Many scientific applications are iterative and specify repetitive communication patterns. This paper shows how a parallel-language compiler and custom cache-coherence protocols in a distributed shared memory system together can implement shared-memory communication efficiently for applications with unpredictable but repetitive communication patterns. The compiler uses data-flow analysis to identify program points where repetitive communication occurs. At runtime, the custom protocol builds communication schedules in one iteration and uses it to pre-send data in following iterations. This paper contains measurements on three iterative applications (including adaptive programs with unstructured data accesses) to show that custom protocols increase the number of shared-data requests satisfied locally, thus reducing the amount of time spent waiting for remote data.

Higher-level parallel programming languages can be difficult to implement efficiently on parallel machines. This paper shows how a flexible, compiler-controlled memory system can help achieve good performance for language constructs that previously appeared too costly to be practical.