{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T14:43:42Z","timestamp":1784645022205,"version":"3.55.0"},"reference-count":17,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2016,7,11]],"date-time":"2016-07-11T00:00:00Z","timestamp":1468195200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Graph."],"published-print":{"date-parts":[[2016,7,11]]},"abstract":"<jats:p>\n            The Halide image processing language has proven to be an effective system for authoring high-performance image processing code. Halide programmers need only provide a high-level strategy for mapping an image processing pipeline to a parallel machine (a\n            <jats:italic>schedule<\/jats:italic>\n            ), and the Halide compiler carries out the mechanical task of generating platform-specific code that implements the schedule. Unfortunately, designing high-performance schedules for complex image processing pipelines requires substantial knowledge of modern hardware architecture and code-optimization techniques. In this paper we provide an algorithm for automatically generating high-performance schedules for Halide programs. Our solution extends the function bounds analysis already present in the Halide compiler to automatically perform locality and parallelism-enhancing global program transformations typical of those employed by expert Halide developers. The algorithm does not require costly (and often impractical) auto-tuning, and, in seconds, generates schedules for a broad set of image processing benchmarks that are performance-competitive with, and often better than, schedules manually authored by expert Halide developers on server and mobile CPUs, as well as GPUs.\n          <\/jats:p>","DOI":"10.1145\/2897824.2925952","type":"journal-article","created":{"date-parts":[[2016,7,11]],"date-time":"2016-07-11T16:04:33Z","timestamp":1468253073000},"page":"1-11","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":143,"title":["Automatically scheduling halide image processing pipelines"],"prefix":"10.1145","volume":"35","author":[{"given":"Ravi Teja","family":"Mullapudi","sequence":"first","affiliation":[{"name":"Carnegie Mellon University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew","family":"Adams","sequence":"additional","affiliation":[{"name":"Google"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dillon","family":"Sharlet","sequence":"additional","affiliation":[{"name":"Google"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jonathan","family":"Ragan-Kelley","sequence":"additional","affiliation":[{"name":"Stanford University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kayvon","family":"Fatahalian","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2016,7,11]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/1778765.1778766"},{"key":"e_1_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/2628071.2628092"},{"key":"e_1_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/1276377.1276506"},{"key":"e_1_2_2_4_1","volume-title":"ISBI 2008. 5th IEEE International Symposium on, IEEE, 1331--1334","author":"Darbon J."},{"key":"e_1_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/2070781.2024209"},{"key":"e_1_2_2_6_1","volume-title":"In Proc. of Fourth Alvey Vision Conference, 147--151","author":"Harris C."},{"key":"e_1_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/2601097.2601174"},{"key":"e_1_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/2897824.2925892"},{"key":"e_1_2_2_9_1","volume-title":"Caffe: Convolutional architecture for fast feature embedding. arXiv preprint arXiv:1408.5093.","author":"Jia Y.","year":"2014"},{"key":"e_1_2_2_10_1","unstructured":"Krizhevsky A. Sutskever I. and Hinton G. E. 2012. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems 1097--1105.  Krizhevsky A. Sutskever I. and Hinton G. E. 2012. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems 1097--1105."},{"key":"e_1_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/2694344.2694364"},{"key":"e_1_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/2010324.1964963"},{"key":"e_1_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/2185520.2185528"},{"key":"e_1_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/2491956.2462176"},{"key":"e_1_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/2776880.2792710"},{"key":"e_1_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2011.5995372"},{"key":"e_1_2_2_17_1","unstructured":"Simonyan K. and Zisserman A. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.  Simonyan K. and Zisserman A. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 ."}],"container-title":["ACM Transactions on Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/2897824.2925952","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/2897824.2925952","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:55:04Z","timestamp":1750222504000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/2897824.2925952"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,7,11]]},"references-count":17,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2016,7,11]]}},"alternative-id":["10.1145\/2897824.2925952"],"URL":"https:\/\/doi.org\/10.1145\/2897824.2925952","relation":{},"ISSN":["0730-0301","1557-7368"],"issn-type":[{"value":"0730-0301","type":"print"},{"value":"1557-7368","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,7,11]]},"assertion":[{"value":"2016-07-11","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}