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Paving the way towards high-level parallel pattern interfaces for data stream processing

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2018年 87卷 228-241页

作者： del Rio Astorga, David Dolz, Manuel F. Fernandez, Javier Daniel Garcia, J. Univ Carlos III Madrid Comp Sci & Engn Dept Leganes 28911 Spain

The emergence of the Internet of Things (IoT) data stream applications has posed a number of new challenges to existing infrastructures, processing engines, and programming models. In this sense, high-level interfaces, encapsulating algorithmic aspects in pattern-based constructions, have considerably reduced the development and parallelization efforts of this type of applications. An example of parallel pattern interface is GRPPI, a C++ generic high-level library that acts as a layer between developers and existing parallel programming frameworks, such as C++ threads, OpenMP and Intel TBB. In this paper, we complement the basic patterns supported by GRPPI with the new stream operators Split-Join and Window, and the advanced parallel patterns Stream-Pool, Windowed-Farm and Stream-Iterator for the aforementioned back ends. Thanks to these new stream operators, complex compositions among streaming patterns can be expressed. On the other hand, the collection of advanced patterns allows users to tackle some domain-specific applications, ranging from the evolutionary to the real-time computing areas, where compositions of basic patterns are not capable of fully mimicking the algorithmic behavior of their original sequential codes. The experimental evaluation of the new advanced patterns and the stream operators on a set of domain-specific use-cases, using different back ends and pattern-specific parameters, reports considerable performance gains with respect to the sequential versions. Additionally, we demonstrate the benefits of the GRPPI pattern interface from the usability, flexibility and readability points of view. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Data stream processing Parallel programming framework Stream operator Domain-specific parallel pattern high-level api

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Supporting Advanced Patterns in GRPPI, a Generic Parallel Pattern Interface

Supporting Advanced Patterns in GRPPI, a Generic Parallel Pa...

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23rd International Conference on Parallel and Distributed Computing (Euro-Par)

作者： del Rio Astorga, David Dolz, Manuel F. Fernandez, Javier Daniel Garcia, J. Univ Carlos III Madrid Comp Sci & Engn Dept Leganes 28911 Spain

ISBN: (纸本)9783319751788;9783319751771

The emergence of generic interfaces, encapsulating algorithmic aspects in pattern-based constructions, has greatly alleviated the development of data-intensive and stream-processing applications. In this paper, we complement the basic patterns supported by GRPPI, a C++ General and Reusable Parallel Pattern Interface of the state-of-the-art, with the advanced parallel patterns Pool, Windowed-Farm, and Stream-Iterator. This collection of advanced patterns is basically oriented to some domain-specific applications, ranging from the evolutionary to the real-time computing areas, where compositions of basic patterns are not capable of fully mimicking algorithmic behavior of their original sequential codes. The experimental evaluation of the advanced patterns on a set of domain-specific use-cases, using different back-ends (C++ Threads, OpenMP and Intel TBB) and pattern-specific parameters, reports remarkable performance gains. We also demonstrate the benefits of the GRPPI pattern interface from the usability and flexibility points of view.

关键词： Parallel programming framework Domain-specific parallel pattern Data and stream computing high-level api

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A generic parallel pattern interface for stream and data processing

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2017年第24期29卷 e4175.1-e4175.12页

作者： del Rio Astorga, David Dolz, Manuel F. Fernandez, Javier Daniel Garcia, J. Univ Carlos III Madrid Comp Sci & Engn Dept Leganes 28911 Spain

Current parallel programming frameworks aid developers to a great extent in implementing applications that exploit parallel hardware resources. Nevertheless, developers require additional expertise to properly use and tune them to operate efficiently on specific parallel platforms. On the other hand, porting applications between different parallel programming models and platforms is not straightforward and demands considerable efforts and specific knowledge. Apart from that, the lack of high-level parallel pattern abstractions, in those frameworks, further increases the complexity in developing parallel applications. To pave the way in this direction, this paper proposes GRPPI, a generic and reusable parallel pattern interface for both stream processing and data-intensive C++ applications. GRPPI accommodates a layer between developers and existing parallel programming frameworks targeting multi-core processors, such as C++ threads, OpenMP and Intel TBB, and accelerators, as CUDA Thrust. Furthermore, thanks to its high-level C++ application programming interface and pattern composability features, GRPPI allows users to easily expose parallelism via standalone patterns or patterns compositions matching in sequential applications. We evaluate this interface using an image processing use case and demonstrate its benefits from the usability, flexibility, and performance points of view. Furthermore, we analyze the impact of using stream and data pattern compositions on CPUs, GPUs and heterogeneous configurations.

关键词： high-level api parallel pattern parallel programming framework stream processing

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