Brief Announcement: Communication Optimal Sparse LU Factorization for Planar Matrices
Author:
Affiliation:
1. Oak Ridge National Laboratory, Oak Ridge, TN, USA
2. Lawrence Berkeley National Laboratory, Berkeley, CA, USA
Funder
DOE U.S. Department of Energy
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3558481.3591315
Reference9 articles.
1. Highly scalable parallel algorithms for sparse matrix factorization
2. P. Sao , X. S. Li , and R. Vuduc , " A communication-avoiding 3D LU factorization algorithm for sparse matrices," in Proceedings of the IEEE International Parallel and Distributed Processing Symposium (IPDPS), (Vancouver , BC , Canada) , May 2018 . P. Sao, X. S. Li, and R. Vuduc, "A communication-avoiding 3D LU factorization algorithm for sparse matrices," in Proceedings of the IEEE International Parallel and Distributed Processing Symposium (IPDPS), (Vancouver, BC, Canada), May 2018.
3. Communication lower bounds for distributed-memory matrix multiplication
4. Communication-optimal Parallel and Sequential QR and LU Factorizations
5. L. Grigori P.-Y. David J. W. Demmel and S. Peyronnet "Brief announcement: Lower bounds on communication for sparse cholesky factorization of a model problem " in Proceedings of the twenty-second annual ACM symposium on Parallelism in algorithms and architectures pp. 79--81 ACM 2010. L. Grigori P.-Y. David J. W. Demmel and S. Peyronnet "Brief announcement: Lower bounds on communication for sparse cholesky factorization of a model problem " in Proceedings of the twenty-second annual ACM symposium on Parallelism in algorithms and architectures pp. 79--81 ACM 2010.
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