EuroPVM/MPI 2006 » William D. Gropp

William D. Gropp: Where Does MPI Need to Grow?

Abstract. MPI has been a successful parallel programming model. The combination of performance, scalability, composability, and support for libraries has made it relatively easy to build complex parallel applications. However, MPI is by no means the perfect parallel programming model. This talk will review the strengths of MPI with respect to other parallel programming models and discuss some of the weaknesses and limitations of MPI in the areas of performance, productivity, scalability, and interoperability. The talk will conclude with a discussion of what extensions (or even changes) may be needed in MPI, and what issues should be addressed by combining MPI with other parallel programming models.

About the speaker.

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 at 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. null

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.