Parallel Solution of Sparse Triangular Linear Systems in the Preconditioned Iterative Methods on the GPU

@inproceedings{Expressway2011ParallelSO,
  title={Parallel Solution of Sparse Triangular Linear Systems in the Preconditioned Iterative Methods on the GPU},
  author={San Tomas Expressway and Santa Clara},
  year={2011}
}
A novel algorithm for solving in parallel a sparse triangular linear system on a graphical processing unit is proposed. It implements the solution of the triangular system in two phases. First, the analysis phase builds a dependency graph based on the matrix sparsity pattern and groups the independent rows into levels. Second, the solve phase obtains the full solution by iterating sequentially across the constructed levels. The solution elements corresponding to each single level are obtained… CONTINUE READING
Highly Influential
This paper has highly influenced 10 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 81 citations. REVIEW CITATIONS

Citations

Publications citing this paper.
Showing 1-10 of 46 extracted citations

A New GPU Algorithm to Compute a Level Set-Based Analysis for the Parallel Solution of Sparse Triangular Systems

2018 IEEE International Parallel and Distributed Processing Symposium (IPDPS) • 2018
View 7 Excerpts
Highly Influenced

Assessing Sparse Triangular Linear System Solvers on GPUs

2017 International Symposium on Computer Architecture and High Performance Computing Workshops (SBAC-PADW) • 2017
View 6 Excerpts
Highly Influenced

Hybrid Multi-elimination ILU Preconditioners on GPUs

2014 IEEE International Parallel & Distributed Processing Symposium Workshops • 2014
View 5 Excerpts
Highly Influenced

81 Citations

01020'12'14'16'18
Citations per Year
Semantic Scholar estimates that this publication has 81 citations based on the available data.

See our FAQ for additional information.

References

Publications referenced by this paper.
Showing 1-10 of 25 references

CUDA by Example: An Introduction to General-Purpose GPU Programming

Scalable Computing: Practice and Experience • 2010
View 1 Excerpt

Programming Massively Parallel Processors. A Hands-on Approach

Scalable Computing: Practice and Experience • 2010
View 1 Excerpt

Yelick and J . Demmel , Optimization of Sparse MatrixVector Multiplication on Emerging Multicore Platforms

K.
2010

Implementing sparse matrix-vector multiplication on throughput-oriented processors

Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis • 2009
View 1 Excerpt

Scalable parallel programming with CUDA

2008 IEEE Hot Chips 20 Symposium (HCS) • 2008
View 1 Excerpt

Direct Methods for Sparse Linear Systems

T. A. Davis
SIAM, Philadelphia, PA • 2006
View 1 Excerpt

Similar Papers

Loading similar papers…