{"id":240,"date":"2020-01-13T08:48:08","date_gmt":"2020-01-13T08:48:08","guid":{"rendered":"https:\/\/blogs.imperial.ac.uk\/research-software-engineering\/?p=240"},"modified":"2020-01-13T09:34:33","modified_gmt":"2020-01-13T09:34:33","slug":"quilting-with-julia-or-how-to-combine-parallelism-and-derived-types-for-high-performance-computing","status":"publish","type":"post","link":"https:\/\/blogs.imperial.ac.uk\/research-software-engineering\/2020\/01\/13\/quilting-with-julia-or-how-to-combine-parallelism-and-derived-types-for-high-performance-computing\/","title":{"rendered":"Quilting with Julia, or how to combine parallelism and derived types for high performance computing"},"content":{"rendered":"<p>Research and quilting have a similar Zen in that both combine and build upon multiple prior works. But the workflow is difficult to reproduce in research <em>software<\/em>: how can we combine group X&#8217;s state-of-the-art ODE solver with group Z&#8217;s state-of-the-art parallel linear algebra to create Y&#8217;s new biology model when they all use different libraries and conventions? This is the problem that <a href=\"https:\/\/julialang.org\/\">Julia<\/a> tackles head on, thanks to it&#8217;s innovative type system and multiple dispatch. In &#8220;<a href=\"https:\/\/mdavezac.github.io\/\">Shared Memory Parallelization of Banded Block-Banded Matrices<\/a>&#8221; we describe how to combine the parallelization capabilities from one package (<a href=\"https:\/\/docs.julialang.org\/en\/v1\/stdlib\/SharedArrays\/\">SharedArrays<\/a>) with the specialized matrix\u00a0 of another (<a href=\"https:\/\/github.com\/JuliaMatrices\/BlockBandedMatrices.jl\">BlockBandedMatrices.jl<\/a>) &#8211; without modifying the internals of either.<\/p>\n<p>This work follows on from a <a href=\"https:\/\/approximatelyfunctioning.blogspot.com\/2018\/12\/banded-matrices-and-ordinary.html\">NumFOCUS sponsored collaboration<\/a> at Imperial College between the Research Computing Service and <a href=\"https:\/\/blogs.imperial.ac.uk\/~solver\/\">Sheehan Olver<\/a> in the Department of Mathematics.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Research and quilting have a similar Zen in that both combine and build upon multiple prior works. But the workflow is difficult to reproduce in research software: how can we combine group X&#8217;s state-of-the-art ODE solver with group Z&#8217;s state-of-the-art parallel linear algebra to create Y&#8217;s new biology model when they all use different libraries [&hellip;]<\/p>\n","protected":false},"author":1400,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[390],"tags":[289404,289405],"class_list":["post-240","post","type-post","status-publish","format-standard","hentry","category-technology","tag-julia","tag-jupyter"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Quilting with Julia, or how to combine parallelism and derived types for high performance computing - Research Software Engineering<\/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:\/\/blogs.imperial.ac.uk\/research-software-engineering\/2020\/01\/13\/quilting-with-julia-or-how-to-combine-parallelism-and-derived-types-for-high-performance-computing\/\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Quilting with Julia, or how to combine parallelism and derived types for high performance computing - Research Software Engineering\" \/>\n<meta property=\"og:description\" content=\"Research and quilting have a similar Zen in that both combine and build upon multiple prior works. 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