Dynamic Partitioning in Different Distributed-Memory Environments

In this paper we present a detailed analysis of dynamic partitioning in different distributed-memory parallel environments based on experimental and analytical methods. We develop an experimental testbed for the IBM SP2 and a network of workstations, and we apply a general analytic model of dynamic partitioning. This experimental and analytical framework is then used to explore a number of fundamental performance issues and tradeoffs concerning dynamic partitioning in different distributed-memory computing environments. Our results demonstrate and quantify how the performance benefits of dynamic partitioning are heavily dependent upon several system variables, including workload characteristics, system architecture, and system load