Accelerated Solution of Sparse Linear Systems

DAGFresh from the NVIDIA Numeric Libraries Team, a white paper illustrating the use of the CUSPARSE and CUBLAS libraries to achieve a 2x speedup of incomplete-LU- and Cholesky-preconditioned iterative methods. The paper focuses on the Bi-Conjugate Gradient and stabilized Conjugate Gradient iterative methods that can be used to solve large sparse non-symmetric and symmetric positive definite linear systems, respectively. The paper also comments on the parallel sparse triangular solver, which is an essential building block in these algorithms.

Read the technical review on our NVIDIA Research Site

Download the white paper or the webinar recording discussing this white paper: www.nvidia.com/webinars.

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About Mark Harris

Mark is Chief Technologist for GPU Computing Software at NVIDIA. Mark has fifteen years of experience developing software for GPUs, ranging from graphics and games, to physically-based simulation, to parallel algorithms and high-performance computing. Mark has been using GPUs for general-purpose computing since before they even supported floating point arithmetic. While a Ph.D. student at UNC he recognized this nascent trend and coined a name for it: GPGPU (General-Purpose computing on Graphics Processing Units), and started GPGPU.org to provide a forum for those working in the field to share and discuss their work. Follow @harrism on Twitter
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