Close

Presentation

GraphSpy: Fused Program Semantic Embedding through Graph Neural Networks for Memory Efficiency
Time
Location
Event Type
Research Manuscript
Virtual Programs
Hosted in Virtual Platform
Keywords
AI/ML System Design
Topics
Design
DescriptionProduction 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.