An approach for code generation in the Sparse Polyhedral Framework
Applications that manipulate sparse data structures contain memory reference patterns that are unknown at compile time due to indirect accesses such as A[B[i]]. To exploit parallelism and improve locality in such applications, prior work has developed a number of Run-Time Reordering Transformations...
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ndltd-arizona.edu-oai-arizona.openrepository.com-10150-6158002016-07-09T03:01:06Z An approach for code generation in the Sparse Polyhedral Framework Strout, Michelle Mills LaMielle, Alan Carter, Larry Ferrante, Jeanne Kreaseck, Barbara Olschanowsky, Catherine Computer Science Department, University of Arizona Inspector/executor strategies Runtime reordering transformations Sparse Polyhedral Framework Applications that manipulate sparse data structures contain memory reference patterns that are unknown at compile time due to indirect accesses such as A[B[i]]. To exploit parallelism and improve locality in such applications, prior work has developed a number of Run-Time Reordering Transformations (RTRTs). This paper presents the Sparse Polyhedral Framework (SPF) for specifying RTRTs and compositions thereof and algorithms for automatically generating efficient inspector and executor code to implement such transformations. Experimental results indicate that the performance of automatically generated inspectors and executors competes with the performance of hand-written ones when further optimization is done. 2016-04 Article An approach for code generation in the Sparse Polyhedral Framework 2016, 53:32 Parallel Computing 0167-8191 10.1016/j.parco.2016.02.004 http://hdl.handle.net/10150/615800 http://arizona.openrepository.com/arizona/handle/10150/615800 Parallel Computing en http://linkinghub.elsevier.com/retrieve/pii/S0167819116000557 Copyright © 2016 Elsevier B.V. All rights reserved. ELSEVIER SCIENCE BV |
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en |
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Inspector/executor strategies Runtime reordering transformations Sparse Polyhedral Framework |
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Inspector/executor strategies Runtime reordering transformations Sparse Polyhedral Framework Strout, Michelle Mills LaMielle, Alan Carter, Larry Ferrante, Jeanne Kreaseck, Barbara Olschanowsky, Catherine An approach for code generation in the Sparse Polyhedral Framework |
description |
Applications that manipulate sparse data structures contain memory reference patterns that are unknown at compile time due to indirect accesses such as A[B[i]]. To exploit parallelism and improve locality in such applications, prior work has developed a number of Run-Time Reordering Transformations (RTRTs). This paper presents the Sparse Polyhedral Framework (SPF) for specifying RTRTs and compositions thereof and algorithms for automatically generating efficient inspector and executor code to implement such transformations. Experimental results indicate that the performance of automatically generated inspectors and executors competes with the performance of hand-written ones when further optimization is done. |
author2 |
Computer Science Department, University of Arizona |
author_facet |
Computer Science Department, University of Arizona Strout, Michelle Mills LaMielle, Alan Carter, Larry Ferrante, Jeanne Kreaseck, Barbara Olschanowsky, Catherine |
author |
Strout, Michelle Mills LaMielle, Alan Carter, Larry Ferrante, Jeanne Kreaseck, Barbara Olschanowsky, Catherine |
author_sort |
Strout, Michelle Mills |
title |
An approach for code generation in the Sparse Polyhedral Framework |
title_short |
An approach for code generation in the Sparse Polyhedral Framework |
title_full |
An approach for code generation in the Sparse Polyhedral Framework |
title_fullStr |
An approach for code generation in the Sparse Polyhedral Framework |
title_full_unstemmed |
An approach for code generation in the Sparse Polyhedral Framework |
title_sort |
approach for code generation in the sparse polyhedral framework |
publisher |
ELSEVIER SCIENCE BV |
publishDate |
2016 |
url |
http://hdl.handle.net/10150/615800 http://arizona.openrepository.com/arizona/handle/10150/615800 |
work_keys_str_mv |
AT stroutmichellemills anapproachforcodegenerationinthesparsepolyhedralframework AT lamiellealan anapproachforcodegenerationinthesparsepolyhedralframework AT carterlarry anapproachforcodegenerationinthesparsepolyhedralframework AT ferrantejeanne anapproachforcodegenerationinthesparsepolyhedralframework AT kreaseckbarbara anapproachforcodegenerationinthesparsepolyhedralframework AT olschanowskycatherine anapproachforcodegenerationinthesparsepolyhedralframework AT stroutmichellemills approachforcodegenerationinthesparsepolyhedralframework AT lamiellealan approachforcodegenerationinthesparsepolyhedralframework AT carterlarry approachforcodegenerationinthesparsepolyhedralframework AT ferrantejeanne approachforcodegenerationinthesparsepolyhedralframework AT kreaseckbarbara approachforcodegenerationinthesparsepolyhedralframework AT olschanowskycatherine approachforcodegenerationinthesparsepolyhedralframework |
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1718342092201132032 |