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diff --git a/thesis/biblio.bib b/thesis/biblio.bib index 7d8fa6b..0c0c008 100644 --- a/thesis/biblio.bib +++ b/thesis/biblio.bib @@ -1,22 +1,20 @@ -@article{jung_brainy_2011, - title = {Brainy: effective selection of data structures}, - volume = {46}, - issn = {0362-1340, 1558-1160}, - url = {https://dl.acm.org/doi/10.1145/1993316.1993509}, - doi = {10.1145/1993316.1993509}, - shorttitle = {Brainy}, - abstract = {Data structure selection is one of the most critical aspects of developing effective applications. By analyzing data structures' behavior and their interaction with the rest of the application on the underlying architecture, tools can make suggestions for alternative data structures better suited for the program input on which the application runs. Consequently, developers can optimize their data structure usage to make the application conscious of an underlying architecture and a particular program input. - This paper presents the design and evaluation of Brainy, a new program analysis tool that automatically selects the best data structure for a given program and its input on a specific microarchitecture. The data structure's interface functions are instrumented to dynamically monitor how the data structure interacts with the application for a given input. The instrumentation records traces of various runtime characteristics including underlying architecture-specific events. These generated traces are analyzed and fed into an offline model, constructed using machine learning, to select the best data structure. That is, Brainy exploits runtime feedback of data structures to model the situation an application runs on, and selects the best data structure for a given application/input/architecture combination based on the constructed model. The empirical evaluation shows that this technique is highly accurate across several real-world applications with various program input sets on two different state-of-the-art microarchitectures. Consequently, Brainy achieved an average performance improvement of 27\% and 33\% on both microarchitectures, respectively.}, - pages = {86--97}, - number = {6}, - journaltitle = {{ACM} {SIGPLAN} Notices}, - shortjournal = {{SIGPLAN} Not.}, - author = {Jung, Changhee and Rus, Silvius and Railing, Brian P. and Clark, Nathan and Pande, Santosh}, +@inproceedings{shacham_chameleon_2009, + location = {Dublin Ireland}, + title = {Chameleon: adaptive selection of collections}, + isbn = {978-1-60558-392-1}, + url = {https://dl.acm.org/doi/10.1145/1542476.1542522}, + doi = {10.1145/1542476.1542522}, + shorttitle = {Chameleon}, + eventtitle = {{PLDI} '09: {ACM} {SIGPLAN} Conference on Programming Language Design and Implementation}, + pages = {408--418}, + booktitle = {Proceedings of the 30th {ACM} {SIGPLAN} Conference on Programming Language Design and Implementation}, + publisher = {{ACM}}, + author = {Shacham, Ohad and Vechev, Martin and Yahav, Eran}, urldate = {2023-09-21}, - date = {2011-06-04}, + date = {2009-06-15}, langid = {english}, - file = {Jung et al. - 2011 - Brainy effective selection of data structures.pdf:/home/aria/Zotero/storage/DPJPURT8/Jung et al. - 2011 - Brainy effective selection of data structures.pdf:application/pdf}, + file = {Shacham et al. - 2009 - Chameleon adaptive selection of collections.pdf:/home/aria/Zotero/storage/75CS9CWY/Shacham et al. - 2009 - Chameleon adaptive selection of collections.pdf:application/pdf}, } @inproceedings{costa_collectionswitch_2018, @@ -37,22 +35,24 @@ file = {Costa and Andrzejak - 2018 - CollectionSwitch a framework for efficient and dy:/home/aria/Zotero/storage/7B8QMVRU/Costa and Andrzejak - 2018 - CollectionSwitch a framework for efficient and dy:application/pdf}, } -@inproceedings{shacham_chameleon_2009, - location = {Dublin Ireland}, - title = {Chameleon: adaptive selection of collections}, - isbn = {978-1-60558-392-1}, - url = {https://dl.acm.org/doi/10.1145/1542476.1542522}, - doi = {10.1145/1542476.1542522}, - shorttitle = {Chameleon}, - eventtitle = {{PLDI} '09: {ACM} {SIGPLAN} Conference on Programming