期刊名称：International Journal of Computer Information Systems and Industrial Management Applications
印刷版ISSN：2150-7988
电子版ISSN：2150-7988
出版年度：2011
卷号：3
页码：355-362
出版社：Machine Intelligence Research Labs (MIR Labs)
摘要：Graphics Processing Units (GPUs) placed at our dis- posal an unprecedented computational-power, largely surpass- ing the performance of cutting-edge CPUs (Central Process- ing Units). The high-parallelism inherent to the GPU makes this device especially well-suited to address Machine Learn- ing (ML) problems with prohibitively computational intensive tasks. Nevertheless, few ML algorithms have been implemented on the GPU and most are not openly shared, posing difficul- ties for researchers and engineers aiming to develop GPU-based systems. To mitigate this problem, we propose the creation of an open source GPU Machine Learning Library (GPUMLib) that aims to provide the building blocks for the development of ef- ficient GPU ML software. Experimental results on benchmark datasets show that the algorithms already implemented yield significant time savings over the CPU counterparts.
