{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,28]],"date-time":"2025-09-28T15:25:23Z","timestamp":1759073123370},"reference-count":15,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Inf. & Syst."],"published-print":{"date-parts":[[2016]]},"DOI":"10.1587\/transinf.2015edp7440","type":"journal-article","created":{"date-parts":[[2016,5,31]],"date-time":"2016-05-31T18:12:43Z","timestamp":1464718363000},"page":"1585-1594","source":"Crossref","is-referenced-by-count":3,"title":["Guide Automatic Vectorization by means of Machine Learning: A Case Study of Tensor Contraction Kernels"],"prefix":"10.1587","volume":"E99.D","author":[{"given":"Antoine","family":"TROUV\u00c9","sequence":"first","affiliation":[{"name":"Engineering Department of Kyushu University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Arnaldo J.","family":"CRUZ","sequence":"additional","affiliation":[{"name":"Engineering Department of Kyushu University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kazuaki J.","family":"MURAKAMI","sequence":"additional","affiliation":[{"name":"Engineering Department of Kyushu University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Masaki","family":"ARAI","sequence":"additional","affiliation":[{"name":"Fujitsu Laboratories Limited"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tadashi","family":"NAKAHIRA","sequence":"additional","affiliation":[{"name":"Fujitsu Laboratories Limited"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eiji","family":"YAMANAKA","sequence":"additional","affiliation":[{"name":"Fujitsu Limited"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"532","reference":[{"key":"1","unstructured":"[1] D. Meyer, E. Dimitriadou, K. Hornik, A. Weingessel, F. Leisch, C.C. Chang, and C.C. Lin, Misc Functions of the Department of Statistics (e1071), (online: http:\/\/CRAN.R-project.org\/package=e1071 )"},{"key":"2","doi-asserted-by":"crossref","unstructured":"[2] E. Park, J. Cavazos, L.-N. Pouchet, C. Bastoul, A. Cohen, and P. Sadayappan, \u201cPredictive Modeling in a Polyhedral Optimization Space,\u201d In International journal of parallel programming, vol.41, no.5, pp.704-750, 2013.","DOI":"10.1007\/s10766-013-0241-1"},{"key":"3","doi-asserted-by":"crossref","unstructured":"[3] S. Kulkarni and J. Cavazos, \u201cMitigating the compiler optimization phase-ordering problem using machine learning,\u201d In Conference on object-oriented programming, systems, languages, and applications (OOPSLA), pp.147-162, 2012.","DOI":"10.1145\/2384616.2384628"},{"key":"4","doi-asserted-by":"crossref","unstructured":"[4] E. Park, J. Cavazos, and M.A. Alvarez, \u201cUsing graph-based program characterization for predictive modeling,\u201d In International symposium on code generation and optimization (CGO), pp.196-206, 2012.","DOI":"10.1145\/2259016.2259042"},{"key":"5","doi-asserted-by":"crossref","unstructured":"[5] K. Stock, L.-N. Pouchet, and P. Sadayappan, \u201cUsing machine learning to improve automatic vectorization,\u201d In ACM transaction on architecture and code optimization (TACO), vol.8, no.4, pp.1-23, 2012.","DOI":"10.1145\/2086696.2086729"},{"key":"6","doi-asserted-by":"crossref","unstructured":"[6] G. Fursin, Y. Kashnikov, A.W. Memon, Z. Chamski, O. Temam, M. Namolaru, E. Yom-Tov, B. Mendelson, A. Zaks, E. Courtois, F. Bodin, P. Barnard, E. Ashton, E. Bonilla, J. Thomson, C.K.I. Williams, and M. O'Boyle, \u201cMilepost GCC: machine learning enabled self-tuning compiler,\u201d In International journal of parallel programming, vol.39, no.3, pp.296-327, 2011.","DOI":"10.1007\/s10766-010-0161-2"},{"key":"7","doi-asserted-by":"crossref","unstructured":"[7] G. Fursin and O. Temam, \u201cCollective optimization: a practical collaborative approach,\u201d In ACM transaction on architecture and code optimization (TACO), vol.7, no.4, pp.1-29, 2010.","DOI":"10.1145\/1880043.1880047"},{"key":"8","doi-asserted-by":"crossref","unstructured":"[8] H. Leather, E. Bonilla, and M. O'Boyle, \u201cAutomatic feature generation for machine learning based optimizing compilation,\u201d In International symposium on code generation and optimization (CGO), pp.81-91, 2009.","DOI":"10.1109\/CGO.2009.21"},{"key":"9","doi-asserted-by":"crossref","unstructured":"[9] G. Pekhimenko and A.D. Brown, \u201cEfficient program compilation through machine learning techniques,\u201d International Workshop on Automatic Performance Tuning (iWAPT), 2009.","DOI":"10.1007\/978-1-4419-6935-4_19"},{"key":"10","doi-asserted-by":"crossref","unstructured":"[10] K. Hoste and L. Eeckhout, \u201cCOLE: compiler optimization level exploration,\u201d In International symposium on code generation and optimization (CGO), pp.165-174, 2008.","DOI":"10.1145\/1356058.1356080"},{"key":"11","unstructured":"[11] L.J.P. van der Maaten and G.E. Hinton, \u201cVisualizing high-dimensional data using t-SNE,\u201d J. Machine Learning Research, vol.9, pp.2579-2605, 2008."},{"key":"12","doi-asserted-by":"crossref","unstructured":"[12] K. Hoste and L. Eeckhout, \u201cMicroarchitecture-independent workload characterization,\u201d In International Symposium on Microarchitecture (MICRO), vol.27, no.3, pp.63-72, 2007.","DOI":"10.1109\/MM.2007.56"},{"key":"13","doi-asserted-by":"crossref","unstructured":"[13] F. Agakov, E. Bonilla, J. Cavazos, B. Franke, G. Fursin, M.F.P. O'Boyle, J. Thomson, M. Toussaint, and C.K.I. Williams, \u201cUsing machine learning to focus iterative optimization,\u201d In International symposium on code generation and optimization (CGO), pp.295-305, 2006.","DOI":"10.1109\/CGO.2006.37"},{"key":"14","doi-asserted-by":"crossref","unstructured":"[14] M. Stephenson and S. Amarasinghe, \u201cPredicting unroll factors using supervised classification,\u201d In International symposium on code generation and optimization, pp.123-134, 2005.","DOI":"10.1109\/CGO.2005.29"},{"key":"15","doi-asserted-by":"crossref","unstructured":"[15] L. Breiman, \u201cStatistical Modeling: The Two Cultures (with comments and a rejoinder by the author),\u201d In Statistical Science, vol.16, no.3, pp.199-231, 2001.","DOI":"10.1214\/ss\/1009213726"}],"container-title":["IEICE Transactions on Information and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E99.D\/6\/E99.D_2015EDP7440\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,9,8]],"date-time":"2019-09-08T22:53:57Z","timestamp":1567983237000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E99.D\/6\/E99.D_2015EDP7440\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016]]},"references-count":15,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2016]]}},"URL":"https:\/\/doi.org\/10.1587\/transinf.2015edp7440","relation":{},"ISSN":["0916-8532","1745-1361"],"issn-type":[{"value":"0916-8532","type":"print"},{"value":"1745-1361","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016]]}}}