{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T21:49:05Z","timestamp":1757454545113,"version":"3.41.0"},"reference-count":17,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2012,10,8]],"date-time":"2012-10-08T00:00:00Z","timestamp":1349654400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["SIGMETRICS Perform. Eval. Rev."],"published-print":{"date-parts":[[2012,10,8]]},"abstract":"<jats:p>OP2 is an \"active\" library framework for the development and solution of unstructured mesh based applications. It aims to decouple the scientific specification of an application from its parallel implementation to achieve code longevity and near-optimal performance through re-targeting the backend to different multi-core\/many-core hardware. This paper presents a predictive performance analysis and benchmarking study of OP2 on heterogeneous cluster systems. We first present the design of a new OP2 back-end that enables the execution of applications on distributed memory clusters, and benchmark its performance during the solution of a 1.5M and 26M edge-based CFD application written using OP2. Benchmark systems include a large-scale CrayXE6 system and an Intel Westmere\/InfiniBand cluster. We then apply performance modeling to predict the application's performance on an NVIDIA Tesla C2070 based GPU cluster, enabling us to compare OP2's performance capabilities on emerging distributed memory heterogeneous systems. Results illustrate the performance benefits that can be gained through many-core solutions both on single-node and heterogeneous configurations in comparison to traditional homogeneous cluster systems for this class of applications.<\/jats:p>","DOI":"10.1145\/2381056.2381072","type":"journal-article","created":{"date-parts":[[2012,10,11]],"date-time":"2012-10-11T14:55:16Z","timestamp":1349967316000},"page":"61-67","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Predictive modeling and analysis of OP2 on distributed memory GPU clusters"],"prefix":"10.1145","volume":"40","author":[{"given":"G. R.","family":"Mudalige","sequence":"first","affiliation":[{"name":"Oxford e-Research Centre, University of Oxford"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"M. B.","family":"Giles","sequence":"additional","affiliation":[{"name":"Oxford e-Research Centre, University of Oxford"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"C.","family":"Bertolli","sequence":"additional","affiliation":[{"name":"Imperial College London"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"P. H.J.","family":"Kelly","sequence":"additional","affiliation":[{"name":"Imperial College London"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2012,10,8]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"GPUDirect. http:\/\/developer.nvidia.com\/gpudirect.  GPUDirect. http:\/\/developer.nvidia.com\/gpudirect."},{"key":"e_1_2_1_2_1","unstructured":"HECToR. http:\/\/www.hector.ac.uk\/service\/hardware\/.  HECToR. http:\/\/www.hector.ac.uk\/service\/hardware\/."},{"key":"e_1_2_1_3_1","unstructured":"ParMETIS. http:\/\/www.cs.umn.edu\/~metis.  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OP2 for Many-Core Platforms , May 2011 . http:\/\/people.maths.ox.ac.uk\/gilesm\/op2\/. Giles, M. B. OP2 for Many-Core Platforms, May 2011. http:\/\/people.maths.ox.ac.uk\/gilesm\/op2\/."},{"key":"e_1_2_1_12_1","first-page":"4","article-title":"Using automatic differentiation for adjoint CFD code development","volume":"16","author":"Giles M. B.","year":"2008","unstructured":"Giles , M. B. , Ghate , D. , and Duta , M. C . Using automatic differentiation for adjoint CFD code development . Computational Fluid Dynamics Journal 16 , 4 ( 2008 ), 434--443. Giles, M. B., Ghate, D., and Duta, M. C. Using automatic differentiation for adjoint CFD code development. Computational Fluid Dynamics Journal 16, 4 (2008), 434--443.","journal-title":"Computational Fluid Dynamics Journal"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/1964218.1964221"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-92990-1_14"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/IPDPS.2008.4536243"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/1964218.1964223"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.finel.2003.10.006"}],"container-title":["ACM SIGMETRICS Performance Evaluation Review"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/2381056.2381072","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/2381056.2381072","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T20:00:39Z","timestamp":1750276839000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/2381056.2381072"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2012,10,8]]},"references-count":17,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2012,10,8]]}},"alternative-id":["10.1145\/2381056.2381072"],"URL":"https:\/\/doi.org\/10.1145\/2381056.2381072","relation":{},"ISSN":["0163-5999"],"issn-type":[{"type":"print","value":"0163-5999"}],"subject":[],"published":{"date-parts":[[2012,10,8]]},"assertion":[{"value":"2012-10-08","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}