Parallelizing nested loops on multicomputers-the grouping approach

In this paper, the design of a tool for partitioning and parallelking nested loops for execution on distributed-memory multicomputers is presented. The core of the tool is a technique called grouping, which identifies appropriate loop partition patterns based on data dependencies across the iterations. The grouping technique combined with analytic results f“ performance modeling tools will allow us to partition certain nested loops systematically and automatically, without users specifying the data partitions. Grouping is based on the concept of pipelined data parallel computation, which promises to achieve a balanced computation and communication on multicomputers. The basic structure of the parallelizing tool is presented. We will also describe the grouping and performance analysis techniques for pipelined data parallel computations. Finally, a prototype of the tool will be introduced to illustrate the feasibility of the approach.