{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T23:01:12Z","timestamp":1777676472337,"version":"3.51.4"},"reference-count":19,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2011,7,7]],"date-time":"2011-07-07T00:00:00Z","timestamp":1309996800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["The International Journal of High Performance Computing Applications"],"published-print":{"date-parts":[[2012,2]]},"abstract":"<jats:p>The Community Atmosphere Model (CAM), which serves as the atmosphere component of the Community Climate System Model (CCSM), is the most computationally expensive CCSM component in typical configurations. On current and next-generation leadership class computing systems, the performance of CAM is tied to its parallel scalability. Improving performance scalability in CAM has been a challenge, due largely to algorithmic restrictions necessitated by the polar singularities in its latitude\u2013longitude computational grid. Nevertheless, through a combination of exploiting additional parallelism, implementing improved communication protocols, and eliminating scalability bottlenecks, we have been able to more than double the maximum throughput rate of CAM on production platforms. We describe these improvements and present results on the Cray XT5 and IBM BG\/P. The approaches taken are not specific to CAM and may inform similar scalability enhancement activities for other codes.<\/jats:p>","DOI":"10.1177\/1094342011412630","type":"journal-article","created":{"date-parts":[[2011,7,8]],"date-time":"2011-07-08T10:03:30Z","timestamp":1310119410000},"page":"17-30","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":16,"title":["Improving the performance scalability of the community atmosphere model"],"prefix":"10.1177","volume":"26","author":[{"given":"Arthur A.","family":"Mirin","sequence":"first","affiliation":[{"name":"Center for Applied Scientific Computing, Lawrence Livermore National Laboratory, Livermore, CA USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Patrick H.","family":"Worley","sequence":"additional","affiliation":[{"name":"Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2011,7,7]]},"reference":[{"key":"bibr1-1094342011412630","doi-asserted-by":"publisher","DOI":"10.1175\/JCLI3761.1"},{"key":"bibr2-1094342011412630","doi-asserted-by":"publisher","DOI":"10.1175\/JCLI3760.1"},{"key":"bibr3-1094342011412630","doi-asserted-by":"publisher","DOI":"10.1109\/99.660313"},{"key":"bibr4-1094342011412630","doi-asserted-by":"publisher","DOI":"10.1177\/1094342005056108"},{"key":"bibr5-1094342011412630","volume-title":"CAM-SE: A scalable spectral element dynamical core for the Community Atmosphere Model. 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