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|
|Seminar:||Computational and Applied Mathematics Colloquium|
|Speaker:||Jonathan Cohen, NVIDIA|
|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
|Date:||01 / 21 / 2011|
|Time:||03:35pm - 04:25pm|