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What is Data Parallelism

Handbook of Research on Scalable Computing Technologies
Data parallel computations apply the same operation in parallel on different elements of the same set of data.
Published in Chapter:
Mixed Parallel Programming Models Using Parallel Tasks
Joerg Duemmler (Chemnitz University of Technology, Germany), Thomas Rauber (University of Bayreuth, Germany), and Gudula Ruenger (Chemnitz University of Technology, Germany)
Copyright: © 2010 |Pages: 30
DOI: 10.4018/978-1-60566-661-7.ch011
Abstract
Parallel programming models using parallel tasks have shown to be successful for increasing scalability on medium-size homogeneous parallel systems. Several investigations have shown that these programming models can be extended to hierarchical and heterogeneous systems which will dominate in the future. In this chapter, the authors discuss parallel programming models with parallel tasks and describe these programming models in the context of other approaches for mixed task and data parallelism. They discuss compiler-based as well as library-based approaches for task programming and present extensions to the model which allow a flexible combination of parallel tasks and an optimization of the resulting communication structure.
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Big Data in Massive Parallel Processing: A Multi-Core Processors Perspective
Simultaneous execution on multiple cores of the same function across the elements of a dataset. This also refers to Single Instruction Multiple Data (SIMD).
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