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World Symposium on Web Applications and Networking (WSWAN)

作者： Gannouni, Sofien King Saud Univ Coll Comp & Informat Sci Dept Comp Sci Riyadh Saudi Arabia

ISBN: (纸本)9781479981724

The General Purpose GPU computational model changes the way parallel processing can be achieved. It is becoming more attractive to carry out parallel tasks on GPU devices. The sequential part of the application runs on the CPU whereas the computationally-intensive part is accelerated by the GPU. GPUs provide a multi-threaded high level of parallelism with hundreds of cores. For high performance computing developers, the GPU cores offer a higher magnitude order of raw computation power than CPU. In this paper we propose an efficient parallel programming framework based on the GPU devices. This framework adopts the Gamma formalism as an abstract model for making parallelism less difficult. The software developer has only to specify the action to be curried-out on any atomic portion of data. The framework will then run the given action simultaneously on the GPU cores.

关键词： parallel programming framework Gamma formalism Reduction GPU CUDA

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SEIP： System for Efficient Image Processing on Distributed Platform

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Journal of Computer Science & Technology 2015年第6期30卷 1215-1232页

作者：刘弢刘轶李钦王香荣高飞朱延超钱德沛 School of Computer Science and Engineering Beihang University Beijing 100191 China

Nowadays, there exist numerous images in the Internet, and with the development ot cloud compuung ano big data applications, many of those images need to be processed for different kinds of applications by using specific image processing algorithms. Meanwhile, there already exist many kinds of image processing algorithms and their variations, while new algorithms are still emerging. Consequently, an ongoing problem is how to improve the efficiency of massive image processing and support the integration of existing implementations of image processing algorithms into the systems. This paper proposes a distributed image processing system named SEIP, which is built on Hadoop, and employs extensible in- node architecture to support various kinds of image processing algorithms on distributed platforms with GPU accelerators. The system also uses a pipeline-based h＇amework to accelerate massive image file processing. A demonstration application for image feature extraction is designed. The system is evaluated in a small-scale Hadoop cluster with GPU accelerators, and the experimental results show the usability and efficiency of SEIP.

关键词： big data distributed system image processing GPU parallel programming framework

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

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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 HIERARCHICAL SYSTEM VIEW AND ITS USE IN THE DATA DISTRIBUTION OF COMPOSED CONTAINERS IN STAPL

A HIERARCHICAL SYSTEM VIEW AND ITS USE IN THE DATA DISTRIBUT...

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作者： Shen, Junjie Texas A&M University

In parallel programming, a concurrent container usually distributes its elements to all processing units (locations) equally to maximize the processing ability. However, this distribution strategy does not perform well when we apply nested parallel func- tions on a composed concurrent container, such as a concurrent vector of vectors or a concurrent map of lists. The distribution of the inner concurrent containers across the system will mess up the locality of the elements in the composed containers, generating a lot of inter-process communication when the nested parallel operations are called to access the container’s elements. As the hierarchy in modern high per- formance computing (HPC) systems become large and complex, a large amount of inter-process communication, especially those between two remote processing units (such as two cores on different nodes), will have dramatic negative impact on the performance of the parallel applications. In this thesis, we introduce a hierarchical system view that represents the topol- ogy of the processing units in a HPC system, and use it to guide the distribution of the composed concurrent containers. It reduces the number of processing elements involved in storing in the inner concurrent containers, which reduces memory usage and improves construction time. It also reduces the amount of inter-process com- munication by improving the locality of the elements when we apply nested parallel functions on a composed concurrent container. To evaluate our approach, we implement two concurrent associative multi-key containers, multimap and multiset, in the Standard Template Adaptive parallel Li- brary (STAPL), and use the hierarchical system view on the distribution of composed 2D and 3D containers. Finally, we show great improvement on both the construction time and the execution time of the nested parallel functions with various numbers of cores and hierarchies.

关键词： Concurrent data structure hierarchical distribution parallel programming framework Thesis

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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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