Myths in Parallel Programming for Scientific Applications
Abstract
Parallel programming for high-performance computing is often described as an art that only an elite group of programmers can master. The arguments for this position are often based on the low fraction of peak performance achieved by many parallel computers, particularly those based on microprocessors, and on the difficultly in developing parallel programs. Like many myths, these have some basis in reality. This talk discusses these myths, the ways in which they reflect both real and imagined problems, and how progress in hardware and software technology is overcoming the problems of parallel computing.
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Biographical Sketch: William Gropp received his B.S. in Mathematics from Case Western Reserve University in 1977, a MS in Physics from the University of Washington in 1978, and a Ph.D. in Computer Science from Stanford in 1982. He held the positions of assistant (1982-1988) and associate (1988-1990) professor in the Computer Science Department of Yale University. In 1990, he joined the Numerical Analysis group at Argonne, where he is a Senior Computer Scientist and Associate Director of the Mathematics and Computer Science Division, a Senior Scientist in the Department of Computer Science at the University of Chicago, and a Senior Fellow in the Argonne-Chicago Computation Institute. His research interests are in parallel computing, software for scientific computing, and numerical methods for partial differential equations. He has played a major role in the development of the MPI message-passing standard. He is co-author of the most widely used implementation of MPI, MPICH, and was involved in the MPI Forum as a chapter author for both MPI-1 and MPI-2. He has written many books and papers on MPI including "Using MPI" and "Using MPI-2". He is also one of the designers of the PETSc parallel numerical library, and has developed efficient and scalable parallel algorithms for the solution of linear and nonlinear equations. |