Browsing by Author "Peker, Melih"

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    Atatürk ve Türk musikisi
    (Bilkent University, 2016) Peker, Melih; Kemal Giregiz, Ali; Uyanık, Ata Oğuzhan; Yiğit Safi, Berke; Çelik, Doruk
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    Automatic code optimization using graph neural networks
    (2023-01) Peker, Melih
    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.
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    Automatic selection of compiler optimizations by machine learning
    (IEEE - Institute of Electrical and Electronics Engineers, 2023-08-28) Peker, Melih; Öztürk, Özcan; Yıldırım, S.; Uluyağmur Öztürk, M.
    Many widely used telecommunications applications have extremely long run times. Therefore, faster and more efficient execution of these codes on the same hardware is important in critical telecommunication applications such as base stations. Compilers greatly affect the properties of the executable program to be created. It is possible to change properties such as compilation speed, execution time, power consumption and code size using compiler flags. This study aims to find the set of flags that will provide the shortest run time among hundreds of compiler flag combinations in GCC using code flow analysis, loop analysis and machine learning methods without running the program.