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 DING Ke,TAN Ying.A review on general purpose computing on GPUs and its applications in computational intelligence[J].CAAI Transactions on Intelligent Systems,2015,10(01):1-11.[doi:10.3969/j.issn.1673-4785.201403072]





A review on general purpose computing on GPUs and its applications in computational intelligence
丁科12 谭营12
1. 北京大学 机器感知与智能教育部重点实验室, 北京 100871;
2. 北京大学 信息科学技术学院, 北京 100871
DING Ke12 TAN Ying12
1. Key Laboratory of Machine Perception (MOE), Peking University, Beijing 100871, China;
2. School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China
computational intelligenceswarm intelligenceevolutionary algorithmsmachine learningdeep learninggraphics processing unit (GPU)general purpose computing on GPUsheterogonous computinghigh performance computing (HPC)
The GPU enjoys the characteristics of high parallelism, low energy consumption and cheap price. Compared with the traditional CPU platform, it is especially suitable for tasks with high data parallelism. GPU computing has come into the mainstream of high performance computation (HPC) due to the emerging of development platforms like CUDA and OpenCL. The GPU’s enormous computational power greatly promotes computational intelligence. A great success has been achieved in the fields such as deep learning and swarm intelligence optimization, and several breakthroughs have been seen in image, and speech recognition because of GPU. Though suffering some drawbacks, GPUs provide common people and small institutions with enormous computing power. This has changed the set-up of scientific computing and programming model because it could only be provided by expensive supercomputers. To help researchers in the field of computational intelligence better utilize GPUs, a detailed survey of GPGPU is given in this paper。First, the characteristics and advantages of GPUs against CPUs are presented. Then we briefly review the development of GPU hardware followed by a survey of the evolution of development tools for GPGPU; special attention is drawn to two major platforms, CUDA and OpenCL. We end this paper with our perspectives of the challenges and trends of GPGPU. We point out that embedding and cluster are two major trends for GPGPU and as both academia and industry continue to see increasing progress in artificial intelligence, the GPU will be more widely used in more domains.


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作者简介:丁科,男,1989年生,博士研究生,主要研究方向为群体智能、GPU通用计算、并行编程和机器学习;谭营,男,1964年生,教授,博士生导师,主要研究方向为计算智能、群体智能、机器学习、人工免疫系统、智能信息处理及信息安全应用。担任IJCIPT主编,IJSIR副主编,IEEE Trans on Cybernetics副主编等,IEEE Senior Member, IEEE CIS-ETTC委员,ICSI系列会议大会主席。主持国家“863”计划、国家自然科学基金、国际合作交流等科研项目30余项。获得2009年度国家自然科学二等奖,是中科院百人计划入选者。获国家发明专利授权3项,发表学术论文260余篇,出版专著5部。
更新日期/Last Update: 2015-06-16