John R. Gilbert
University of California, Santa Barbara
130 Papers
1.4K Citations
John R. Gilbert is an academic researcher from University of California, Santa Barbara. The author has contributed to research in topics: Sparse matrix & Parallel algorithm. The author has an hindex of 47, co-authored 130 publications. Previous affiliations of John R. Gilbert include Cornell University & University of New Mexico.
Chat about Author
Papers
A Supernodal Approach to Sparse Partial Pivoting
TL;DR: A sparse LU code is developed that is significantly faster than earlier partial pivoting codes and compared with UMFPACK, which uses a multifrontal approach; the code is very competitive in time and storage requirements, especially for large problems.
Sparse matrices in matlab: design and implementation
TL;DR: The matrix computation language and environment MATLAB is extended to include sparse matrix storage and operations, and nearly all the operations of MATLAB now apply equally to full or sparse matrices, without any explicit action by the user.
The Combinatorial BLAS: design, implementation, and applications
Aydin Buluc,John R. Gilbert +1 more
- 01 Nov 2011
TL;DR: The parallel Combinatorial BLAS is described, which consists of a small but powerful set of linear algebra primitives specifically targeting graph and data mining applications, and an extensible library interface and some guiding principles for future development are provided.
481
Parallel sparse matrix-vector and matrix-transpose-vector multiplication using compressed sparse blocks
Aydin Buluc,Jeremy T. Fineman,Matteo Frigo,John R. Gilbert,Charles E. Leiserson +4 more
- 11 Aug 2009
TL;DR: In this article, a storage format for sparse matrices, called compressed sparse blocks (CSB), is introduced, which allows both Ax and A,x to be computed efficiently in parallel, where A is an n×n sparse matrix with nnzen nonzeros and x is a dense n-vector.
Approximating treewidth, pathwidth, frontsize, and shortest elimination tree
TL;DR: Various parameters of graphs connected to sparse matrix factorization and other applications can be approximated using an algorithm of Leighton et al. that finds vertex separators of graphs, and it is shown that unless P = NP there are no absolute approximation algorithms for any of the parameters.
370