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diff --git a/thesis/biblio.bib b/thesis/biblio.bib index ed823cc..fe7a041 100644 --- a/thesis/biblio.bib +++ b/thesis/biblio.bib @@ -1,20 +1,23 @@ -@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}, + keywords = {ml, read}, + 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}, } @inproceedings{costa_collectionswitch_2018, @@ -32,27 +35,27 @@ urldate = {2023-09-21}, date = {2018-02-24}, langid = {english}, + keywords = {estimate-based, read}, 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}, } -@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}, + keywords = {read, rules-based}, + 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}, } @article{qin_primrose_2023, @@ -72,27 +75,18 @@ date = {2023-02-15}, eprinttype = {arxiv}, eprint = {2205.09655 [cs]}, - keywords = {Computer Science - Data Structures and Algorithms, Computer Science - Programming Languages}, + keywords = {functional requirements, read}, 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}, - file = {Full Text:/home/aria/Zotero/storage/KTJNYCES/L. Liu and S. Rus - 2009 - Perflint A Context Sensitive Performance Advisor .pdf:application/pdf}, +@inproceedings{osterlund_dynamically_2013, + title = {Dynamically transforming data structures}, + doi = {10.1109/ASE.2013.6693099}, + pages = {410--420}, + booktitle = {2013 28th {IEEE}/{ACM} International Conference on Automated Software Engineering ({ASE})}, + author = {Österlund, Erik and Löwe, Welf}, + date = {2013}, + keywords = {rules-based}, } @incollection{hutchison_coco_2013, @@ -113,13 +107,60 @@ date = {2013}, doi = {10.1007/978-3-642-39038-8_1}, note = {Series Title: Lecture Notes in Computer Science}, + keywords = {read, rules-based}, } -@inproceedings{osterlund_dynamically_2013, - title = {Dynamically transforming data structures}, - doi = {10.1109/ASE.2013.6693099}, - pages = {410--420}, - booktitle = {2013 28th {IEEE}/{ACM} International Conference on Automated Software Engineering ({ASE})}, - author = {Österlund, Erik and Löwe, Welf}, - date = {2013}, +@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}, + keywords = {read, rules-based}, + file = {Full Text:/home/aria/Zotero/storage/KTJNYCES/L. Liu and S. Rus - 2009 - Perflint A Context Sensitive Performance Advisor .pdf:application/pdf}, +} + +@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{thomas_framework_2005, + location = {New York, {NY}, {USA}}, + title = {A Framework for Adaptive Algorithm Selection in {STAPL}}, + isbn = {1-59593-080-9}, + url = {https://doi.org/10.1145/1065944.1065981}, + doi = {10.1145/1065944.1065981}, + series = {{PPoPP} '05}, + abstract = {Writing portable programs that perform well on multiple platforms or for varying input sizes and types can be very difficult because performance is often sensitive to the system architecture, the run-time environment, and input data characteristics. This is even more challenging on parallel and distributed systems due to the wide variety of system architectures. One way to address this problem is to adaptively select the best parallel algorithm for the current input data and system from a set of functionally equivalent algorithmic options. Toward this goal, we have developed a general framework for adaptive algorithm selection for use in the Standard Template Adaptive Parallel Library ({STAPL}). Our framework uses machine learning techniques to analyze data collected by {STAPL} installation benchmarks and to determine tests that will select among algorithmic options at run-time. We apply a prototype implementation of our framework to two important parallel operations, sorting and matrix multiplication, on multiple platforms and show that the framework determines run-time tests that correctly select the best performing algorithm from among several competing algorithmic options in 86-100\% of the cases studied, depending on the operation and the system.}, + pages = {277--288}, + booktitle = {Proceedings of the Tenth {ACM} {SIGPLAN} Symposium on Principles and Practice of Parallel Programming}, + publisher = {Association for Computing Machinery}, + author = {Thomas, Nathan and Tanase, Gabriel and Tkachyshyn, Olga and Perdue, Jack and Amato, Nancy M. and Rauchwerger, Lawrence}, + date = {2005}, + note = {event-place: Chicago, {IL}, {USA}}, + keywords = {ml, read}, +} + +@inproceedings{franke_collection_2022, + location = {New York, {NY}, {USA}}, + title = {Collection Skeletons: Declarative Abstractions for Data Collections}, + isbn = {978-1-4503-9919-7}, + url = {https://doi.org/10.1145/3567512.3567528}, + doi = {10.1145/3567512.3567528}, + series = {{SLE} 2022}, + abstract = {Modern programming languages provide programmers with rich abstractions for data collections as part of their standard libraries, e.g. Containers in the C++ {STL}, the Java Collections Framework, or the Scala Collections {API}. Typically, these collections frameworks are organised as hierarchies that provide programmers with common abstract data types ({ADTs}) like lists, queues, and stacks. While convenient, this approach introduces problems which ultimately affect application performance due to users over-specifying collection data types limiting implementation flexibility. In this paper, we develop Collection Skeletons which provide a novel, declarative approach to data collections. Using our framework, programmers explicitly select properties for their collections, thereby truly decoupling specification from implementation. By making collection properties explicit immediate benefits materialise in form of reduced risk of over-specification and increased implementation flexibility. We have prototyped our declarative abstractions for collections as a C++ library, and demonstrate that benchmark applications rewritten to use Collection Skeletons incur little or no overhead. In fact, for several benchmarks, we observe performance speedups (on average between 2.57 to 2.93, and up to 16.37) and also enhanced performance portability across three different hardware platforms.}, + pages = {189--201}, + booktitle = {Proceedings of the 15th {ACM} {SIGPLAN} International Conference on Software Language Engineering}, + publisher = {Association for Computing Machinery}, + author = {Franke, Björn and Li, Zhibo and Morton, Magnus and Steuwer, Michel}, + date = {2022}, + note = {event-place: Auckland, New Zealand}, + keywords = {functional requirements}, + file = {Accepted Version:/home/aria/Zotero/storage/TJ3AGL2S/Franke et al. - 2022 - Collection Skeletons Declarative Abstractions for.pdf:application/pdf}, } |