Application of dependence analysis and runtime data flow graph scheduling to matrix computations

Repository

Application of dependence analysis and runtime data flow graph scheduling to matrix computations

Show simple record

dc.contributor.advisor Van de Geijn, Robert A.
dc.creator Chan, Ernie W., 1982-
dc.date.accessioned 2010-11-23T21:39:30Z
dc.date.accessioned 2010-11-23T21:39:36Z
dc.date.available 2010-11-23T21:39:30Z
dc.date.available 2010-11-23T21:39:36Z
dc.date.created 2010-08
dc.date.issued 2010-11-23
dc.date.submitted August 2010
dc.identifier.uri http://hdl.handle.net/2152/ETD-UT-2010-08-1563
dc.description.abstract We present a methodology for exploiting shared-memory parallelism within matrix computations by expressing linear algebra algorithms as directed acyclic graphs. Our solution involves a separation of concerns that completely hides the exploitation of parallelism from the code that implements the linear algebra algorithms. This approach to the problem is fundamentally different since we also address the issue of programmability instead of strictly focusing on parallelization. Using the separation of concerns, we present a framework for analyzing and developing scheduling algorithms and heuristics for this problem domain. As such, we develop a theory and practice of scheduling concepts for matrix computations in this dissertation.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.subject Matrix computation
dc.subject Directed acyclic graph
dc.subject Algorithm-by-blocks
dc.title Application of dependence analysis and runtime data flow graph scheduling to matrix computations
dc.date.updated 2010-11-23T21:39:36Z
dc.contributor.committeeMember Browne, James C.
dc.contributor.committeeMember Lin, Calvin
dc.contributor.committeeMember Pingali, Keshav
dc.contributor.committeeMember Plaxton, Charles G.
dc.contributor.committeeMember Quintana-Orti, Enrique S.
dc.description.department Computer Sciences
dc.type.genre thesis
dc.type.material text
thesis.degree.department Computer Sciences
thesis.degree.discipline Computer Sciences
thesis.degree.grantor University of Texas at Austin
thesis.degree.level Doctoral
thesis.degree.name Doctor of Philosophy

Files in this work

Download File: CHAN-DISSERTATION.pdf
Size: 833.4Kb
Format: application/pdf

This work appears in the following Collection(s)

Show simple record


Advanced Search

Browse

My Account

Statistics

Information