DaSH: A benchmark suite for hybrid dataflow and shared memory programming models
•Three important properties of DaSH are breadth, depth and portability.•The main strength of dataflow is the ability to eliminate unnecessary barriers.•Dataflow and shared memory implementations have comparable code complexity.•Dataflow implementations can improve performance of shared memory progra...
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| Published in: | Parallel computing Vol. 45; pp. 18 - 48 |
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| Main Authors: | , , , , , |
| Format: | Journal Article Publication |
| Language: | English |
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01.06.2015
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| ISSN: | 0167-8191, 1872-7336 |
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| Abstract | •Three important properties of DaSH are breadth, depth and portability.•The main strength of dataflow is the ability to eliminate unnecessary barriers.•Dataflow and shared memory implementations have comparable code complexity.•Dataflow implementations can improve performance of shared memory programs.•No single parallel programming paradigm is suitable for all DaSH benchmarks.
The current trend in development of parallel programming models is to combine different well established models into a single programming model in order to support efficient implementation of a wide range of real world applications. The dataflow model has particularly managed to recapture the interest of the research community due to its ability to express parallelism efficiently. Thus, a number of recently proposed hybrid parallel programming models combine dataflow and traditional shared memory models. Their findings have influenced the introduction of task dependency in the OpenMP 4.0 standard.
This article presents DaSH – the first comprehensive benchmark suite for hybrid dataflow and shared memory programming models. DaSH features 11 benchmarks, each representing one of the Berkeley dwarfs that capture patterns of communication and computation common to a wide range of emerging applications. DaSH also includes sequential and shared-memory implementations based on OpenMP and Intel TBB to facilitate easy comparison between hybrid dataflow implementations and traditional shared memory implementations based on work-sharing and/or tasks. Finally, we use DaSH to evaluate three different hybrid dataflow models, identify their advantages and shortcomings, and motivate further research on their characteristics. |
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| AbstractList | •Three important properties of DaSH are breadth, depth and portability.•The main strength of dataflow is the ability to eliminate unnecessary barriers.•Dataflow and shared memory implementations have comparable code complexity.•Dataflow implementations can improve performance of shared memory programs.•No single parallel programming paradigm is suitable for all DaSH benchmarks.
The current trend in development of parallel programming models is to combine different well established models into a single programming model in order to support efficient implementation of a wide range of real world applications. The dataflow model has particularly managed to recapture the interest of the research community due to its ability to express parallelism efficiently. Thus, a number of recently proposed hybrid parallel programming models combine dataflow and traditional shared memory models. Their findings have influenced the introduction of task dependency in the OpenMP 4.0 standard.
This article presents DaSH – the first comprehensive benchmark suite for hybrid dataflow and shared memory programming models. DaSH features 11 benchmarks, each representing one of the Berkeley dwarfs that capture patterns of communication and computation common to a wide range of emerging applications. DaSH also includes sequential and shared-memory implementations based on OpenMP and Intel TBB to facilitate easy comparison between hybrid dataflow implementations and traditional shared memory implementations based on work-sharing and/or tasks. Finally, we use DaSH to evaluate three different hybrid dataflow models, identify their advantages and shortcomings, and motivate further research on their characteristics. The current trend in development of parallel programming models is to combine different well established models into a single programming, model in order to support efficient implementation of a wide range of real world applications. The dataflow model has particularly managed to recapture the interest of the research community due to its ability to express parallelism efficiently. Thus, a number of recently proposed hybrid parallel programming models combine dataflow and traditional shared memory models. Their findings have influenced the introduction of task dependency in the OpenMP 4.0 standard.; This article presents DaSH - the first comprehensive benchmark suite for hybrid dataflow and shared memory programming models. DaSH features 11 benchmarks, each representing one of the Berkeley dwarfs that capture patterns of communication and computation common to a wide range of emerging applications. DaSH also includes sequential and shared-memory implementations based on OpenMP and Intel TBB to facilitate easy comparison between hybrid dataflow implementations and traditional shared memory implementations based on work-sharing and/or tasks. Finally, we use DaSH to evaluate three different hybrid dataflow models, identify their advantages and shortcomings, and motivate further research on their characteristics. Peer Reviewed |
| Author | Unsal, Osman S. Erić, Igor Cristal, Adrian Gajinov, Vladimir Ayguadé, Eduard Stipić, Srdjan |
| Author_xml | – sequence: 1 givenname: Vladimir surname: Gajinov fullname: Gajinov, Vladimir email: vladimir.gajinov@gmail.com organization: Barcelona Supercomputing Center, Spain – sequence: 2 givenname: Srdjan surname: Stipić fullname: Stipić, Srdjan organization: Barcelona Supercomputing Center, Spain – sequence: 3 givenname: Igor surname: Erić fullname: Erić, Igor organization: University of Belgrade, Serbia – sequence: 4 givenname: Osman S. surname: Unsal fullname: Unsal, Osman S. organization: Barcelona Supercomputing Center, Spain – sequence: 5 givenname: Eduard surname: Ayguadé fullname: Ayguadé, Eduard organization: Barcelona Supercomputing Center, Spain – sequence: 6 givenname: Adrian surname: Cristal fullname: Cristal, Adrian organization: Artificial Intelligence Research Institute, Spain |
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| SubjectTerms | Algorithm Architectures Arquitectura de computadors Arquitectures paral·leles Benchmark suite Dataflow Informàtica Parallel programming (Computer science) Programació en paral·lel (Informàtica) Programming model Shared memory Transactional memory Àrees temàtiques de la UPC |
| Title | DaSH: A benchmark suite for hybrid dataflow and shared memory programming models |
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