Title :
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
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;
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
DOI :
10.1109/AIMSEC.2011.6010978
