the observed relative performance with predicted relative performance of diierent data decompositions on a program with a variable number of oating point operations and a 5-point stencil communication pattern.