Accumulative Computation on MapReduce

• Item type: Trans(1)
• 公開日: 2014-01-22
• タイトル: Accumulative Computation on MapReduce
• タイトル / 言語: en
• タイトル: Accumulative Computation on MapReduce
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [通常論文] parallel programming, MapReduce, accumulative computation, automatic parallelization, algorithmic skeleton
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_6501
• 資源タイプ: journal article
• 著者所属: The Graduate University for Advanced Studies／National Institute of Informatics
• 著者所属: Kyushu Institute of Technology
• 著者所属: Kochi University of Technology
• 著者所属: National Institute of Informatics
• 著者所属(英): en
• 著者所属(英): The Graduate University for Advanced Studies / National Institute of Informatics
• 著者所属(英): en
• 著者所属(英): Kyushu Institute of Technology
• 著者所属(英): en
• 著者所属(英): Kochi University of Technology
• 著者所属(英): en
• 著者所属(英): National Institute of Informatics
• 著者名: Yu, Liu; Kento, Emoto; Kiminori, Matsuzaki; Zhenjiang, Hu
• 著者名(英): Yu, Liu; Kento, Emoto; Kiminori, Matsuzaki; Zhenjiang, Hu
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: MapReduce programming model attracts a lot of enthusiasm among both industry and academia, largely because it simplifies the implementations of many data parallel applications. In spite of the simplicity of the programming model, there are many applications that are hard to be implemented by MapReduce, due to their innate characters of computational dependency. In this paper we propose a new approach of using the programming pattern accumulate over MapReduce, to handle a large class of problems that cannot be simply divided into independent sub-computations. Using this accumulate pattern, many problems that have computational dependency can be easily expressed, and then the programs will be transformed to MapReduce programs executed on large clusters. Users without much knowledge of MapReduce can also easily write programs in a sequential manner but finally obtain efficient and scalable MapReduce programs. We describe the programming interface of our accumulate framework and explain how to transform a user-specified accumulate computation to an efficient MapReduce program. Our experiments and evaluations illustrate the usefulness and efficiency of the framework.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: MapReduce programming model attracts a lot of enthusiasm among both industry and academia, largely because it simplifies the implementations of many data parallel applications. In spite of the simplicity of the programming model, there are many applications that are hard to be implemented by MapReduce, due to their innate characters of computational dependency. In this paper we propose a new approach of using the programming pattern accumulate over MapReduce, to handle a large class of problems that cannot be simply divided into independent sub-computations. Using this accumulate pattern, many problems that have computational dependency can be easily expressed, and then the programs will be transformed to MapReduce programs executed on large clusters. Users without much knowledge of MapReduce can also easily write programs in a sequential manner but finally obtain efficient and scalable MapReduce programs. We describe the programming interface of our accumulate framework and explain how to transform a user-specified accumulate computation to an efficient MapReduce program. Our experiments and evaluations illustrate the usefulness and efficiency of the framework.
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AA11464814
• 書誌情報: 情報処理学会論文誌プログラミング（PRO）; 巻 7, 号 1, p. 18-27, 発行日 2014-01-22
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-7802
• 出版者 / 言語: ja
• 出版者: 情報処理学会