GPU Accelerated Sparse Cholesky Factorization
The solution of sparse symmetric positive definite linear systems is an important computational kernel in large-scale scientific and engineering modeling and simulation. We will solve the linear systems using a direct method, in which a Cholesky factorization of the coefficient matrix is performed using a right-looking approach and the resulting triangular factors are used to compute the solution. Sparse Cholesky factorization is compute intensive. In this work we investigate techniques for reducing the factorization time in sparse Cholesky factorization by offloading some of the dense matrix operations on a GPU. We will describe the techniques we have considered. We achieved up to 4x speedup compared to the CPU-only version.
- Research Organization:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Sponsoring Organization:
- US Department of Energy; USDOE Office of Science (SC)
- DOE Contract Number:
- AC02-05CH11231
- OSTI ID:
- 2586562
- Resource Relation:
- Proceedings of Sc 2024 W Workshops of the International Conference for High Performance Computing Networking Storage and Analysis
- Country of Publication:
- United States
- Language:
- English
Similar Records
Sparse Cholesky factorization on a multiprocessor
A scalable parallel algorithm for sparse Cholesky factorization