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SC Conference - Activity Details



CUKr-Krylov Solvers on GPU Clusters

Authors:
Serban Georgescu  (University of Tokyo)
Yohei Sato  (University of Tokyo)
Hiroshi Okuda  (University of Tokyo)
Posters Session
Tuesday,  05:15PM - 07:00PM
Room Rotunda Lobby
Abstract:
Krylov-type iterative solvers are widely used for solving large systems of linear equations. Severely memory bounded, Krylov solvers have difficulty achieving even 10% of CPU peak. With the large and widening performance gap between CPUs and GPUs, employing GPUs as accelerators looks indeed promising. In this poster we present a new open-source software package named CUKr (CUDA Krylov) intended as a scalable framework for building Krylov solvers on GPU clusters. Additionally, the implementation style required for working on multiple GPUs makes it suitable for multicore/manycore. The same solver can be run multi-node / multi-GPU / multi-core, with or without iterative refinement and so on, using simple command-line options. We will be showing performance and scalability results obtained for a Conjugate Gradient solver built using this framework on a collection of real-world matrices on a GPU cluster and a multicore system. For large matrices, up to 15x speedup has been obtained.
   IEEE Computer Society  /  ACM     2 0   Y E A R S   -   U N L E A S H I N G   T H E   P O W E R   O F   H P C