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Supersonic: Learning to Generate Source Code Optimizations in C/C plus
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AI
DOI：10.1109/TSE.2024.3423769.png)
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Abstract
En 中文
Software optimization refines programs for resource efficiency while preserving functionality. Traditionally, it is a process done by developers and compilers. This paper introduces a third option, automated optimization at the source code level. We present Supersonic , a neural approach targeting minor source code modifications for optimization. Using a seq2seq model, Supersonic is trained on C/C++ program pairs ( x(t) , x(t+1) ), where x(t+1) is an optimized version of x(t) , and outputs a diff. Supersonic 's performance is benchmarked against OpenAI's GPT-3.5-Turbo and GPT-4 on competitive programming tasks. The experiments show that Supersonic not only outperforms both models on the code optimization task but also minimizes the extent of the change with a model more than 600x smaller than GPT-3.5-Turbo and 3700x smaller than GPT-4.
Keywords:
Optimization
Codes
Training
Source coding
Task analysis
Decoding
Vectors
Code optimization
Seq2Seq learning
large language model
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