| Title | GRAPHSPY: Fused Program Semantic Embedding through Graph Neural Networks for Memory Efficiency |
| Authors | Guo, Yixin Li, Pengcheng Luo, Yingwei Wang, Xiaolin Wang, Zhenlin |
| Affiliation | Peking Univ, Beijing, Peoples R China Alibaba Damo Acad, Sunnyvale, CA 94085 USA Peng Cheng Lab, Shenzhen, Peoples R China Michigan Tech, Houghton, MI USA |
| Issue Date | 2021 |
| Publisher | 2021 58TH ACM/IEEE DESIGN AUTOMATION CONFERENCE (DAC) |
| Abstract | Production software oftentimes suffers from unnecessary memory inefficiencies caused by inappropriate use of data structures, programming abstractions, or conservative compiler optimizations. Unfortunately, existing works often adopt a whole-program fine-grained monitoring method incurring incredibly high overhead. This work proposes a learning-aided approach to identify unnecessary memory operations, by applying several prevalent graph neural network models to extract program semantics with respect to program structure, execution semantics and dynamic states. Results show that the proposed approach captures memory inefficiencies with high accuracy of 95.27% and only around 17% overhead of the state-of-the-art. |
| URI | http://hdl.handle.net/20.500.11897/638964 |
| ISBN | 978-1-6654-3274-0 |
| ISSN | 0738-100X |
| DOI | 10.1109/DAC18074.2021.9586120 |
| Indexed | EI CPCI-S(ISTP) |
| Appears in Collections: | 待认领 |
