  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.

Software Engineering;Artificial Intelligence