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      •   BUIR Home
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      • Bilkent Theses
      • Theses - Department of Computer Engineering
      • Dept. of Computer Engineering - Master's degree
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      Automatic code optimization using graph neural networks

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      Embargo Lift Date: 2023-07-23
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      3.4 Mb
      Author(s)
      Peker, Melih
      Advisor
      Öztürk, Özcan
      Date
      2023-01
      Publisher
      Bilkent University
      Language
      English
      Type
      Thesis
      Item Usage Stats
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      Abstract
      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 rep-resenting 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.
      Keywords
      Code optimization
      Graph neural networks
      GCC
      Compilers
      Permalink
      http://hdl.handle.net/11693/111195
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      • Dept. of Computer Engineering - Master's degree 566
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