MPI Scaling Up for Powerlist Based Parallel Programs

Powerlists are recursive data structures that together with their associated algebraic theories could offer both a methodology to design parallel algorithms and parallel programming abstractions to ease the development of parallel applications This has been also proved by a concrete development of such a framework that allows easy, efficient, and reliable implementation of Java parallel programs on shared memory systems. The paper presents a highly scalable version of this framework by extending it to distributed memory systems based on an MPI implementation. Through this extension we may use the framework to develop Java parallel programs also on distributed memory systems such as clusters. The design of the framework enables flexibility in defining the appropriate execution type depending on the execution system and its characteristics. Therefore, it is possible to choose MPI execution (that also could be combined with multithreading) if the available system includes an MPI platform, or simple multithreading execution. Examples are given and performance experiments are conducted. The performance analysis of these applications emphasises the utility and the efficiency of this framework extension.