Self-scheduling on distributed-memory machines

The authors present a general approach of self-scheduling a non-uniform parallel loop on a distributed-memory machine. The approach has two phases: a static scheduling phase and a dynamic scheduling phase. In addition to reduce scheduling overhead, using the static scheduling phase allows the data needed by the statically scheduled iterations to be prefetched. The dynamic scheduling phase balances the workload. Data distribution methods for self-scheduling are also the focus of this paper. The authors classify the data distribution methods into four categories and present partial duplication, a method that allows the problem size to grow linearly in the number of processors. The experiments conducted on a 64-node NCUBE show that as much as 79% improvement is achieved over static scheduling on the generation of a false-color image.