Data Allocation for Distributed Programs

Abstract

This thesis shows that both data and code must be efficiently distributed to achieve good performance in a distributed system. Most previous research has either tried to distribute code structures to improve parallelism or to distribute data to reduce communication costs. Code distribution (exploiting functional parallelism) is an effort to distribute or to duplicate function codes to optimize parallel performance. On the other hand, data distribution tries to place data structures as close as possible to the function codes that use it, so that communication cost can be reduced.