Data Parallel Programming in an Adaptive Environment

Abstract

For better utilization of computing resources, it is important to consider parallel programming environments in which the number of available processors varies at runtime. In this paper, we discuss runtime support for data parallel programming in such an adaptive environment. Executing data parallel programs in an adaptive environment requires redistributing data when the number of processors changes, and also requires determining new loop bounds and communication patterns for the new set of processors. We have developed a runtime library to provide this support. We discuss how the runtime library can be used by compilers to generate code for an adaptive environment. We also present performance results for a multiblock Navier-Stokes solver run on a network of workstations using PVM for message passing. Our experiments show that if the number of processors is not varied frequently, the cost of data redistribution is not significant compared to the time required for the actual computations. (Also cross-referenced as UMIACS-TR-94-109)