Function Merging by Sequence Alignment
- Submitting institution
-
The University of Manchester
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 158740631
- Type
- E - Conference contribution
- DOI
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10.1109/CGO.2019.8661174
- Title of conference / published proceedings
- CGO 2019 - Proceedings of the 2019 IEEE/ACM International Symposium on Code Generation and Optimization
- First page
- 0
- Volume
- -
- Issue
- -
- ISSN
- -
- Open access status
- -
- Month of publication
- March
- Year of publication
- 2019
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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4
- Research group(s)
-
A - Computer Science
- Citation count
- 3
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- "This paper introduced a bio-informatics inspired approach for significantly reducing program size by automatically identifying and merging similar functions into one.
Best paper award in CGO 2019, an IEEE/ACM conference with 31% acceptance rate (21/67).
Presented at the 2019 EuroLLVM Developers’ Meeting.
Enabled GBP10,000 technology transfer EU TETRAMAX grant in collaboration with Codasip, a company offering customised RISC-V designs.
A follow-up paper, ""Effective Function merging in the SSA form"", was published in PLDI 2020, a premier ACM SIGPLAN conference with 23% acceptance rate (77/341)."
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
