Evaluating Distributed Shared Memory for Parallel Numerical Applications

One method to evaluate a distributed shared memory(DSM) system is to analyze its performance for a variety of algorithms using a simulator. Eager DSM systems reduce latencies for remote data accesses by sending shared data changes immediately to all processors that may need it. Simulation results for two parallel application programs, Gaussian elimination and Fast Fourier Transform, are compared for eagersharing SESAME versus more traditional demand-driven DSM systems. The evaluations indicate that (1) eager DSM systems can scale hundreds of times more e ciently in large networks; and (2) di erent data sharing strategies greatly a ect system performance. Eager sharing lets systems of thousands of processors e ciently run parallel scienti c programs by providing each processor with needed data at the earliest possible time. On-going research is creating parallel programming environments for high performance SESAME systems.