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In this paper, we introduce an exact yet practical cost-based optimization framework for fusion plans and describe its end-to-end integration into Apache SystemML. We present techniques for candidate exploration and selection of fusion plans, as well as code generation of local and distributed operations over dense, sparse, and compressed data. Our experiments in SystemML show end-to-end performance improvements of up to 22x, with negligible compilation overhead.<\/jats:p>","DOI":"10.14778\/3229863.3229865","type":"journal-article","created":{"date-parts":[[2018,9,10]],"date-time":"2018-09-10T12:12:28Z","timestamp":1536581548000},"page":"1755-1768","source":"Crossref","is-referenced-by-count":43,"title":["On optimizing operator fusion plans for large-scale machine learning in systemML"],"prefix":"10.14778","volume":"11","author":[{"given":"Matthias","family":"Boehm","sequence":"first","affiliation":[{"name":"IBM Research - Almaden"}]},{"given":"Berthold","family":"Reinwald","sequence":"additional","affiliation":[{"name":"IBM Research - Almaden"}]},{"given":"Dylan","family":"Hutchison","sequence":"additional","affiliation":[{"name":"University of Washington"}]},{"given":"Prithviraj","family":"Sen","sequence":"additional","affiliation":[{"name":"IBM Research - Almaden"}]},{"given":"Alexandre V.","family":"Evfimievski","sequence":"additional","affiliation":[{"name":"IBM Research - Almaden"}]},{"given":"Niketan","family":"Pansare","sequence":"additional","affiliation":[{"name":"IBM Research - Almaden"}]}],"member":"320","published-online":{"date-parts":[[2018,8]]},"reference":[{"key":"e_1_2_1_1_1","first-page":"265","volume-title":"OSDI","author":"Abadi M.","year":"2016","unstructured":"M. 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