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

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 lin...

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Main Author: Chan, Ernie W., 1982-
Format: Others
Language:English
Published: 2010
Subjects:
Online Access:http://hdl.handle.net/2152/ETD-UT-2010-08-1563
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spelling ndltd-UTEXAS-oai-repositories.lib.utexas.edu-2152-ETD-UT-2010-08-15632015-09-20T16:55:56ZApplication of dependence analysis and runtime data flow graph scheduling to matrix computationsChan, Ernie W., 1982-Matrix computationDirected acyclic graphAlgorithm-by-blocksWe 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.text2010-11-23T21:39:30Z2010-11-23T21:39:36Z2010-11-23T21:39:30Z2010-11-23T21:39:36Z2010-082010-11-23August 20102010-11-23T21:39:36Zthesisapplication/pdfhttp://hdl.handle.net/2152/ETD-UT-2010-08-1563eng
collection NDLTD
language English
format Others
sources NDLTD
topic Matrix computation
Directed acyclic graph
Algorithm-by-blocks
spellingShingle Matrix computation
Directed acyclic graph
Algorithm-by-blocks
Chan, Ernie W., 1982-
Application of dependence analysis and runtime data flow graph scheduling to matrix computations
description 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. === text
author Chan, Ernie W., 1982-
author_facet Chan, Ernie W., 1982-
author_sort Chan, Ernie W., 1982-
title Application of dependence analysis and runtime data flow graph scheduling to matrix computations
title_short Application of dependence analysis and runtime data flow graph scheduling to matrix computations
title_full Application of dependence analysis and runtime data flow graph scheduling to matrix computations
title_fullStr Application of dependence analysis and runtime data flow graph scheduling to matrix computations
title_full_unstemmed Application of dependence analysis and runtime data flow graph scheduling to matrix computations
title_sort application of dependence analysis and runtime data flow graph scheduling to matrix computations
publishDate 2010
url http://hdl.handle.net/2152/ETD-UT-2010-08-1563
work_keys_str_mv AT chanerniew1982 applicationofdependenceanalysisandruntimedataflowgraphschedulingtomatrixcomputations
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