{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T07:37:51Z","timestamp":1768030671393,"version":"3.49.0"},"reference-count":25,"publisher":"IEEE","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016,12]]},"DOI":"10.1109\/bigdata.2016.7840756","type":"proceedings-article","created":{"date-parts":[[2017,2,7]],"date-time":"2017-02-07T21:46:59Z","timestamp":1486504019000},"page":"1483-1492","source":"Crossref","is-referenced-by-count":7,"title":["Mini-apps for high performance data analysis"],"prefix":"10.1109","author":[{"given":"Sreenivas R.","family":"Sukumar","sequence":"first","affiliation":[]},{"given":"Michael A.","family":"Matheson","sequence":"additional","affiliation":[]},{"given":"Ramakrishnan","family":"Kannan","sequence":"additional","affiliation":[]},{"given":"Seung-Hwan","family":"Lim","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/ISPASS.2015.7095783"},{"key":"ref11","article-title":"Scaling up machine learning: Parallel and distributed approaches","author":"ron","year":"2011"},{"key":"ref12","article-title":"One size fits all&#x201D;: an idea whose time has come and gone.&#x201D; Data Engineering, 2005. ICDE 2005","author":"michael","year":"2005","journal-title":"Proceedings 21 st International Conference on IEEE"},{"key":"ref13","first-page":"281","article-title":"Map-reduce for machine learning on multicore","volume":"19","author":"chu","year":"2007","journal-title":"Advances in neural information processing systems"},{"key":"ref14","article-title":"Using a Developing MiniApp to Compare Platform Characteristics on Cray Systems, Cray User Group Proceedings","author":"messer","year":"2014"},{"key":"ref15","first-page":"5574","article-title":"Improving performance via mini-applications","author":"heroux","year":"0","journal-title":"Sandia National Laboratories Tech Rep"},{"key":"ref16","year":"0"},{"key":"ref17","year":"0"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-007-0114-2"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-06548-9_1"},{"key":"ref4","article-title":"Data dwarfs: Motivating a coverage set for future large data center workloads","author":"mehul","year":"2010","journal-title":"Proc Workshop Architectural Concerns in Large Datacenters"},{"key":"ref3","article-title":"Improving performance via mini-applications","author":"heroux","year":"2009","journal-title":"Sandia National Laboratories Technical Report SAND 2009"},{"key":"ref6","article-title":"A Tale of Two Data-Intensive Approaches: Applications, Architectures and Infrastructure","author":"shantenu","year":"2014","journal-title":"3rd International IEEE Congress on &#x2018;Big Data&#x2019; Application and Experience Track"},{"key":"ref5","article-title":"Towards an Understanding of Facets and Exemplars of &#x2018;Big Data&#x2019; Applications","author":"fox","year":"2014","journal-title":"proceedings of Workshop Twenty Years of Beowulf"},{"key":"ref8","year":"0"},{"key":"ref7","article-title":"Big Data","year":"2013","journal-title":"Public Working Group (NBD-PWG) Use Cases and Requirements"},{"key":"ref2","article-title":"Frontiers on Massive Data Analysis","year":"2013"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/BigData.2015.7363882"},{"key":"ref1","first-page":"14","article-title":"Synergistic challenges in data-intensive science and Exa-scale computing","volume":"3","author":"chen","year":"2013","journal-title":"DOE ASCAC Data Subcommittee Report"},{"key":"ref20","year":"0"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1145\/2851141.2851152"},{"key":"ref21","article-title":"MPI-FAUN: An MPI-Based Framework for Alternating-Updating Nonnegative Matrix Factorization","author":"kannan","year":"2016","journal-title":"arXiv preprint arXiv 1609 09861"},{"key":"ref24","doi-asserted-by":"crossref","first-page":"458","DOI":"10.1016\/j.tcs.2008.07.017","article-title":"Main-memory triangle comnutations for very large (sparse (power-law)) graphs","volume":"407","author":"latanv","year":"2008","journal-title":"Theoretical Computer Science"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1088\/0957-4484\/27\/41\/414003"},{"key":"ref25","article-title":"Matrix Factorization at Scale: a Comparison of ScientificData Analytics in Spark and C+ Mpi Using Three Case Studies","author":"alex","year":"0","journal-title":"arXiv preprint arXiv 1607 01335 (2016)"}],"event":{"name":"2016 IEEE International Conference on Big Data (Big Data)","location":"Washington DC,USA","start":{"date-parts":[[2016,12,5]]},"end":{"date-parts":[[2016,12,8]]}},"container-title":["2016 IEEE International Conference on Big Data (Big Data)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/7818133\/7840573\/07840756.pdf?arnumber=7840756","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,9,18]],"date-time":"2019-09-18T11:18:14Z","timestamp":1568805494000},"score":1,"resource":{"primary":{"URL":"http:\/\/ieeexplore.ieee.org\/document\/7840756\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,12]]},"references-count":25,"URL":"https:\/\/doi.org\/10.1109\/bigdata.2016.7840756","relation":{},"subject":[],"published":{"date-parts":[[2016,12]]}}}