Matches in SemOpenAlex for { <https://semopenalex.org/work/W1574163721> ?p ?o ?g. }
Showing items 1 to 95 of
95
with 100 items per page.
- W1574163721 abstract "This thesis presents research into parallel linear solvers for block-diagonal-bordered sparse matrices. The block-diagonal-bordered form identifies parallelism that can be exploited for both direct and iterative linear solvers. We have developed efficient parallel block-diagonal-bordered sparse direct methods based on both LU factorization and Choleski factorization algorithms, and we have also developed a parallel block-diagonal-bordered sparse iterative method based on the Gauss-Seidel method. Parallel factorization algorithms for block-diagonal-bordered form matrices require a specialized ordering step coupled to an explicit load balancing step in order to generate this matrix form and to distribute the computational workload uniformly for an irregular matrix throughout a distributed-memory multi-processor. Matrix orderings are performed using a diakoptic technique based on node-tearing-nodal analysis. Parallel Gauss-Seidel algorithms for block-diagonal-bordered form matrices require a two-part matrix ordering technique--first to partition the matrix into block-diagonal-bordered form, again, using the node-tearing diakoptic techniques and then to multi-color the data in the last diagonal block using graph coloring techniques. The ordered matrices have extensive parallelism, while maintaining the strict precedence relationships in the Gauss-Seidel algorithm.Empirical performance measurements for real power system networks are presented for implementations of a parallel block-diagonal-bordered LU algorithm, a similar Choleski algorithm, and a parallel block-diagonal-bordered Gauss-Seidel algorithm run on a distributed memory Thinking Machines CM-5 multi-processor. We have compared the performance of the direct and iterative parallel implementations on the CM-5, and show that significant algorithmic speedup may be possible for the Gauss-Seidel algorithm versus Choleski factorization for positive definite matrices. We have developed a simple technique that uses empirical data to predict the performance of these algorithms on future architectures. We apply these techniques to develop algorithm performance predictions for future Scalable Parallel Processing (SPP) architectures." @default.
- W1574163721 created "2016-06-24" @default.
- W1574163721 creator A5003605480 @default.
- W1574163721 creator A5059904315 @default.
- W1574163721 date "1995-01-01" @default.
- W1574163721 modified "2023-09-22" @default.
- W1574163721 title "Parallel block-diagonal-bordered sparse linear solvers for power systems applications" @default.
- W1574163721 hasPublicationYear "1995" @default.
- W1574163721 type Work @default.
- W1574163721 sameAs 1574163721 @default.
- W1574163721 citedByCount "4" @default.
- W1574163721 crossrefType "journal-article" @default.
- W1574163721 hasAuthorship W1574163721A5003605480 @default.
- W1574163721 hasAuthorship W1574163721A5059904315 @default.
- W1574163721 hasConcept C106487976 @default.
- W1574163721 hasConcept C113313756 @default.
- W1574163721 hasConcept C11413529 @default.
- W1574163721 hasConcept C114614502 @default.
- W1574163721 hasConcept C121332964 @default.
- W1574163721 hasConcept C123213974 @default.
- W1574163721 hasConcept C126312332 @default.
- W1574163721 hasConcept C130367717 @default.
- W1574163721 hasConcept C158693339 @default.
- W1574163721 hasConcept C159694833 @default.
- W1574163721 hasConcept C159985019 @default.
- W1574163721 hasConcept C163716315 @default.
- W1574163721 hasConcept C173608175 @default.
- W1574163721 hasConcept C187834632 @default.
- W1574163721 hasConcept C192562407 @default.
- W1574163721 hasConcept C202444582 @default.
- W1574163721 hasConcept C2524010 @default.
- W1574163721 hasConcept C2777210771 @default.
- W1574163721 hasConcept C33923547 @default.
- W1574163721 hasConcept C3828260 @default.
- W1574163721 hasConcept C41008148 @default.
- W1574163721 hasConcept C42355184 @default.
- W1574163721 hasConcept C56372850 @default.
- W1574163721 hasConcept C62520636 @default.
- W1574163721 hasConcept C84507854 @default.
- W1574163721 hasConcept C85817219 @default.
- W1574163721 hasConcept C96442724 @default.
- W1574163721 hasConceptScore W1574163721C106487976 @default.
- W1574163721 hasConceptScore W1574163721C113313756 @default.
- W1574163721 hasConceptScore W1574163721C11413529 @default.
- W1574163721 hasConceptScore W1574163721C114614502 @default.
- W1574163721 hasConceptScore W1574163721C121332964 @default.
- W1574163721 hasConceptScore W1574163721C123213974 @default.
- W1574163721 hasConceptScore W1574163721C126312332 @default.
- W1574163721 hasConceptScore W1574163721C130367717 @default.
- W1574163721 hasConceptScore W1574163721C158693339 @default.
- W1574163721 hasConceptScore W1574163721C159694833 @default.
- W1574163721 hasConceptScore W1574163721C159985019 @default.
- W1574163721 hasConceptScore W1574163721C163716315 @default.
- W1574163721 hasConceptScore W1574163721C173608175 @default.
- W1574163721 hasConceptScore W1574163721C187834632 @default.
- W1574163721 hasConceptScore W1574163721C192562407 @default.
- W1574163721 hasConceptScore W1574163721C202444582 @default.
- W1574163721 hasConceptScore W1574163721C2524010 @default.
- W1574163721 hasConceptScore W1574163721C2777210771 @default.
- W1574163721 hasConceptScore W1574163721C33923547 @default.
- W1574163721 hasConceptScore W1574163721C3828260 @default.
- W1574163721 hasConceptScore W1574163721C41008148 @default.
- W1574163721 hasConceptScore W1574163721C42355184 @default.
- W1574163721 hasConceptScore W1574163721C56372850 @default.
- W1574163721 hasConceptScore W1574163721C62520636 @default.
- W1574163721 hasConceptScore W1574163721C84507854 @default.
- W1574163721 hasConceptScore W1574163721C85817219 @default.
- W1574163721 hasConceptScore W1574163721C96442724 @default.
- W1574163721 hasLocation W15741637211 @default.
- W1574163721 hasOpenAccess W1574163721 @default.
- W1574163721 hasPrimaryLocation W15741637211 @default.
- W1574163721 hasRelatedWork W1488842985 @default.
- W1574163721 hasRelatedWork W1585371964 @default.
- W1574163721 hasRelatedWork W1589986168 @default.
- W1574163721 hasRelatedWork W2044416352 @default.
- W1574163721 hasRelatedWork W2066166631 @default.
- W1574163721 hasRelatedWork W2084848530 @default.
- W1574163721 hasRelatedWork W2098862161 @default.
- W1574163721 hasRelatedWork W2115915434 @default.
- W1574163721 hasRelatedWork W2121271278 @default.
- W1574163721 hasRelatedWork W2127144731 @default.
- W1574163721 hasRelatedWork W2133539877 @default.
- W1574163721 hasRelatedWork W2143133118 @default.
- W1574163721 hasRelatedWork W2150644616 @default.
- W1574163721 hasRelatedWork W2153024623 @default.
- W1574163721 hasRelatedWork W2338693488 @default.
- W1574163721 hasRelatedWork W2529420697 @default.
- W1574163721 hasRelatedWork W2909974497 @default.
- W1574163721 hasRelatedWork W3151562337 @default.
- W1574163721 hasRelatedWork W32838146 @default.
- W1574163721 hasRelatedWork W44937787 @default.
- W1574163721 isParatext "false" @default.
- W1574163721 isRetracted "false" @default.
- W1574163721 magId "1574163721" @default.
- W1574163721 workType "article" @default.