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A Survey on Accelerated Mapreduce for Hadoop


Affiliations
1 School of Information Technology, RGPV, Bhopal, India
2 Department of IT, UIT RGPV, Bhopal, India
 

Big Data is defined by 3Vs which stands for variety, volume and velocity. The volume of data is very huge, data exists in variety of file types and data grows very rapidly. Big data storage and processing has always been a big issue. Big data has become even more challenging to handle these days. To handle big data high performance techniques have been introduced. Several frameworks like Apache Hadoop has been introduced to process big data. Apache Hadoop provides map/reduce to process big data. But this map/reduce can be further accelerated. In this paper a survey has been performed for map/reduce acceleration and energy efficient computation in quick time.

Keywords

Map Reduce, GPU Computation And Open CL.
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  • A Survey on Accelerated Mapreduce for Hadoop

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Authors

Jyotindra Tiwari
School of Information Technology, RGPV, Bhopal, India
Mahesh Pawar
Department of IT, UIT RGPV, Bhopal, India
Anjana Pandey
Department of IT, UIT RGPV, Bhopal, India

Abstract


Big Data is defined by 3Vs which stands for variety, volume and velocity. The volume of data is very huge, data exists in variety of file types and data grows very rapidly. Big data storage and processing has always been a big issue. Big data has become even more challenging to handle these days. To handle big data high performance techniques have been introduced. Several frameworks like Apache Hadoop has been introduced to process big data. Apache Hadoop provides map/reduce to process big data. But this map/reduce can be further accelerated. In this paper a survey has been performed for map/reduce acceleration and energy efficient computation in quick time.

Keywords


Map Reduce, GPU Computation And Open CL.

References





DOI: https://doi.org/10.13005/ojcst%2F10.03.07