The parallel implementations allow us to considerably reduce the execution time of the simulator as compared to purely sequential executions. Running these programs within the simulation environment allows for a much wider range of studies than would be otherwise possible. For the NAS-BTIO benchmarks, well over 20000 write requests were eliminated by issuing larger, collective requests. The matrix multiplication program was able to operate more efficiently with larger matrices when cache coordination allowed larger data sets to be kept in memory. Running the simulation model in parallel has shown promising reductions in the execution time of the experiments, up to 6 times improvement on 16 processors.
Parallelism is achieved by domain decomposition and message-passing.
MPI-SIM was used to evaluate the scalability of the kernel as a function of number of processors, message latencies and blocking factor. The target architecture was assumed to be the IBM SP2 multicomputer. Beyond that increasing the number of processors performance gains were small. Figure 1 shows the predicted runtime for up to 240 processors. We are currently investigating the scalability of the problem for larger problem sizes and hope to identify the maximum number of processors that can be used effectively for this problem.