Title :
An improved GPU MapReduce framework for data intensive applications
Author :
Nitu, Razvan ; Apostol, Elena ; Cristea, Valentin
Author_Institution :
Fac. of Autom. Control & Comput., Univ. Politeh. of Bucharest, Bucharest, Romania
Abstract :
The MapReduce paradigm is one of the best solutions for implementing distributed applications which perform intensive data processing. In terms of performance regarding this type of applications, MapReduce can be improved by adding GPU capabilities. In this context, the GPU clusters for large scale computing can bring a considerable increase in the efficiency and speedup of data intensive applications. In this article we present a framework for executing MapReduce using GPU programming. We describe several improvements to the concept of GPU MapReduce and we compare our solution with others.
Keywords :
data handling; graphics processing units; GPU MapReduce framework; GPU clusters; GPU programming; MapReduce paradigm; data intensive applications; distributed applications; intensive data processing; large scale computing; Graphics processing units; Kernel; Mars; Memory management; Parallel processing; Process control; Vectors; GPU MapReduce; Hadoop; OpenCL; shared memory;
Conference_Titel :
Intelligent Computer Communication and Processing (ICCP), 2014 IEEE International Conference on
Conference_Location :
Cluj Napoca
Print_ISBN :
978-1-4799-6568-7
DOI :
10.1109/ICCP.2014.6937021