Simulation / Modeling / Design

Accelerated Solution of Sparse Linear Systems

Jun 22, 2011
By Mark Harris
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AI-Generated Summary
  • A white paper from the NVIDIA Numeric Libraries Team demonstrates a 2x speedup of incomplete-LU- and Cholesky-preconditioned iterative methods using CUSPARSE and CUBLAS libraries.
  • The paper focuses on Bi-Conjugate Gradient and stabilized Conjugate Gradient iterative methods for solving large sparse non-symmetric and symmetric positive definite linear systems.
  • The parallel sparse triangular solver is highlighted as an essential building block in these algorithms.

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Fresh 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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Tags

Simulation / Modeling / Design | Algorithms / Numerical Techniques | cuBLAS | NVIDIA Research

About the Authors

About Mark Harris
Mark is an NVIDIA Distinguished Engineer working on RAPIDS. Mark has over twenty years of experience developing software for GPUs, ranging from graphics and games, to physically-based simulation, to parallel algorithms and high-performance computing. While a Ph.D. student at The University of North Carolina he recognized a nascent trend and coined a name for it: GPGPU (General-Purpose computing on Graphics Processing Units).
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