GRAPHSPY: Fused Program Semantic Embedding through Graph Neural Networks for Memory Efficiency
Document Type
Conference Proceeding
Publication Date
12-5-2021
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.
Publication Title
Proceedings - Design Automation Conference
ISBN
9781665432740
Recommended Citation
Guo, Y.,
Li, P.,
Luo, Y.,
Wang, X.,
&
Wang, Z.
(2021).
GRAPHSPY: Fused Program Semantic Embedding through Graph Neural Networks for Memory Efficiency.
Proceedings - Design Automation Conference,
2021-December, 1045-1050.
http://doi.org/10.1109/DAC18074.2021.9586120
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/15510