東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁 到查詢結果 [ author_sort:"zhu, jie." ]

切換: 標籤 | MARC模式 | ISBD

GPU-In-Hadoop: MapReduce on Distribu...

Zhu, Jie.

FindBook

Google Book

Amazon

博客來

GPU-In-Hadoop: MapReduce on Distributed Heterogeneous Platforms.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	GPU-In-Hadoop: MapReduce on Distributed Heterogeneous Platforms./
作者:	Zhu, Jie.
面頁冊數:	74 p.
附註:	Source: Masters Abstracts International, Volume: 52-06.
Contained By:	Masters Abstracts International52-06(E).
標題:	Computer Science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1555438
ISBN:	9781303878909

GPU-In-Hadoop: MapReduce on Distributed Heterogeneous Platforms.
Zhu, Jie.

GPU-In-Hadoop: MapReduce on Distributed Heterogeneous Platforms. - 74 p.

Source: Masters Abstracts International, Volume: 52-06.

Thesis (M.S.)--Arkansas State University, 2014.

There are four main challenges that have arisen as the scales of high performance distributed systems grow. Those challenges are the resilience to failure, the programmability, the heterogeneity, and the energy efficiency of those systems. Accomplishing all four without sacrificing performance requires a rethinking of legacy distributed programming models processors and homogeneous clusters. In this paper, the Hadoop system is integrated with CUDA to implement the utilization of heterogeneous processors in a distributed system. This process is achieved by exploiting the implicit data parallelism of mapper and reducer in the Hadoop MapReduce. Combining Hadoop with CUDA provides three excellent merits. First, both of Hadoop and CUDA are easy-to-learn and flexible application language. Second, Hadoop produces the reliability guarantees and distributed file system. Third, the low power consumption and performance acceleration of parallel processors are provided by CUDA. Four approaches will be presented using JCUDA, JNI, and Hadoop Pipes, as well as Hadoop streaming, to extend to Hadoop the support execution of user-written kernels on GPU.

ISBN: 9781303878909Subjects--Topical Terms:

626642
Computer Science.

GPU-In-Hadoop: MapReduce on Distributed Heterogeneous Platforms.
LDR:01981nam a2200277 4500 001 1965687
005 20141029122152.5
008 150210s2014 ||||||||||||||||| ||eng d
020 $a 9781303878909
035 $a (MiAaPQ)AAI1555438
035 $a AAI1555438
040 $a MiAaPQ $c MiAaPQ
100 1 $a Zhu, Jie. $3 2102381
245 1 0 $a GPU-In-Hadoop: MapReduce on Distributed Heterogeneous Platforms.
300 $a 74 p.
500 $a Source: Masters Abstracts International, Volume: 52-06.
500 $a Adviser: Hai Jiang.
502 $a Thesis (M.S.)--Arkansas State University, 2014.
520 $a There are four main challenges that have arisen as the scales of high performance distributed systems grow. Those challenges are the resilience to failure, the programmability, the heterogeneity, and the energy efficiency of those systems. Accomplishing all four without sacrificing performance requires a rethinking of legacy distributed programming models processors and homogeneous clusters. In this paper, the Hadoop system is integrated with CUDA to implement the utilization of heterogeneous processors in a distributed system. This process is achieved by exploiting the implicit data parallelism of mapper and reducer in the Hadoop MapReduce. Combining Hadoop with CUDA provides three excellent merits. First, both of Hadoop and CUDA are easy-to-learn and flexible application language. Second, Hadoop produces the reliability guarantees and distributed file system. Third, the low power consumption and performance acceleration of parallel processors are provided by CUDA. Four approaches will be presented using JCUDA, JNI, and Hadoop Pipes, as well as Hadoop streaming, to extend to Hadoop the support execution of user-written kernels on GPU.
590 $a School code: 1231.
650 4 $a Computer Science. $3 626642
650 4 $a Design and Decorative Arts. $3 1024640
690 $a 0984
690 $a 0389
710 2 $a Arkansas State University. $b Computer Science. $3 1680831
773 0 $t Masters Abstracts International $g 52-06(E).
790 $a 1231
791 $a M.S.
792 $a 2014
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1555438