PList-based Divide and Conquer Parallel Programming

This paper details an extension of a Java parallel programming framework – JPLF. The JPLF framework is a programming framework that helps programmers build parallel programs using existing building blocks. The framework is based on {em PowerLists} and PList Theories and it naturally supports multi-way Divide and Conquer. By using this framework, the programmer is exempted from dealing with all the complexities of writing parallel programs from scratch. This extension to the JPLF framework adds PLists support to the framework and so, it enlarges the applicability of the framework to a larger set of parallel solvable problems. Using this extension, we may apply more flexible data division strategies. In addition, the length of the input lists no longer has to be a power of two – as required by the PowerLists theory. In this paper we unveil new applications that emphasize the new class of computations that can be executed within the JPLF framework. We also give a detailed description of the data structures and functions involved in the PLists extension of the JPLF, and extended performance experiments are described and analyzed

Implementing powerlists with Bulk Synchronous Parallel ML

International audienceThe latest developments of the computation systems impose using tools and methodologies able to simplify the development process of parallel software, but also to assure a high level of performance and robustness. Powerlists and their variants are data structures that can be successfully used in a simple, provably correct, functional description of parallel programs, which are divide and conquer in nature. They represent one of the high-level algebraic theories which are appropriate to be used as fundamentals for a model of parallel computation that assures correctness proving. The paper presents how programs defined based on powerlists could be transformed to real code in the functional language OCaml plus calls to the parallel functional programming library Bulk Synchronous Parallel ML. BSML functions follow the BSP model requirements, and so its advantages are introduced in OCaml parallel code. The transformations are based on a framework that assures: simple, correct, efficient implementation. Examples are given and concrete experiments for their executions are conducted. The results emphasise the utility and the efficiency of the framework