ハシモト　マサトモ   Masatomo Hashimoto   橋本　政朋    所属   千葉工業大学  人工知能・ソフトウェア技術研究センター 人工知能・ソフトウェア技術研究センター    職種   主席研究員
言語種別	英語
発行・発表の年月	2017/04
形態種別	国際会議プロシーディングス
査読	査読あり
標題	An Empirical Study of Computation-Intensive Loops for Identifying and Classifying Loop Kernels
執筆形態	共著
掲載誌名	Proceedings of the 8th International Conference on Performance Engineering
掲載区分	国外
出版社・発行元	ACM
巻・号・頁	pp.361-371
著者・共著者	Masatomo Hashimoto, Masaaki Terai, Toshiyuki Maeda and Kazuo Minami
概要	In this study, we explored a thousand computation-intensive applications in terms of the distribution of kernel classes, each of which is related to expected efficiency and specific tuning patterns. To statistically estimate the distribution of the kernel classes, 100 loops were randomly sampled and then manually classified by experienced performance engineers. The result indicates that 50-70% of the kernels are memory-bound and hence difficult to run efficiently on modern scalar processors. In addition, based on the classification results, we constructed experimental classifiers for identifying loop kernels and for predicting kernel classes, which achieved cross-validated classification accuracy of 81% and 65%, respectively.
DOI	10.1145/3030207.3030217
ISBN	978-1-4503-4404-3
researchmap用URL	https://dl.acm.org/citation.cfm?id=3030217