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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Bibliographic Details
Published in:Proceedings of the 2001 ACM/IEEE Conference on Supercomputing p. 38
Main Authors: Gutiérrez, Eladio, Plata, Oscar, Zapata, Emilio L.
Format: Conference Proceeding
Language:English
Published: New York, NY, USA ACM 10.11.2001
IEEE
Series:ACM Conferences
Subjects:
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
ISBN:9781581132939, 158113293X
Online Access:Get full text
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Summary: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.
Bibliography:SourceType-Conference Papers & Proceedings-1
ObjectType-Conference Paper-1
content type line 25
ISBN:9781581132939
158113293X
DOI:10.1145/582034.582072