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Concurrent Number Cruncher : An Efficient Sparse Linear Solver on the GPU
Abstract : A wide class of geometry processing and PDE resolution methods needs to solve a linear system, where the non-zero pattern of the matrix is dictated by the connectivity matrix of the mesh. The advent of GPUs with their ever-growing amount of parallel horsepower makes them a tempting resource for such numerical computations. This can be helped by new APIs (CTM from ATI and CUDA from NVIDIA) which give a direct access to the multithreaded computational resources and associated memory bandwidth of GPUs; CUDA even provides a BLAS implementation but only for dense matrices (CuBLAS). However, existing GPU linear solvers are restricted to specific types of matrices, or use non-optimal compressed row storage strategies. By combining recent GPU programming techniques with supercomputing strategies (namely block compressed row storage and register blocking), we implement a sparse generalpurpose linear solver which outperforms leading-edge CPU counterparts (MKL / ACML).
Cited literature [17 references]
https://hal.inria.fr/inria-00186833
Contributor : Nicolas Ray <>
Submitted on : Monday, November 12, 2007 - 3:58:59 PM
Last modification on : Monday, March 15, 2021 - 10:56:03 AM
Long-term archiving on: : Monday, April 12, 2010 - 1:57:26 AM
Contributor : Nicolas Ray <>
Submitted on : Monday, November 12, 2007 - 3:58:59 PM
Last modification on : Monday, March 15, 2021 - 10:56:03 AM
Long-term archiving on: : Monday, April 12, 2010 - 1:57:26 AM
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HPCC_number_cruncher.pdf
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