{"id":531,"date":"2016-02-27T14:47:32","date_gmt":"2016-02-27T19:47:32","guid":{"rendered":"https:\/\/my.vanderbilt.edu\/masi\/?p=531"},"modified":"2016-11-01T10:45:27","modified_gmt":"2016-11-01T15:45:27","slug":"performance-management-of-high-performance-computing-for-medical-image-processing-in-amazon-web-services","status":"publish","type":"post","link":"https:\/\/my.vanderbilt.edu\/masi\/2016\/02\/performance-management-of-high-performance-computing-for-medical-image-processing-in-amazon-web-services\/","title":{"rendered":"Performance Management of High Performance Computing for Medical Image Processing in Amazon Web Services"},"content":{"rendered":"

Shunxing Bao, Stephen M. Damon, Bennett A. Landman, Aniruddha Gokhale. \u201cPerformance Management of High Performance Computing for Medical Image Processing in Amazon Web Services.\u201d In Proceedings of the SPIE Medical Imaging Conference. San Diego, California, February 2016. Oral presentation.<\/p>\n

Full Text: <\/strong>https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/?term=Performance+Management+of+High+Performance+Computing+for+Medical+Image+Processing+in+Amazon+Web+Services<\/a><\/p>\n

Abstract<\/h2>\n

Adopting high<\/span> performance<\/span> cloud computing<\/span> for medical<\/span> image<\/span> processing<\/span> is a popular trend given the pressing needs of large studies. Amazon<\/span> Web<\/span> Services<\/span> (AWS) provide reliable, on-demand, and inexpensive cloud computing<\/span> services<\/span>. Our research objective is to implement an affordable, scalable and easy-to-use AWS framework for the Java Image<\/span> Science Toolkit (JIST). JIST is a plugin for Medical<\/span>–Image<\/span> Processing<\/span>, Analysis, and Visualization (MIPAV) that provides a graphical pipeline implementation allowing users to quickly test and develop pipelines. JIST is DRMAA-compliant allowing it to run on portable batch system grids. However, as new processing<\/span> methods are implemented and developed, memory may often be a bottleneck for not only lab computers, but also possibly some local grids. Integrating JIST with the AWS cloud alleviates these possible restrictions and does not require users to have deep knowledge of programming in Java. Workflow definition\/management<\/span> and cloud configurations are two key challenges in this research. Using a simple unified control panel, users have the ability to set the numbers of nodes and select from a variety of pre-configured AWS EC2 nodes with different numbers of processors and memory storage. Intuitively, we configured Amazon<\/span> S3 storage to be mounted by pay-for-use Amazon<\/span> EC2 instances. Hence, S3 storage is recognized as a shared cloud resource. The Amazon<\/span> EC2 instances provide pre-installs of all necessary packages to run JIST. This work presents an implementation that facilitates the integration of JIST with AWS. We describe the theoretical cost\/benefit formulae to decide between local serial execution versus cloud computing<\/span> and apply this analysis to an empirical diffusion tensor imaging pipeline.<\/p>\n

\"Figure
Figure 1
Workflow framework.<\/figcaption><\/figure>\n","protected":false},"excerpt":{"rendered":"

Shunxing Bao, Stephen M. Damon, Bennett A. Landman, Aniruddha Gokhale. \u201cPerformance Management of High Performance Computing for Medical Image Processing in Amazon Web Services.\u201d In Proceedings of the SPIE Medical Imaging Conference. San Diego, California, February 2016. Oral presentation. Full Text: https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/?term=Performance+Management+of+High+Performance+Computing+for+Medical+Image+Processing+in+Amazon+Web+Services Abstract Adopting high performance cloud computing for medical image processing is a popular…<\/p>\n","protected":false},"author":6300,"featured_media":532,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[69],"tags":[71,38],"class_list":["post-531","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-cloud-computing","tag-amazon-web-services","tag-jist"],"_links":{"self":[{"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/posts\/531","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/users\/6300"}],"replies":[{"embeddable":true,"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/comments?post=531"}],"version-history":[{"count":2,"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/posts\/531\/revisions"}],"predecessor-version":[{"id":753,"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/posts\/531\/revisions\/753"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/media\/532"}],"wp:attachment":[{"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/media?parent=531"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/categories?post=531"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/tags?post=531"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}