Saturday, March 15

1:30 PM-3:30 PM
Chair: Vipin Kumar, University of Minnesota, Minneapolis
Greenway A-B

CP9
Sparse Matrix Methods

1:30 Design and Implementation of a Scalable Parallel Direct Solver for Sparse Symmetric Positive Definite Systems: Preliminary Results

Anshul Gupta and Fred Gustavson, IBM T. J. Watson Research Center, and Mahesh Joshi, George Karypis, and Vipin Kumar, University of Minnesota, Minneapolis

1:50 A Parallel Sparse Symmetric Indefinite Solver

Yogin Campbell and Alex Pothen, Old Dominion University

2:10 A Comparison of 1-D and 2-D Data Mapping for Sparse LU Factorization with Partial Pivoting

Cong Fu, Xiangmin Jiao, and Tao Yang, University of California, Santa Barbara

2:30 A Parallel Frontal Solver for Process Simulation

J. U. Mallya, Cray Research, Inc.; M. A. Stadtherr, University of Notre Dame; S. E. Zitney and S. Choudhary, Cray Research, Inc.

2:50 Sampling and Analytical Techniques for Data Distribution of Parallel Sparse Computation

Tyng-Ruey Chuang, Institute of Information Science, Academia Sinica, Taiwan, and Rong-Guey Chang and Jenq Kuen Lee, National Tsing Hua University, Taiwan

3:10 Improving Memory-System Performance of Sparse Matrix-Vector Multiplication

Sivan Toledo, IBM T. J. Watson Research Center

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