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- Chen, ZiminKTH,Teoretisk datalogi, TCS (author)
Supersonic: Learning to Generate Source Code Optimizations in C/C++
- BookEnglish2023
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- 2023
- printrdacarrier
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- LIBRIS-ID:oai:DiVA.org:kth-339526
- https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-339526URI
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- Language:English
- Summary in:English
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- QC 20231120
- 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 (xt, xt+1), where xt+1 is an optimized version of xt, 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.
Subject headings and genre
- NATURVETENSKAP Data- och informationsvetenskap Datavetenskap hsv//swe
- NATURAL SCIENCES Computer and Information Sciences Computer Sciences hsv//eng
- Datalogi
- Computer Science
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- Fang, SenKTH,Teoretisk datalogi, TCS(Swepub:kth)u1b71h6u (author)
- Monperrus, MartinKTH,Teoretisk datalogi, TCS(Swepub:kth)u13jhcyf (author)
- KTHTeoretisk datalogi, TCS (creator_code:org_t)
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