{"id":916,"date":"2014-07-22T17:18:53","date_gmt":"2014-07-22T08:18:53","guid":{"rendered":"https:\/\/www.kmi.nagoya-u.ac.jp\/eng\/seminar\/916\/"},"modified":"2014-07-22T17:18:53","modified_gmt":"2014-07-22T08:18:53","slug":"20140722_clark","status":"publish","type":"seminar","link":"https:\/\/www.kmi.nagoya-u.ac.jp\/eng\/seminar\/916\/","title":{"rendered":"Hierarchical Solvers on Heterogeneous Architectures"},"content":{"rendered":"<div>Owing to the computationally demanding nature of these calculations, GPUs are an increasingly popular platform for deployment, where a single calculation can requires thousands of GPUs working in tandem for months. There has been much progress to date in developing scalable sparse linear solver algorithms, utilizing well-known mathematical methods such as mixed precision, domain decomposition and pipelining to improve performance, allowing efficient use of large GPU installations such as Blue Waters and Titan. However, there has been less focus on deploying &#8220;mathematically optimal&#8221; linear solvers, that have optimal O(N) complexity. In this work we utilize the QUDA framework to deploy adaptive multigrid solvers on GPUs, in particular we describe the architecture abstractions that allow for deployment on heterogeneous systems, utilizing both GPUs and CPUs. We discuss in general the suitability of heterogeneous architectures for hierarchical algorithms.<\/div>\n<div><\/div>\n","protected":false},"featured_media":0,"template":"","tags":[],"seminar_category":[53],"acf":{"s_now_accepting":true,"s_date_order":"2014-07-22 14:00:00","s_date_end":null,"s_date_text":"","s_text":"Mike Clark","s_place":"other","s_place_other":"ES621","s_categoryother":"KMI Theory CTPLab Seminar","s_poster":"","s_poster2":"<form mt:asset-id=\"408\" class=\"mt-enclosure mt-enclosure-image\" style=\"display: inline;\"><a href=\"\/eng\/seminar\/files\/images\/phi2012bbBG.jpg\">\u8868\u793a<\/a><\/form>","s_slide":"<form mt:asset-id=\"776\" class=\"mt-enclosure mt-enclosure-file\" style=\"display: inline;\"><a href=\"\/eng\/seminar\/files\/20140722_Clark.pdf\">20140722_Clark.pdf<\/a><\/form>"},"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v23.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Hierarchical Solvers on Heterogeneous Architectures - KMI - Nagoya University<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.kmi.nagoya-u.ac.jp\/eng\/seminar\/916\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Hierarchical Solvers on Heterogeneous Architectures - KMI - Nagoya University\" \/>\n<meta property=\"og:description\" content=\"Owing to the computationally demanding nature of these calculations, GPUs are an increasingly popular platform for deployment, where a single calculation can requires thousands of GPUs working in tandem for months. There has been much progress to date in developing scalable sparse linear solver algorithms, utilizing well-known mathematical methods such as mixed precision, domain decomposition and pipelining to improve performance, allowing efficient use of large GPU installations such as Blue Waters and Titan. However, there has been less focus on &hellip;\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.kmi.nagoya-u.ac.jp\/eng\/seminar\/916\/\" \/>\n<meta property=\"og:site_name\" content=\"KMI - Nagoya University\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"1 minute\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.kmi.nagoya-u.ac.jp\/eng\/seminar\/916\/\",\"url\":\"https:\/\/www.kmi.nagoya-u.ac.jp\/eng\/seminar\/916\/\",\"name\":\"Hierarchical Solvers on Heterogeneous Architectures - KMI - Nagoya University\",\"isPartOf\":{\"@id\":\"https:\/\/www.kmi.nagoya-u.ac.jp\/eng\/#website\"},\"datePublished\":\"2014-07-22T08:18:53+00:00\",\"dateModified\":\"2014-07-22T08:18:53+00:00\",\"breadcrumb\":{\"@id\":\"https:\/\/www.kmi.nagoya-u.ac.jp\/eng\/seminar\/916\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/www.kmi.nagoya-u.ac.jp\/eng\/seminar\/916\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/www.kmi.nagoya-u.ac.jp\/eng\/seminar\/916\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/www.kmi.nagoya-u.ac.jp\/eng\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Seminars\",\"item\":\"https:\/\/www.kmi.nagoya-u.ac.jp\/eng\/seminar\/\"},{\"@type\":\"ListItem\",\"position\":3,\"name\":\"Hierarchical Solvers on Heterogeneous Architectures\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/www.kmi.nagoya-u.ac.jp\/eng\/#website\",\"url\":\"https:\/\/www.kmi.nagoya-u.ac.jp\/eng\/\",\"name\":\"KMI - Nagoya University\",\"description\":\"Nagoya University: Kobayashi-Maskawa Institute for the Origin of Particles and the Universe (KMI)\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/www.kmi.nagoya-u.ac.jp\/eng\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Hierarchical Solvers on Heterogeneous Architectures - KMI - Nagoya University","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/www.kmi.nagoya-u.ac.jp\/eng\/seminar\/916\/","og_locale":"en_US","og_type":"article","og_title":"Hierarchical Solvers on Heterogeneous Architectures - KMI - Nagoya University","og_description":"Owing to the computationally demanding nature of these calculations, GPUs are an increasingly popular platform for deployment, where a single calculation can requires thousands of GPUs working in tandem for months. There has been much progress to date in developing scalable sparse linear solver algorithms, utilizing well-known mathematical methods such as mixed precision, domain decomposition and pipelining to improve performance, allowing efficient use of large GPU installations such as Blue Waters and Titan. 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