Optimizing irregular shared-memory applications for distributed-memory systems
In prior work, we have proposed techniques to extend the ease of shared-memory parallel programming to distributed-memory platforms by automatic translation of OpenMP programs to MPI. In the case of irregular applications, the performance of this translation scheme is limited by the fact that accesses to shared-data cannot be accurately resolved at compile-time. Additionally, irregular applications with high communication to computation ratios pose challenges even for direct implementation on message passing systems. In this paper, we present combined compile-time/run-time techniques for optimizing irregular shared-memory applications on message passing systems in the context of automatic translation from OpenMP to MPI. Our transformations enable computation-communication overlap by restructuring irregular parallel loops. The compiler creates inspectors to analyze actual data access patterns for irregular accesses at runtime. This analysis is combined with the compile-time analysis of regular data accesses to determine which iterations of irregular loops access non-local data. The iterations are then reordered to enable computation-communication overlap. In the case where the irregular access occurs inside nested loops, the loop nest is restructured. We evaluate our techniques by translating OpenMP versions of three benchmarks from two important classes of irregular applications - sparse matrix computations and molecular dynamics. We find that for these applications, on sixteen nodes, versions employing computation-communication overlap are almost twice as fast as baseline OpenMP-to-MPI versions, almost 30% faster than inspector-only versions, almost 25% faster than hand-coded versions on two applications and about 9% slower on the third.
algorithms, compiler techniques, compilers, computation-communication overlap, concurrent, distributed and parallel languages, iteration reordering, mpi, openmp, performance
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