Math 627 - Introduction to Parallel Computing
Spring 2009 - Matthias K. Gobbert
Presentations of the Class Projects

Friday, May 15, 2009, 02:30 p.m., SOND 206

  1. 02:30-02:45
    Long-Time Simulation of Calcium Waves in a Heart Cell to Model Wave Self-Organization
    Zana Coulibaly, Michael Muscedere, Matthias K. Gobbert, and Bradford E. Peercy, Department of Mathematics and Statistics
    A model for the flow of calcium on the scale of one heart cell is given by a system of time-dependent reaction-diffusion equations coupled by nonlinear reaction terms. The model has previously been validated to recreate the conditions of an experiment and can be used to to perform simulations. A coefficient function of the calcium current through a calcium release unit determines the calcium distribution level and thus influences wave self-organization. Previous experiments determined extreme values of this parameter for which a wave self-organizes or not. Running the simulation multiple times for multiple values allowed us to determine a smaller, more precise range of the parameter for which a wave will self-organize or not.

  2. 02:50-03:05
    Parallel Performance Studies with COMSOL Multiphysics via Scripting and Batch Processing
    Noemi Petra and Matthias K. Gobbert, Department of Mathematics and Statistics
    COMSOL Multiphysics is an extremely powerful and versatile finite element package for the solution of partial differential equations. While the graphical user interface (GUI) offers a friendly environment for solving small problems, for the solution of a more demanding problem with correspondingly larger memory requirements and longer run times, it is often desirable to explore the script-based modeling capabilities, as well as the benefits of running COMSOL in parallel. This work gives step-by-step instructions on how to use m-files in conjunction with MATLAB as scripting tool and COMSOL's own binary format for batch processing as well as guidance on how to run COMSOL in parallel on the cluster hpc in the UMBC High Performance Computing Facility. We also report on the shared memory parallel performance of COMSOL using all cores available on a compute node. The results show that the speedup is not in proportion to the number of cores used.

  3. 03:10-03:25
    Parallel Performance Studies for a Maximum Likelihood Estimation Problem with TAO
    Andrew Raim, Nagaraj K. Neerchal, Matthias K. Gobbert, and Jorge G. Morel, Department of Mathematics and Statistics
    In this report we present an application of parallel computing to an estimation procedure in statistics. The method of maximum likelihood estimation (MLE) is based on the ability to perform maximizations of functions. The optimizations are often carried with numerical methods on computer in practice, and may be time consuming for some likelihood functions. We consider one such likelihood function based on the Finite Mixture (FM) Multinomial distribution. We conduct estimation for this problem in parallel on a cluster of computers. Our study utilizes the High Performance Computing Facility (HPCF) at University of Maryland, Baltimore County (UMBC), and uses the Toolkit for Advanced Optimization (TAO) software library. We study how the resource requirements change as problem sizes vary, and demonstrate that scaling up the number of processes for larger problems decreases wall clock time significantly.

  4. 03:30-03:45
    Parallel Numerical Study for a 3-D Poisson Problem
    Guan Wang and Matthias K. Gobbert, Department of Mathematics and Statistics
    In this talk, we want to find the numerical solution to the 3-D Poisson problem using Jacobi method by parallel computing. After checking the correctness of our computation, we will compare the speed of computation by blocking communication with MPI_Ssend/MPI_Recv and by non-blocking communication with MPI_Isend/MPI_Irecv. Also, we need to compare the speedup and efficiency of these two different communications and verify non-blocking case is more efficient.

  5. 03:50-04:05
    Parallel Studies for Chemically Reacting Systems
    Yushu Yang and Muruhan Rathinam, Department of Mathematics and Statistics
    Parallel computing code can be applied to solve chemically reacting systems. In a well stirred chemical system, the number of molecules for each species can be solved by implicit tau method, and the histogram of the method can be compared with the exact simulation called SSA. In this report, the parallel code will be implemented in both SSA and implicit tau, with the discussion that how random number generator will be applied in the parallel code from large number of sample simulations. Moreover, the studies of analyzing the number of molecules from large samples will be provided.

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This page version 1.0, May 2009.