M.S. THESIS DEFENSE PRESENTATION Title: AUTOMATIC CODE OPTIMIZATION USING GRAPH NEURAL NETWORKS
Melih Peker, M.Sc. Student in Computer Engineering
Advisor: Prof. Dr. Özcan Öztürk
Member: Assoc.Prof. Ercüment Çiçek
Prof. Dr. Süleyman Tosun (Hacettepe Uni.)
The Seminar will be on Thursday, December 29, 2022, 16:00 İstanbul
This is an online seminar. To request the event link, please send a message to department.
Compilers provide hundreds of optimization options, and choosing a good optimization sequence is a complex and time-consuming task. It requires extensive effort and expert input to select a good set of optimization flags. Therefore, there is a lot of research focused on finding optimizations automatically. While most of this research considers using static, spatial, or dynamic features, some of the latest research directly applied deep neural networks on source code. We combined the static features, spatial features, and deep neural networks by representing source code as graphs and trained Graph Neural Network (GNN) for automatically finding suitable optimization flags.
We chose eight binary optimization flags and two benchmark suites, Polybench and cBench. We created a dataset of 12000 graphs using 256 optimization flag combinations on 47 benchmarks. We trained and tested our model using these benchmarks, and our results show that we can achieve a maximum of 48.6% speed-up compared to the case where all optimization flags are enabled.