Language Design and Implementation}, - pages = {408--418}, - booktitle = {Proceedings of the 30th {ACM} {SIGPLAN} Conference on Programming Language Design and Implementation}, - publisher = {{ACM}}, - author = {Shacham, Ohad and Vechev, Martin and Yahav, Eran}, +@article{jung_brainy_2011, + title = {Brainy: effective selection of data structures}, + volume = {46}, + issn = {0362-1340, 1558-1160}, + url = {https://dl.acm.org/doi/10.1145/1993316.1993509}, + doi = {10.1145/1993316.1993509}, + shorttitle = {Brainy}, + abstract = {Data structure selection is one of the most critical aspects of developing effective applications. By analyzing data structures' behavior and their interaction with the rest of the application on the underlying architecture, tools can make suggestions for alternative data structures better suited for the program input on which the application runs. Consequently, developers can optimize their data structure usage to make the application conscious of an underlying architecture and a particular program input. + This paper presents the design and evaluation of Brainy, a new program analysis tool that automatically selects the best data structure for a given program and its input on a specific microarchitecture. The data structure's interface functions are instrumented to dynamically monitor how the data structure interacts with the application for a given input. The instrumentation records traces of various runtime characteristics including underlying architecture-specific events. These generated traces are analyzed and fed into an offline model, constructed using machine learning, to select the best data structure. That is, Brainy exploits runtime feedback of data structures to model the situation an application runs on, and selects the best data structure for a given application/input/architecture combination based on the constructed model. The empirical evaluation shows that this technique is highly accurate across several real-world applications with various program input sets on two different state-of-the-art microarchitectures. Consequently, Brainy achieved an average performance improvement of 27\% and 33\% on both microarchitectures, respectively.}, + pages = {86--97}, + number = {6}, + journaltitle = {{ACM} {SIGPLAN} Notices}, + shortjournal = {{SIGPLAN} Not.}, + author = {Jung, Changhee and Rus, Silvius and Railing, Brian P. and Clark, Nathan and Pande, Santosh}, urldate = {2023-09-21}, - date = {2009-06-15}, + date = {2011-06-04}, langid = {english}, - file = {Shacham et al. - 2009 - Chameleon adaptive selection of collections.pdf:/home/aria/Zotero/storage/75CS9CWY/Shacham et al. - 2009 - Chameleon adaptive selection of collections.pdf:application/pdf}, + file = {Jung et al. - 2011 - Brainy effective selection of data structures.pdf:/home/aria/Zotero/storage/DPJPURT8/Jung et al. - 2011 - Brainy effective selection of data structures.pdf:application/pdf}, } @article{qin_primrose_2023, @@ -75,3 +75,21 @@ keywords = {Computer Science - Data Structures and Algorithms, Computer Science - Programming Languages}, file = {arXiv Fulltext PDF:/home/aria/Zotero/storage/IL59NESA/Qin et al. - 2023 - Primrose Selecting Container Data Types by Their .pdf:application/pdf;arXiv.org Snapshot:/home/aria/Zotero/storage/DCIW4XE4/2205.html:text/html}, } + +@article{chung_towards_2004, + title = {Towards Automatic Performance Tuning}, + author = {Chung, I-Hsin}, + date = {2004-11}, + file = {Chung - 2004 - Towards Automatic Performance Tuning.pdf:/home/aria/Zotero/storage/WQBJMSN8/Chung - 2004 - Towards Automatic Performance Tuning.pdf:application/pdf}, +} + +@inproceedings{l_liu_perflint_2009, + title = {Perflint: A Context Sensitive Performance Advisor for C++ Programs}, + doi = {10.1109/CGO.2009.36}, + eventtitle = {2009 International Symposium on Code Generation and Optimization}, + pages = {265--274}, + booktitle = {2009 International Symposium on Code Generation and Optimization}, + author = {{L. Liu} and {S. Rus}}, + date = {2009-03-22}, + note = {Journal Abbreviation: 2009 International Symposium on Code Generation and Optimization}, +} |