The Computing Innovation Fellows Project

Posted on: Sunday, May 9th, 2010
Broad Research Area: Numerical/Scientific Computing / HPC / Data-Intensive Scalable Computing, Theory / Algorithms

Research Interests:

The Mathematical Sciences Department at the IBM T.J. Watson Research Center is engaged in basic and applied research in several areas of scientific computing, high-performance computing, algorithms, and optimization. We are seeking postdoctoral researchers in the areas of highly parallel graph algorithms, preconditioners, and other core methods to aid the development of massively parallel scalable solvers for large sparse systems of linear equations.

Solving large sparse systems of linear equations is the core computation in a large number of applications in science, engineering, and optimization. With the rapidly increasing number and complexity of scientific applications that model time dependent physical phenomena, there is an urgent need for robust, high-performance, highly-parallel, practical general purpose sparse linear solvers because of the limitations of the current iterative solver software libraries and the asymptotically superlinear time and memory demands of direct solvers. The development of such solvers requires advances in some key enabling algorithms and preconditioning techniques that are self adaptable to a variety of sparse linear systems and machine architectures. In this context, some of the topics that we plan to explore in the near future include parallel approximate and exact algorithms for graph matching (particularly, maximum edge-weight matchings for general and bipartite graphs), parallel gra ph partitioning and mesh distribution, use of machine learning for preconditioner tuning, self adapting preconditioners, preconditioners based on random sampling, composite preconditioners and solvers, etc.

Contact Information:

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