{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,8,26]],"date-time":"2023-08-26T13:10:58Z","timestamp":1693055458577},"reference-count":27,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2021,11,29]],"date-time":"2021-11-29T00:00:00Z","timestamp":1638144000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Concurrency and Computation"],"published-print":{"date-parts":[[2022,4,25]]},"abstract":"Abstract<\/jats:title>Some algorithms deal with large amounts of data and vast and complex calculations, such as the algorithms used in numerical weather models in meteorological science. The need for extensive computer and computing resources is a problem in the process of running meteorological models, and this problem cannot be solved by or is not efficient to run on classical computing. Instead of traditional computers, high\u2010performance computer systems are used as a solution to this problem with the Message Passing Interface (MPI) to split the computational load over many CPU cores. In this study, we undertake the parallelizing of a meteorological algorithm written for the special purpose of forecasting temperature inversion. We evaluate the performance of running this algorithm in parallel to see the effect of using multiple cores on the performance with a specific example. Furthermore, we evaluate the algorithm's memory consumption by running the program on a GPU with CUDA and measure the execution times. The results show that the execution time decreases when using more cores until a breaking point, and this breaking point is changed proportionally with the size of the algorithm. In terms of time, we obtain up to 84.95% better performance by running the program in parallel using MPI and up to 94.10% better performance by running the program in parallel using CUDA.<\/jats:p>","DOI":"10.1002\/cpe.6738","type":"journal-article","created":{"date-parts":[[2021,11,29]],"date-time":"2021-11-29T08:05:41Z","timestamp":1638173141000},"update-policy":"http:\/\/dx.doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Parallelization of a meteorological model using message passing interface and CUDA: A case study with the inversion estimation algorithm"],"prefix":"10.1002","volume":"34","author":[{"ORCID":"http:\/\/orcid.org\/0000-0003-1929-5358","authenticated-orcid":false,"given":"Ahmet E.","family":"Topcu","sequence":"first","affiliation":[{"name":"College of Engineering and Technology American University of the Middle East Egaila Kuwait"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-8848-1213","authenticated-orcid":false,"given":"\u0130sameddin","family":"Omak","sequence":"additional","affiliation":[{"name":"Computer Engineering Department Ankara Yildirim Beyazit University Ankara Turkey"}]}],"member":"311","published-online":{"date-parts":[[2021,11,29]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-18476-0_19"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11269-009-9483-5"},{"key":"e_1_2_9_4_1","unstructured":"National center for atmospheric research website. 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Accessed May 10 2018.https:\/\/www.mgm.gov.tr\/site\/yardim1.aspx?=Enverziyon"},{"key":"e_1_2_9_11_1","volume-title":"Inversion estimation for urban air pollution risk, 2008\u20102009 Winter Season Evaluation [Kentsel hava kirlili\u011fi riski i\u00e7in enverziyon tahmini 2008\u20102009 K\u0131\u015f D\u00f6nemi De\u011ferlendirmesi], Ankara","author":"D\u00fcndar C","year":"2009"},{"key":"e_1_2_9_12_1","unstructured":"Inversion risk map Turkish State Meteorological Service Website; 2018. Accessed May 10 2018.https:\/\/www.mgm.gov.tr\/tahmin\/enverziyon\u2010risk\u2010haritasi.aspx"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cpc.2010.12.021"},{"key":"e_1_2_9_14_1","unstructured":"Medium\u2010range weather forecasts website fEC. Supercomputer; 2018. Accessed May 12 2018.https:\/\/www.ecmwf.int\/en\/computing\/our\u2010facilities\/supercomputer"},{"key":"e_1_2_9_15_1","unstructured":"Website BSC. Technical information. Accessed May 12 2018.https:\/\/www.bsc.es\/marenostrum\/marenostrum\/technical\u2010information"},{"key":"e_1_2_9_16_1","unstructured":"Atmospheric research website fNC. Cheyenne. Accessed May 12 2018.https:\/\/www2.cisl.ucar.edu\/resources\/computational\u2010systems\/cheyenne"},{"key":"e_1_2_9_17_1","doi-asserted-by":"crossref","unstructured":"GabrielE FaggGE BosilcaG et al.Open MPI: goals concept and design of a next generation MPI implementation. Proceedings of the European Parallel Virtual Machine\/Message Passing Interface Users' Group Meeting; 2004:97\u2010104; Springer.","DOI":"10.1007\/978-3-540-30218-6_19"},{"key":"e_1_2_9_18_1","doi-asserted-by":"publisher","DOI":"10.1002\/cpe.5157"},{"key":"e_1_2_9_19_1","doi-asserted-by":"crossref","unstructured":"AbdelgadirAT PathanASK AhmedM.On the performance of MPI\u2010OpenMP on a 12 nodes multi\u2010core cluster. 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