Improving parallel irregular reductions using partial array expansion
Much effort has been devoted recently to efficiently parallelize irregular reductions. In this paper, parallelizing techniques for these computations are analyzed in terms of three performance aspects: parallelism, data locality and memory overhead. These aspects have a strong influence in the overa...
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| Veröffentlicht in: | Proceedings of the 2001 ACM/IEEE Conference on Supercomputing S. 38 |
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| Sprache: | Englisch |
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New York, NY, USA
ACM
10.11.2001
IEEE |
| Schriftenreihe: | ACM Conferences |
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| ISBN: | 9781581132939, 158113293X |
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| Abstract | Much effort has been devoted recently to efficiently parallelize irregular reductions. In this paper, parallelizing techniques for these computations are analyzed in terms of three performance aspects: parallelism, data locality and memory overhead. These aspects have a strong influence in the overall performance and scalability of the parallel code. We will discuss how the parallelization techniques usually try to optimize some of these aspects, while missing the other(s). We will show that by combining complementary techniques we can improve the overall performance/scalability of the parallel irregular reduction, obtaining an effective solution for large problems on large machines. Specifically, a combination of array expansion and a locality-oriented method (DWA-LIP), named partial array expansion, is introduced. An implementation of the proposed method is discussed, showing that the transformation that the compiler must apply to the irregular reduction code is not excessively complex. Finally, the method is analyzed and experimentally evaluated. |
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| AbstractList | Much effort has been devoted recently to efficiently parallelize irregular reductions. In this paper, parallelizing techniques for these computations are analyzed in terms of three performance aspects: parallelism, data locality and memory overhead. These aspects have a strong influence in the overall performance and scalability of the parallel code. We will discuss how the parallelization techniques usually try to optimize some of these aspects, while missing the other(s). We will show that by combining complementary techniques we can improve the overall performance/scalability of the parallel irregular reduction, obtaining an effective solution for large problems on large machines. Specifically, a combination of array expansion and a locality-oriented method (DWA-LIP), named partial array expansion, is introduced. An implementation of the proposed method is discussed, showing that the transformation that the compiler must apply to the irregular reduction code is not excessively complex. Finally, the method is analyzed and experimentally evaluated. |
| Author | Gutiérrez, Eladio Plata, Oscar Zapata, Emilio L. |
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| SubjectTerms | Computer architecture Computer systems organization -- Architectures -- Distributed architectures -- Grid computing Computer systems organization -- Architectures -- Parallel architectures -- Multicore architectures Computer systems organization -- Architectures -- Serial architectures -- Superscalar architectures Computing methodologies -- Modeling and simulation -- Simulation types and techniques -- Massively parallel and high-performance simulations Computing methodologies -- Parallel computing methodologies -- Parallel programming languages Concurrent computing Data analysis Data engineering Data structures General and reference -- Cross-computing tools and techniques -- Performance Multidimensional systems Parallel processing Performance analysis Permission Scalability Software and its engineering -- Software notations and tools -- General programming languages -- Language types -- Parallel programming languages Software and its engineering -- Software organization and properties -- Software system structures -- Distributed systems organizing principles -- Grid computing |
| Title | Improving parallel irregular reductions using partial array expansion |
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