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خانه مقالات مقالات کامپیوتر هادوپ A Unified MapReduce Domain-Specific Language for Distributed and Shared Memory Architectures
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A Unified MapReduce Domain-Specific Language for Distributed and Shared Memory Architectures

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MapReduce is a suitable and ecient parallel programming pattern for processing big data analysis. In recent
years, many frameworks/languages have implemented this pattern to achieve high performance in data mining applications, particularly for distributed memory architectures (e.g., clusters).Nevertheless, the industry of processors is now able to o er powerful processing on single machines (e.g., multi-core). Thus, these applications may address the parallelism in another architectural level.

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ABSTRACT

 MapReduce is a suitable and ecient parallel programming pattern for processing big data analysis. In recent
years, many frameworks/languages have implemented this pattern to achieve high performance in data mining applications, particularly for distributed memory architectures (e.g., clusters).Nevertheless, the industry of processors is now able to o er powerful processing on single machines (e.g., multi-core). Thus, these applications may address the parallelism in another architectural level. The target problems of this paper are code reuse
and programming e ort reduction since current solutions do not provide a single interface to deal with these two architectural levels. Therefore, we propose a unified domain-specific language in conjunction with transformation rules for code generation for Hadoop and Phoenix++. We selected these frameworks as state-of-the-art MapReduce implementations for distributed and shared memory architectures, respectively. Our solution achieves a programming e ort reduction from 41.84% and up to 95.43% without significant performance losses (below the threshold of3%)  compared to Hadoop and Phoenix ++

INTRODUCTION
An exponential volume of data is generated by a variety of fields worldwide, for example, social networks, governments, health care, stock market, among others. The socalled Big Data is addressed by data analysis applications, which may imply high computational costs. Consequently,high-performance computing is needed to process all data in time. Google initially proposed a solution for improving the performance of these application’s domain, by combining Map and Reduce operations as a single parallel pattern named MapReduce [5]. Since then, the MapReduce has originated many implementations by both industry and academic research.Some of them have achieved great importance, such as Hadoop1, which is suited for programming in large clusters architectures, and Phoenix++ [13] for programming in multicore architectures

Year : 2015

By : Daniel Adornes, Dalvan Griebler, Cleverson Ledur, Luiz Gustavo Fernandes

File Information : English Language /6 Page /Size : 163 K

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 سال : 2015

کاری از : Daniel Adornes, Dalvan Griebler, Cleverson Ledur, Luiz Gustavo Fernandes

اطلاعات فایل : زبان انگلیسی /6 صفحه /حجم : 163 K

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