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GPU-based mapreduce schemes for big ...

Chen, Yi.

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GPU-based mapreduce schemes for big data processing.

Record Type:	Electronic resources : Monograph/item
Title/Author:	GPU-based mapreduce schemes for big data processing./
Author:	Chen, Yi.
Description:	165 p.
Notes:	Source: Masters Abstracts International, Volume: 52-01.
Contained By:	Masters Abstracts International52-01(E).
Subject:	Computer Science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1541458
ISBN:	9781303242366

GPU-based mapreduce schemes for big data processing.
Chen, Yi.

GPU-based mapreduce schemes for big data processing. - 165 p.

Source: Masters Abstracts International, Volume: 52-01.

Thesis (M.S.)--Arkansas State University, 2013.

This item must not be sold to any third party vendors.

MapReduce programming model and its implementations have simplified many par-allel applications. Because of the raising demand of higher computing performance, Graphics Processing Units (GPU) has been used to accelerate MapReduce in several stud-ies. Different from CPU, high GPU utilization requires not only descent parallel algo-rithm but also careful considerations of hardware details. This paper describes the devel-opment path of our MapReduce system from single GPU to multiple GPUs. Utilization of each GPU is promoted by using new GPU features such as streams and Hyper-Q. Fur-thermore, several scheduling schemes are designed to avoid blocked GPU operations. To address the challenge of Big Data, our MapReduce system handles large data sets that ex-ceed GPU and even CPU memory. Experimental results show the performance im-provement and increased scalability gained from each acceleration technique. Although our current work is specific to MapReduce, many underlying ideas are also applicable to acceleration of other GPU applications.

ISBN: 9781303242366Subjects--Topical Terms:

626642
Computer Science.

GPU-based mapreduce schemes for big data processing.
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500 $a Source: Masters Abstracts International, Volume: 52-01.
500 $a Adviser: Hai Jiang.
502 $a Thesis (M.S.)--Arkansas State University, 2013.
506 $a This item must not be sold to any third party vendors.
520 $a MapReduce programming model and its implementations have simplified many par-allel applications. Because of the raising demand of higher computing performance, Graphics Processing Units (GPU) has been used to accelerate MapReduce in several stud-ies. Different from CPU, high GPU utilization requires not only descent parallel algo-rithm but also careful considerations of hardware details. This paper describes the devel-opment path of our MapReduce system from single GPU to multiple GPUs. Utilization of each GPU is promoted by using new GPU features such as streams and Hyper-Q. Fur-thermore, several scheduling schemes are designed to avoid blocked GPU operations. To address the challenge of Big Data, our MapReduce system handles large data sets that ex-ceed GPU and even CPU memory. Experimental results show the performance im-provement and increased scalability gained from each acceleration technique. Although our current work is specific to MapReduce, many underlying ideas are also applicable to acceleration of other GPU applications.
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