For more information about this meeting, contact Xiantao Li, Yuxi Zheng, Kris Jenssen, Jinchao Xu, Hope Shaffer.
| Title: | Thinking parallel: sparse iterative solvers with CUDA |
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| Seminar: | Computational and Applied Mathematics Colloquium |
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| Speaker: | Jonathan Cohen, NVIDIA |
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| Abstract: |
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| Iterative sparse linear solvers are a critical component of a scientific computing platform. Developing effective preconditioning strategies is the main challenge in developing iterative sparse solvers on massively parallel systems. As computing systems become increasingly power-constrained, memory hierarchies for massively parallel systems will become deeper and more hierarchical. Parallel algorithms with all-to-all communication patterns that assume uniform memory access times will be inefficient on these systems. In this talk, I will outline the challenges of developing good parallel preconditioners, and demonstrate that domain decomposition methods have communication patterns that match emerging parallel platforms. I will present recent work to develop restricted additive Schwarz (RAS) preconditioners as part of the open source 'cusp' library of sparse parallel algorithms. On 2d Poisson problems, a RAS preconditioner is consistently faster than diagonal preconditioning in time-to-solution. Detailed analysis demonstrates that the communication pattern of RAS matches the on-chip bandwidths of a Fermi GPU. Line smoothing, which requires solving a large number of small tridiagonal linear systems in local memory, is another preconditioning approach with similar communication patterns. I will conclude with a road map for developing a range of preconditioners, smoothers, and linear solvers on massively parallel hardware based on the domain decomposition and line smoothing approaches. |
Room Reservation Information
| Room Number: | MB106 |
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| Date: | 01 / 21 / 2011 |
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| Time: | 03:35pm - 04:25pm |
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