{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T13:44:42Z","timestamp":1777902282491,"version":"3.51.4"},"reference-count":24,"publisher":"SAGE Publications","issue":"11","license":[{"start":{"date-parts":[[2013,10,22]],"date-time":"2013-10-22T00:00:00Z","timestamp":1382400000000},"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":["SIMULATION"],"published-print":{"date-parts":[[2013,11]]},"abstract":"<jats:p>The graphic processing unit (GPU) can perform some large-scale simulations in an economical way. However, harnessing the power of a GPU to discrete event simulation (DES) is difficult because of the mismatch between GPU\u2019s synchronous execution mode and DES\u2019s asynchronous time advance mechanism. In this paper, we present a GPU-based simulation kernel (gDES) to support DES and propose three algorithms to support high efficiency. Since both limited parallelism and redundant synchronization affect the performance of DES based on a GPU, we propose a breadth-expansion conservative time window algorithm to increase the degree of parallelism while retaining the number of synchronizations. By using the expansion method, it can import as many as possible \u2018safe\u2019 events. The irregular and dynamic requirement for storing the events leads to uneven and sparse memory usage, thereby causing waste of memory and unnecessary overhead. A memory management algorithm is proposed to store events in a balanced and compact way by using a lightweight stochastic method. When events processed by threads in a warp have different types, the performance of gDES decreases rapidly because of branch divergence. An event redistribution algorithm is proposed by reassigning events of the same type to neighboring threads to reduce the probability of branch divergence. We analyze the superiority of the proposed algorithms and gDES with a series of experiments. Compared to a CPU-based simulator on a multicore platform, the gDES can achieve up to 11\u00d7, 5\u00d7, and 8\u00d7 speedup in PHOLD, QUEUING NETWORK, and epidemic simulation, respectively.<\/jats:p>","DOI":"10.1177\/0037549713508839","type":"journal-article","created":{"date-parts":[[2013,10,23]],"date-time":"2013-10-23T03:33:52Z","timestamp":1382499232000},"page":"1335-1354","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":15,"title":["A GPU-based discrete event simulation kernel"],"prefix":"10.1177","volume":"89","author":[{"given":"Wenjie","family":"Tang","sequence":"first","affiliation":[{"name":"College of Computer, National University of Defense Technology, P.R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiping","family":"Yao","sequence":"additional","affiliation":[{"name":"College of Information System and Management, National University of Defense Technology, P.R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2013,10,22]]},"reference":[{"key":"bibr1-0037549713508839","volume-title":"Parallel and distribution simulation systems","author":"Fujimoto RM","year":"2000"},{"key":"bibr2-0037549713508839","doi-asserted-by":"publisher","DOI":"10.1109\/WSC.1994.717527"},{"issue":"62","key":"bibr3-0037549713508839","first-page":"53","volume":"11","author":"Carothers CD","year":"2000","journal-title":"J Parallel Distrib Comput"},{"key":"bibr4-0037549713508839","first-page":"6617","volume":"20","author":"Yao Y","year":"2008","journal-title":"J Syst Simul"},{"key":"bibr5-0037549713508839","doi-asserted-by":"publisher","DOI":"10.1109\/PADS.2005.1"},{"key":"bibr6-0037549713508839","volume-title":"NVIDIA CUDA compute unified device architecture programming guide","author":"NVIDIA Corporation","year":"2009","edition":"4"},{"key":"bibr7-0037549713508839","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-8659.2007.01012.x"},{"key":"bibr8-0037549713508839","doi-asserted-by":"publisher","DOI":"10.1109\/TITB.2010.2072963"},{"key":"bibr9-0037549713508839","volume-title":"CUDA C programming best practices guide","author":"NVIDIA Corporation","year":"2009","edition":"4"},{"key":"bibr10-0037549713508839","doi-asserted-by":"publisher","DOI":"10.1109\/WSC.2008.4736131"},{"key":"bibr11-0037549713508839","doi-asserted-by":"publisher","DOI":"10.1109\/PADS.2008.36"},{"key":"bibr12-0037549713508839","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-011-0675-4"},{"issue":"4","key":"bibr13-0037549713508839","first-page":"1","volume":"11","author":"Lysenko M","year":"2008","journal-title":"J Artif Soc Social Simul"},{"key":"bibr14-0037549713508839","doi-asserted-by":"publisher","DOI":"10.1109\/PADS.2006.15"},{"key":"bibr15-0037549713508839","doi-asserted-by":"publisher","DOI":"10.1177\/0037549709340781"},{"issue":"3","key":"bibr16-0037549713508839","first-page":"18","volume":"21","author":"Park H","year":"2011","journal-title":"ACM Trans Model Simul"},{"key":"bibr17-0037549713508839","first-page":"183","volume-title":"Proceedings of the SCS western multiconference on distributed simulation","author":"Lubachesky DB","year":"1988"},{"key":"bibr18-0037549713508839","first-page":"95","volume":"23","author":"Steinman J","year":"1991","journal-title":"Adv Parallel Distrib Simul"},{"key":"bibr19-0037549713508839","doi-asserted-by":"publisher","DOI":"10.1145\/151261.151266"},{"key":"bibr20-0037549713508839","doi-asserted-by":"publisher","DOI":"10.1109\/ASPDAC.2012.6164991"},{"key":"bibr21-0037549713508839","first-page":"23","volume-title":"Proceedings of 26th workshop on principles of advanced and distributed simulation (PADS2012)","author":"Kunz G","year":"2012"},{"key":"bibr22-0037549713508839","first-page":"557","volume-title":"Proceedings of the 46th ACM\/IEEE design automation conference (DAC)","author":"Chatterjee D","year":"2012"},{"key":"bibr23-0037549713508839","first-page":"149","volume-title":"Proceedings of the 15th Asia and South Paci\ufb01c design automation conference (ASP-DAC2012)","author":"Nanjundappa M","year":"2012"},{"key":"bibr24-0037549713508839","doi-asserted-by":"publisher","DOI":"10.1109\/MM.2008.31"}],"container-title":["SIMULATION"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/0037549713508839","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/0037549713508839","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/0037549713508839","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T11:25:02Z","timestamp":1777634702000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/0037549713508839"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2013,10,22]]},"references-count":24,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2013,11]]}},"alternative-id":["10.1177\/0037549713508839"],"URL":"https:\/\/doi.org\/10.1177\/0037549713508839","relation":{},"ISSN":["0037-5497","1741-3133"],"issn-type":[{"value":"0037-5497","type":"print"},{"value":"1741-3133","type":"electronic"}],"subject":[],"published":{"date-parts":[[2013,10,22]]}}}