Listing of all papers published by Bongki Moon

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  • Adaptive Runtime Support for Direct Simulation Monte Carlo Methods on Distributed Memory Architechtures
    Published in Proceedings of the Scalable High Performance Computing Conference (SHPCC-94) pg: 176-183

    Bongki Moon, Joel Saltz.

    In highly adaptive irregular problems such as many Particle-In-Cell (PIC) codes and Direct Simulation Monte Carlo (DSMC) codes, data access patterns may vary from time step to time step. This fluctuation may hinder efficient utilization of distributed memory parallel computers because of the resulting overhead for data redistribution and dynamic load balancing. This may hinder efficient utilization of runtime pre-processing because the pre-processing requirements are sensitive to perturbations in the data access patterns. To efficiently parallelize such adaptive irregular problems on distributed memory parallel computers, several issues such as effective methods for domain partitioning, efficient index dereferencing and fast data transportation must be addressed. This paper presents efficient runtime support methods for such problems. These new runtime support primitives have recently been implemented and added to the CHAOS library. A new domain partitioning algorithm is introduced A simple one-dimensional domain partitioning method is implemented and compared with unstructured mesh partitioners such as recursive coordinate bisection and recursive inertial bisection. A remapping decision policy has been investigated for dynamic load balancing on 3-dimensional DSMC codes. Performance results are presented.

  • Parallel {DSMC} Solution of Three-Dimensional Flow over a Finite Flat Plane
    Proceedings of 6th {AIAA/ASME} Joint Thermophysics and Heat Transfer Conference

    R. Nance, R. Wilmoth, B. Moon, H. Hassan, J. Saltz.

    No Abstract Available...

  • 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.