{"id":813,"date":"2014-02-27T04:31:18","date_gmt":"2014-02-27T04:31:18","guid":{"rendered":"http:\/\/research.binus.ac.id\/\/?p=813"},"modified":"2020-10-26T14:09:10","modified_gmt":"2020-10-26T07:09:10","slug":"design-of-single-user-decision-support-system-model-based-on-fuzzy-simple-additive-weighting-algorithm-to-reduce-consumer-confusion-problems-in-smartphone-purchases","status":"publish","type":"post","link":"https:\/\/research.binus.ac.id\/bdsrc\/2014\/02\/27\/design-of-single-user-decision-support-system-model-based-on-fuzzy-simple-additive-weighting-algorithm-to-reduce-consumer-confusion-problems-in-smartphone-purchases\/","title":{"rendered":"Comparison of Conjugate Gradient Method and Jacobi Method Algorithm on MapReduce Framework"},"content":{"rendered":"<p style=\"text-align: justify\">As the volume of data continues to grow across many areas of science, parallel computing is a solution to the scaling problem many applications face. The goal of a parallel program is to enable the execution of larger problems and to reduce the execution time compared to sequential programs. Among parallel computing frameworks, MapReduce is a framework that enables parallel processing of data on collections of commodity computing nodes without the need to handle the complexities of implementing a parallel program. This paper presents implementations of the parallel Jacobi and Conjugate Gradient methods using MapReduce. A performance analysis shows that MapReduce can speed up the Jacobi method over sequential processing for dense matrices with dimension \u2265 14,000.<\/p>\n<p>Applied Mathematical Sciences, vol. 8, no. 17, pp. 837-849, 2014<\/p>\n<p><strong>Muhamad Fitra Kacamarga, Bens Pardamean, James Baurley<\/strong><\/p>\n<p><a href=\"https:\/\/www.researchgate.net\/publication\/263389280_Comparison_of_Conjugate_Gradient\">Read Full Paper<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>As the volume of data continues to grow across many areas of science, parallel computing is a solution to the scaling problem many applications face. The goal of a parallel program is to enable the execution of larger problems and to reduce the execution time compared to sequential programs. Among parallel computing frameworks, MapReduce is [&hellip;]<\/p>\n","protected":false},"author":7,"featured_media":3061,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[12],"tags":[],"class_list":["post-813","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-publications"],"_links":{"self":[{"href":"https:\/\/research.binus.ac.id\/bdsrc\/wp-json\/wp\/v2\/posts\/813","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/research.binus.ac.id\/bdsrc\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/research.binus.ac.id\/bdsrc\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/research.binus.ac.id\/bdsrc\/wp-json\/wp\/v2\/users\/7"}],"replies":[{"embeddable":true,"href":"https:\/\/research.binus.ac.id\/bdsrc\/wp-json\/wp\/v2\/comments?post=813"}],"version-history":[{"count":9,"href":"https:\/\/research.binus.ac.id\/bdsrc\/wp-json\/wp\/v2\/posts\/813\/revisions"}],"predecessor-version":[{"id":3060,"href":"https:\/\/research.binus.ac.id\/bdsrc\/wp-json\/wp\/v2\/posts\/813\/revisions\/3060"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/research.binus.ac.id\/bdsrc\/wp-json\/wp\/v2\/media\/3061"}],"wp:attachment":[{"href":"https:\/\/research.binus.ac.id\/bdsrc\/wp-json\/wp\/v2\/media?parent=813"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/research.binus.ac.id\/bdsrc\/wp-json\/wp\/v2\/categories?post=813"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/research.binus.ac.id\/bdsrc\/wp-json\/wp\/v2\/tags?post=813"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}