Accelerating MatLab code using GPU: A review of tools and strategies

Author

Zhang, Baida ; Xu, Shuai ; Zhang, Feng ; Bi, Yuan ; Huang, Linqi

Author_Institution

Sch. of Comput., Nat. Univ. of Defense Technol., Changsha, China

fYear

2011

fDate

8-10 Aug. 2011

Firstpage

1875

Lastpage

1878

Abstract

Lots of toolboxes of accelerating MatLab using GPU are available now[1], but, users are confused by which toolbox is best suitable for a particular task. Three toolboxes-Jacket, GPUmat, and Parallel Computing Toolbox of MatLab are selected. For each toolbox, its advantages and pitfalls are reviewed, with an aim to allow the reader to identify which toolbox is appropriate for a given task. Strategies of whether a function should execute on GPU are given after a formula analysis. The analysis is also a framework for program automatically decides which function is cost-efficient to execute on GPU. A series of benchmark of different types of computing, including data transfer between GPU and CPU, data matrix Generation, matrix operation and GPU functions were tested in all three toolboxes. And the results show that Jacket is the best one. Some advices to improve the performance of toolboxes are given in the end.

Keywords

computer graphic equipment; coprocessors; mathematics computing; parallel processing; GPUmat; Jacket; MatLab code; data matrix generation; data transfer; matrix operation; parallel computing toolbox; Acceleration; Computer architecture; Conferences; Graphics processing unit; Licenses; MATLAB; Parallel processing;

fLanguage

English

Publisher

ieee

Conference_Titel

Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC), 2011 2nd International Conference on

Conference_Location

Deng Leng

Print_ISBN

978-1-4577-0535-9

Type

conf

DOI

10.1109/AIMSEC.2011.6010978

Filename

6010978