VTensor: Using Virtual Tensors to Build a Layout-Oblivious AI Programming Framework
文献类型：期刊论文
| 作者 | Yu, Feng2,3; Zhao, Jia-Cheng2,3; Cui, Hui-Min2,3; Feng, Xiao-Bing2,3; Xue, Jingling1 |
| 刊名 | JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY
 |
| 出版日期 | 2023-09-01 |
| 卷号 | 38期号:5页码:1074-1097 |
| 关键词 | artificial intelligence (AI) programming layout-oblivious tensor processing |
| ISSN号 | 1000-9000 |
| DOI | 10.1007/s11390-022-1457-6 |
| 英文摘要 | Tensors are a popular programming interface for developing artificial intelligence (AI) algorithms. Layout refers to the order of placing tensor data in the memory and will affect performance by affecting data locality; therefore the deep neural network library has a convention on the layout. Since AI applications can use arbitrary layouts, and existing AI systems do not provide programming abstractions to shield the layout conventions of libraries, operator developers need to write a lot of layout-related code, which reduces the efficiency of integrating new libraries or developing new operators. Furthermore, the developer assigns the layout conversion operation to the internal operator to deal with the uncertainty of the input layout, thus losing the opportunity for layout optimization. Based on the idea of polymorphism, we propose a layout-agnostic virtual tensor programming interface, namely the VTensor framework, which enables developers to write new operators without caring about the underlying physical layout of tensors. In addition, the VTensor framework performs global layout inference at runtime to transparently resolve the required layout of virtual tensors, and runtime layout-oriented optimizations to globally minimize the number of layout transformation operations. Experimental results demonstrate that with VTensor, developers can avoid writing layout-dependent code. Compared with TensorFlow, for the 16 operations used in 12 popular networks, VTensor can reduce the lines of code (LOC) of writing a new operation by 47.82% on average, and improve the overall performance by 18.65% on average. |
| 资助项目 | National Key Research and Development Program of China[2021ZD0110101] ; National Natural Science Foundation of China[62090024] ; National Natural Science Foundation of China[61872043] ; National Natural Science Foundation of China[61802368] ; Australian Research Council[DP180104069] ; Australian Research Council[DP210102409] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:001114345700008 |
| 出版者 | SPRINGER SINGAPORE PTE LTD |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/38459]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Cui, Hui-Min |
| 作者单位 | 1.Univ New South Wales, Sch Comp Sci & Engn, Sydney 1466, Australia 2.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 100080, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yu, Feng,Zhao, Jia-Cheng,Cui, Hui-Min,et al. VTensor: Using Virtual Tensors to Build a Layout-Oblivious AI Programming Framework[J]. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY,2023,38(5):1074-1097. |
| APA | Yu, Feng,Zhao, Jia-Cheng,Cui, Hui-Min,Feng, Xiao-Bing,&Xue, Jingling.(2023).VTensor: Using Virtual Tensors to Build a Layout-Oblivious AI Programming Framework.JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY,38(5),1074-1097. |
| MLA | Yu, Feng,et al."VTensor: Using Virtual Tensors to Build a Layout-Oblivious AI Programming Framework".JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 38.5(2023):1074-1097. |
入库方式： OAI收割
来源：计算技术研究所
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