{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,12,29]],"date-time":"2022-12-29T05:58:30Z","timestamp":1672293510841},"reference-count":5,"publisher":"Association for Computing Machinery (ACM)","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2022,8]]},"abstract":"<jats:p>\n            Distributed matrix computation solutions support query interfaces of linear algebra expressions, which often contain redundancy, i.e., common and loop-constant subexpressions. However, existing solutions fail to find all redundant subexpressions. Moreover, eliminating the found redundancy leads to new execution order of operators, which may have side effect. To exploit the benefits of redundancy elimination, we propose a new system called\n            <jats:italic>ReMac<\/jats:italic>\n            , which performs\n            <jats:italic>automatic<\/jats:italic>\n            and\n            <jats:italic>adaptive elimination.<\/jats:italic>\n            In particular, automatic elimination adopts a block-wise search that exploits the properties of matrix computation for speed-up. Adaptive elimination employs a cost model and a dynamic programming-based method to generate efficient plans with redundancy elimination. In this demonstration, attendees will have an opportunity to experience the effect that automatic and adaptive elimination have on distributed matrix computation.\n          <\/jats:p>","DOI":"10.14778\/3554821.3554872","type":"journal-article","created":{"date-parts":[[2022,9,29]],"date-time":"2022-09-29T22:28:39Z","timestamp":1664490519000},"page":"3674-3677","source":"Crossref","is-referenced-by-count":1,"title":["ReMac"],"prefix":"10.14778","volume":"15","author":[{"given":"Zihao","family":"Chen","sequence":"first","affiliation":[{"name":"East China Normal University"}]},{"given":"Zhizhen","family":"Xu","sequence":"additional","affiliation":[{"name":"East China Normal University"}]},{"given":"Baokun","family":"Han","sequence":"additional","affiliation":[{"name":"East China Normal University"}]},{"given":"Chen","family":"Xu","sequence":"additional","affiliation":[{"name":"East China Normal University"}]},{"given":"Weining","family":"Qian","sequence":"additional","affiliation":[{"name":"East China Normal University"}]},{"given":"Aoying","family":"Zhou","sequence":"additional","affiliation":[{"name":"East China Normal University"}]}],"member":"320","published-online":{"date-parts":[[2022,9,29]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"ScaLAPACK. http:\/\/www.netlib.org\/scalapack\/.  ScaLAPACK. http:\/\/www.netlib.org\/scalapack\/."},{"key":"e_1_2_1_2_1","volume-title":"Proceedings of the 10th Conference on Innovative Data Systems Research (CIDR).","author":"Matthias","unstructured":"Matthias Boehm et al. 2020. SystemDS: A Declarative Machine Learning System for the End-to-End Data Science Lifecycle . In Proceedings of the 10th Conference on Innovative Data Systems Research (CIDR). Matthias Boehm et al. 2020. SystemDS: A Declarative Machine Learning System for the End-to-End Data Science Lifecycle. In Proceedings of the 10th Conference on Innovative Data Systems Research (CIDR)."},{"key":"e_1_2_1_3_1","doi-asserted-by":"crossref","unstructured":"Reza Bosagh Zadeh et al. 2016. Matrix Computations and Optimization in Apache Spark. In SIGKDD. 31--38.  Reza Bosagh Zadeh et al. 2016. Matrix Computations and Optimization in Apache Spark. In SIGKDD. 31--38.","DOI":"10.1145\/2939672.2939675"},{"key":"e_1_2_1_4_1","doi-asserted-by":"crossref","unstructured":"Paul G. Brown. 2010. Overview of SciDB: Large Scale Array Storage Processing and Analysis. In SIGMOD. 963--968.  Paul G. Brown. 2010. Overview of SciDB: Large Scale Array Storage Processing and Analysis. In SIGMOD. 963--968.","DOI":"10.1145\/1807167.1807271"},{"key":"e_1_2_1_5_1","doi-asserted-by":"crossref","unstructured":"Zihao Chen et al. 2022. Redundancy Elimination in Distributed Matrix Computation. In SIGMOD. 573--586.  Zihao Chen et al. 2022. Redundancy Elimination in Distributed Matrix Computation. In SIGMOD. 573--586.","DOI":"10.1145\/3514221.3517877"}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3554821.3554872","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T11:34:05Z","timestamp":1672227245000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3554821.3554872"}},"subtitle":["a matrix computation system with redundancy elimination"],"short-title":[],"issued":{"date-parts":[[2022,8]]},"references-count":5,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2022,8]]}},"alternative-id":["10.14778\/3554821.3554872"],"URL":"https:\/\/doi.org\/10.14778\/3554821.3554872","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2022,8]]}}}