Date of Award
8-16-2013
Document Type
Thesis
Degree Name
Computer Science, MS
First Advisor
Hai Jiang
Committee Members
E. Hammerand; Hung-Chi Su; Jeff Jenness
Abstract
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.
Rights Management
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Chen, Yi, "GPU-Based Mapreduce Schemes for Big Data Processing" (2013). Student Theses and Dissertations. 816.
https://arch.astate.edu/all-etd/816
