{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/finding-representative-sets-of-optimizations","title":"Finding representative sets of optimizations for adaptive multiversioning applications","arxiv_id":"1407.4075","date":"2014-07-14","proceeding":null,"authors":["Lianjie Luo","Yang Chen","Chengyong Wu","Shun Long","Grigori Fursin"],"abstract":"Iterative compilation is a widely adopted technique to optimize programs for\ndifferent constraints such as performance, code size and power consumption in\nrapidly evolving hardware and software environments. However, in case of\nstatically compiled programs, it is often restricted to optimizations for a\nspecific dataset and may not be applicable to applications that exhibit\ndifferent run-time behavior across program phases, multiple datasets or when\nexecuted in heterogeneous, reconfigurable and virtual environments. Several\nframeworks have been recently introduced to tackle these problems and enable\nrun-time optimization and adaptation for statically compiled programs based on\nstatic function multiversioning and monitoring of online program behavior. In\nthis article, we present a novel technique to select a minimal set of\nrepresentative optimization variants (function versions) for such frameworks\nwhile avoiding performance loss across available datasets and code-size\nexplosion. We developed a novel mapping mechanism using popular decision tree\nor rule induction based machine learning techniques to rapidly select best code\nversions at run-time based on dataset features and minimize selection overhead.\nThese techniques enable creation of self-tuning static binaries or libraries\nadaptable to changing behavior and environments at run-time using staged\ncompilation that do not require complex recompilation frameworks while\neffectively outperforming traditional single-version non-adaptable code.","url_abs":"http://arxiv.org/abs/1407.4075v1","url_pdf":"http://arxiv.org/pdf/1407.4075v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"finding-representative-sets-of-optimizations","repo_url":"https://github.com/ctuning/ck-analytics","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"finding-representative-sets-of-optimizations","repo_url":"https://github.com/ctuning/ck-autotuning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"finding-representative-sets-of-optimizations","repo_url":"https://github.com/ctuning/ck-crowdtuning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"finding-representative-sets-of-optimizations","repo_url":"https://github.com/ctuning/ck-math","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"finding-representative-sets-of-optimizations","repo_url":"https://github.com/ctuning/ck-web","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"finding-representative-sets-of-optimizations","repo_url":"https://github.com/ctuning/ctuning-programs","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"finding-representative-sets-of-optimizations","repo_url":"https://github.com/ctuning/reproduce-adapt16","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"finding-representative-sets-of-optimizations","repo_url":"https://github.com/ctuning/reproduce-ck-paper","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"finding-representative-sets-of-optimizations","repo_url":"https://github.com/ctuning/reproduce-ck-paper-large-experiments","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"finding-representative-sets-of-optimizations","repo_url":"https://github.com/ctuning/reproduce-milepost-project","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}