Listing of all papers published by Shamik D. Sharma

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  • Multiprocessor Runtime Support for Fine-Grained, Irregular {DAGs}
    Submitted to {ASPLOS-VI}, 1994

    Frederick T. Chong, Shamik D. Sharma, Eric Brewer, Joel Saltz.

    mine multiprocessor runtime support for fine-grained, ir regular directed acyclic graphs (DAGs) such as those that arise from sparse-matrix triangular solves. We conduct our experiments on the CM-5, whose lower latencies and active-message support allow us to achieve unprecedented speedups for a general multiprocessor. Where as previous implementations have maximum speedups of less than 4 on even simple banded matrices, we are able to obtain scalable performance onextremely small and irregular problems. On a matrix with only 5300 rows, we are able to achieve scalable performance with a speedup of almost 35 for 128 processors.

    We achieve these speedups with non-matrix-specific methods which are applicable to any DAG. We compare a range of run-time preprocessed and dynamic approaches on matrices from the Harwell-Boeing benchmark set. Although precomputed data distributions and execution schedules produce the best performance, we find that it is challenging to keep their cost low enough to make them worthwhile on small, fine-grained problems.

    Additionally, we find that a policy of frequent net work polling can reduce communication overhead by a factor of three over the standard CM-5 policies. We present a detailed study of runtime overheads and demonstrate that send and receive processor overhead still dominate these applications on the CM-5. We conclude that these applications would highly benefit from architectural support for low-overhead communication.

  • Run-time and Compile-time Support for Adaptive Irregular Problems
    Published in Supercomputing 1994 published by IEEE Press, pg: 97-106

    Shamik D. Sharma, Ravi Ponnusamy, Bongki Moon, Yuan-Shin Hwang, Raja Das, Joel Saltz.

    In adaptive irregular problems the data arrays are accessed via indirection arrays, and data access patterns change during computation. Implementing such problems on distributed memory machines requires support for dynamic data partitioning, efficient preprocessing and fast data migration. This research presents efficient runtime primitives for such problems. This new set of primitives is part of the CHAOS library. It subsumes the previous PARTI library which targeted only static irregular problems. To demonstrate the efficacy of the runtime support, two real adaptive irregular applications have been parallelized using CHAOS primitives: a molecular dynamics code (CHARMM) and a particle-in-cell code (DSMC). The paper also proposes extensions to Fortran D which can allow compilers to generate more efficient code for adaptive problems. These language extensions have been implemented in the Syracuse Fortran 90D/HPF prototype compiler. The performance of the compiler parallelized codes is compared with the hand parallelized versions.