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Author: mallamanis

_publications/chen2023supersonic.markdown

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+---
+layout: publication
+title: "Supersonic: Learning to Generate Source Code Optimizations in C/C++"
+authors: Zimin Chen, Sen Fang, Martin Monperrus
+conference:
+year: 2023
+additional_links:
+- {name: "ArXiV", url: "https://arxiv.org/abs/2309.14846"}
+tags: ["optimization"]
+---
+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.