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Archit. Code Optim."],"published-print":{"date-parts":[[2016,6,27]]},"abstract":"<jats:p>The variety of today\u2019s architectures forces programmers to spend a great deal of time porting and tuning application codes across different platforms. Compilers themselves need additional tuning, which has considerable complexity as the standard optimization levels, usually designed for the average case and the specific target architecture, often fail to bring the best results.<\/jats:p>\n          <jats:p>\n            This article proposes\n            <jats:italic>COBAYN<\/jats:italic>\n            : Compiler autotuning framework using BAYesian Networks, an approach for a compiler autotuning methodology using machine learning to speed up application performance and to reduce the cost of the compiler optimization phases. The proposed framework is based on the application characterization done dynamically by using independent microarchitecture features and Bayesian networks. The article also presents an evaluation based on using static analysis and hybrid feature collection approaches. In addition, the article compares Bayesian networks with respect to several state-of-the-art machine-learning models.\n          <\/jats:p>\n          <jats:p>Experiments were carried out on an ARM embedded platform and GCC compiler by considering two benchmark suites with 39 applications. The set of compiler configurations, selected by the model (less than 7% of the search space), demonstrated an application performance speedup of up to 4.6 \u00d7 on Polybench (1.85 \u00d7 on average) and 3.1 \u00d7 on cBench (1.54 \u00d7 on average) with respect to standard optimization levels. Moreover, the comparison of the proposed technique with (i) random iterative compilation, (ii) machine learning--based iterative compilation, and (iii) noniterative predictive modeling techniques shows, on average, 1.2 \u00d7 , 1.37 \u00d7 , and 1.48 \u00d7 speedup, respectively. Finally, the proposed method demonstrates 4 \u00d7 and 3 \u00d7 speedup, respectively, on cBench and Polybench in terms of exploration efficiency given the same quality of the solutions generated by the random iterative compilation model.<\/jats:p>","DOI":"10.1145\/2928270","type":"journal-article","created":{"date-parts":[[2016,6,14]],"date-time":"2016-06-14T12:29:28Z","timestamp":1465907368000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":73,"title":["COBAYN"],"prefix":"10.1145","volume":"13","author":[{"given":"Amir Hossein","family":"Ashouri","sequence":"first","affiliation":[{"name":"Politecnico di Milano"}]},{"given":"Giovanni","family":"Mariani","sequence":"additional","affiliation":[{"name":"IBM, Dwingeloo, the Netherlands"}]},{"given":"Gianluca","family":"Palermo","sequence":"additional","affiliation":[{"name":"Politecnico di Milano"}]},{"given":"Eunjung","family":"Park","sequence":"additional","affiliation":[{"name":"Los Alamos National Laboratory, USA"}]},{"given":"John","family":"Cavazos","sequence":"additional","affiliation":[{"name":"University of Delaware, DE, USA"}]},{"given":"Cristina","family":"Silvano","sequence":"additional","affiliation":[{"name":"Politecnico di Milano"}]}],"member":"320","published-online":{"date-parts":[[2016,6,14]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/CGO.2006.37"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/1542476.1542481"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/2628071.2628092"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/2872421.2872424"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/ESTIMedia.2014.6962349"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/VLSI-SoC.2013.6673262"},{"key":"e_1_2_1_7_1","volume-title":"Positive Definite Matrices","author":"Bhatia Rajendra","unstructured":"Rajendra Bhatia . 2009. 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