Experiments with a Task Partitioning Model for Heterogeneous Computing

Abstract

One potentially promising approach for exploiting the best features of a variety of different computer architectures is to partition an application program to simultaneously execute on two or more different machines interconnected with a high-speed network. A fundamental problem with this heterogeneous computing, however, is the difficulty of partitioning an application program across the machines. This paper presents a partitioning strategy that relates the relative performance of two heterogeneous machines to the communication cost of transferring partial results across their interconnection network. Experiments are described that use this strategy to partition two different application programs across the sequential front-end processor of a Connection Machine CM-200, and its parallel back-end array.