Exploring the future of out-of-core computing with compute-local non-volatile memory
SC '13 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
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We investigate the use of graphics processors (GPUs) to accelerate the solution of large-scale linear systems when the problem data is larger than the main memory of the system and storage on disk is employed. Our solution addresses the programmability problem with a combination of the high-level approach in libflame (the FLAME library for dense linear algebra)and a run-time system that handles I/O transparently to the programmer. Results on a desktop computer equipped with an NVIDIA GPU reveal this platform as a cost-effective tool that yields high-performance for solving moderate to large-scale linear algebra problems. The computation of the Cholesky factorization is used to illustrate these techniques.