The influence of relaxed supernode partitions on the multifrontal method
ACM Transactions on Mathematical Software (TOMS)
The role of elimination trees in sparse factorization
SIAM Journal on Matrix Analysis and Applications
On finding supernodes for sparse matrix computations
SIAM Journal on Matrix Analysis and Applications
Block sparse Cholesky algorithms on advanced uniprocessor computers
SIAM Journal on Scientific Computing
SIAM Journal on Scientific Computing
Automatically tuned linear algebra software
SC '98 Proceedings of the 1998 ACM/IEEE conference on Supercomputing
A Specialized Interior-Point Algorithm for Multicommodity Network Flows
SIAM Journal on Optimization
Parallel and Fully Recursive Multifrontal Supernodal Sparse Cholesky
ICCS '02 Proceedings of the International Conference on Computational Science-Part II
Sparse Matrix Structure for Dynamic Parallelisation Efficiency
Euro-Par '00 Proceedings from the 6th International Euro-Par Conference on Parallel Processing
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The sparse Cholesky factorization of some large matrices can require a two dimensional partitioning of the matrix. The sparse hypermatrix storage scheme produces a recursive 2D partitioning of a sparse matrix. The subblocks are stored as dense matrices so BLAS3 routines can be used. However, since we are dealing with sparse matrices some zeros may be stored in those dense blocks. The overhead introduced by the operations on zeros can become large and considerably degrade performance. In this paper we present an improvement to our sequential in-core implementation of a sparse Cholesky factorization based on a hypermatrix storage structure. We compare its performance with several codes and analyze the results.