Dynamic Data Distribution and Processor Repartitioning for Irregularly Structured Computations

Abstract

Irregular applications comprise a significant and increasing portion of jobs running in parallel environments. Recent research has shown that, in parallel environments, both the system utilization and application turn around time improve when resources allocated to applications can be dynamically adjusted at run-time, depending on the workload. To realize this, at least some of the parallel applications in the system need to be dynamically reconfigurable. We have implemented the Distributed Resource Management System (DRMS) that supports the development and execution of regular and irregular reconfigurable applications in time-variant resource environments. In this paper, we discuss DRMS support for developing reconfigurable irregular applications and describe the dynamic data redistribution mechanisms in some detail. We also present performance levels achieved by the data redistribution primitives, using a sparse Cholesky factorization algorithms as a model irregular application. Our results show that the cost of dynamic data redistribution among different processor configurations for irregular data are comparable to those for regular data.