Abstract
There is often a trade-off between preserving sparsity and numerical stability in sparse matrix factorizations. In applications like the direct solution of Equality Constrained Least Squares problem, the accurate detection of the rank of a large and sparse constraint matrix is a key issue. Column pivoting is not suitable for distributed memory machines because it forces the program into a lock-step mode, preventing any overlapping of computations. So factorization algorithms on such machines need to use a reliable, yet inexpensive incremental condition estimator to decide on which columns to be included. We describe an incremental condition estimator that can be used during a sparse QR factorization. We show that it is quite reliable and is well suited for use on parallel machines. We supply experimental results to support its effectiveness as well as suitability for parallel architectures.
| Original language | English (US) |
|---|---|
| Title of host publication | Applications |
| Editors | David W. Walker, Quentin F. Stout |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 322-327 |
| Number of pages | 6 |
| ISBN (Electronic) | 0818621133, 9780818621130 |
| DOIs | |
| State | Published - Jan 1 1990 |
| Event | 5th Distributed Memory Computing Conference, DMCC 1990 - Charleston, United States Duration: Apr 8 1990 → Apr 12 1990 |
Publication series
| Name | Proceedings of the 5th Distributed Memory Computing Conference, DMCC 1990 |
|---|---|
| Volume | 1 |
Conference
| Conference | 5th Distributed Memory Computing Conference, DMCC 1990 |
|---|---|
| Country | United States |
| City | Charleston |
| Period | 4/8/90 → 4/12/90 |
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All Science Journal Classification (ASJC) codes
- Hardware and Architecture
- Information Systems and Management
- Computer Networks and Communications
Cite this
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Incremental condition estimator for parallel sparse matrix factorization. / Barlow, Jesse Louis; Vemulapati, U. B.
Applications. ed. / David W. Walker; Quentin F. Stout. Institute of Electrical and Electronics Engineers Inc., 1990. p. 322-327 555401 (Proceedings of the 5th Distributed Memory Computing Conference, DMCC 1990; Vol. 1).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Incremental condition estimator for parallel sparse matrix factorization
AU - Barlow, Jesse Louis
AU - Vemulapati, U. B.
PY - 1990/1/1
Y1 - 1990/1/1
N2 - There is often a trade-off between preserving sparsity and numerical stability in sparse matrix factorizations. In applications like the direct solution of Equality Constrained Least Squares problem, the accurate detection of the rank of a large and sparse constraint matrix is a key issue. Column pivoting is not suitable for distributed memory machines because it forces the program into a lock-step mode, preventing any overlapping of computations. So factorization algorithms on such machines need to use a reliable, yet inexpensive incremental condition estimator to decide on which columns to be included. We describe an incremental condition estimator that can be used during a sparse QR factorization. We show that it is quite reliable and is well suited for use on parallel machines. We supply experimental results to support its effectiveness as well as suitability for parallel architectures.
AB - There is often a trade-off between preserving sparsity and numerical stability in sparse matrix factorizations. In applications like the direct solution of Equality Constrained Least Squares problem, the accurate detection of the rank of a large and sparse constraint matrix is a key issue. Column pivoting is not suitable for distributed memory machines because it forces the program into a lock-step mode, preventing any overlapping of computations. So factorization algorithms on such machines need to use a reliable, yet inexpensive incremental condition estimator to decide on which columns to be included. We describe an incremental condition estimator that can be used during a sparse QR factorization. We show that it is quite reliable and is well suited for use on parallel machines. We supply experimental results to support its effectiveness as well as suitability for parallel architectures.
UR - http://www.scopus.com/inward/record.url?scp=85067895714&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85067895714&partnerID=8YFLogxK
U2 - 10.1109/DMCC.1990.555401
DO - 10.1109/DMCC.1990.555401
M3 - Conference contribution
AN - SCOPUS:85067895714
T3 - Proceedings of the 5th Distributed Memory Computing Conference, DMCC 1990
SP - 322
EP - 327
BT - Applications
A2 - Walker, David W.
A2 - Stout, Quentin F.
PB - Institute of Electrical and Electronics Engineers Inc.
ER -