Skip to Main Content
Frequently Asked Questions
Submit an ETD
Global Search Box
Need Help?
Keyword Search
Participating Institutions
Advanced Search
School Logo
Files
File List
A Workload Balanced MapReduce Framework on GPU Platforms.pdf (3.29 MB)
ETD Abstract Container
Abstract Header
A Workload Balanced MapReduce Framework on GPU Platforms
Author Info
Zhang, Yue
Permalink:
http://rave.ohiolink.edu/etdc/view?acc_num=wright1450180042
Abstract Details
Year and Degree
2015, Master of Science in Computer Engineering (MSCE), Wright State University, Computer Engineering.
Abstract
The MapReduce framework is a programming model proposed by Google to process large datasets. It is an efficient framework that can be used in many areas, such as social network, scientific research, electronic business, etc. Hence, more and more MapReduce frameworks are implemented on different platforms, including Phoenix (based on multicore CPU), MapCG (based on GPU), and StreamMR (based on GPU). However, these MapReduce frameworks have limitations, and they cannot handle the collision problem in the map phase, and the unbalanced workload problems in the reduce phase. To improve the performance of the MapReduce framework on GPGPUs, in this thesis, a workload balance MapReduce framework (B_MapCG) on GPUs is proposed and developed based on the MapCG framework, to reduce the number of collisions while inserting key-value pairs in the map phase, and to handle the unbalanced workload problems in the reduce phase. The proposed B_MapCG framework is evaluated on the Tesla K40 GPU with four benchmarks and eight different datasets. The experimental results showed that the B_MapCG framework achieved big performance improvements for all the four test benchmarks both in the map phase and the reduce phase compared with MapCG.
Committee
Meilin Liu, Ph.D. (Advisor)
Jack Jean, Ph.D. (Committee Member)
Travis Doom, Ph.D. (Committee Member)
Pages
66 p.
Subject Headings
Computer Engineering
Keywords
Computer Engineering
Recommended Citations
Refworks
EndNote
RIS
Mendeley
Citations
Zhang, Y. (2015).
A Workload Balanced MapReduce Framework on GPU Platforms
[Master's thesis, Wright State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=wright1450180042
APA Style (7th edition)
Zhang, Yue.
A Workload Balanced MapReduce Framework on GPU Platforms.
2015. Wright State University, Master's thesis.
OhioLINK Electronic Theses and Dissertations Center
, http://rave.ohiolink.edu/etdc/view?acc_num=wright1450180042.
MLA Style (8th edition)
Zhang, Yue. "A Workload Balanced MapReduce Framework on GPU Platforms." Master's thesis, Wright State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=wright1450180042
Chicago Manual of Style (17th edition)
Abstract Footer
Document number:
wright1450180042
Download Count:
948
Copyright Info
© 2015, all rights reserved.
This open access ETD is published by Wright State University and OhioLINK.