{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,26]],"date-time":"2025-12-26T16:44:43Z","timestamp":1766767483766,"version":"3.38.0"},"reference-count":18,"publisher":"SAGE Publications","issue":"2","license":[{"start":{"date-parts":[[2014,8,12]],"date-time":"2014-08-12T00:00:00Z","timestamp":1407801600000},"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":[[2015,5]]},"abstract":"<jats:p> A block-structured adaptive mesh refinement (AMR) technique has been used to obtain numerical solutions for many scientific applications. Some block-structured AMR approaches have focused on forming patches of non-uniform sizes where the size of a patch can be tuned to the geometry of a region of interest. In this paper, we develop strategies for adaptive execution of block-structured AMR applications on GPUs, for hyperbolic directionally split solvers. While effective hybrid execution strategies exist for applications with uniform patches, our work considers efficient execution of non-uniform patches with different workloads. Our techniques include bin-packing work units to load balance GPU computations, adaptive asynchronism between CPU and GPU executions using a knapsack formulation, and scheduling communications for multi-GPU executions. Our experiments with synthetic and real data, for single-GPU and multi-GPU executions, on Tesla S1070 and Fermi C2070 clusters, show that our strategies result in up to a 3.23 speedup in performance over existing strategies. <\/jats:p>","DOI":"10.1177\/1094342014545546","type":"journal-article","created":{"date-parts":[[2014,8,13]],"date-time":"2014-08-13T13:48:12Z","timestamp":1407937692000},"page":"135-153","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Adaptive executions of hyperbolic block-structured AMR applications on GPU systems"],"prefix":"10.1177","volume":"29","author":[{"given":"Hari K","family":"Raghavan","sequence":"first","affiliation":[{"name":"Supercomputer Education and Research Centre, Indian Institute of Science, Bangalore, India"}]},{"given":"Sathish S","family":"Vadhiyar","sequence":"additional","affiliation":[{"name":"Supercomputer Education and Research Centre, Indian Institute of Science, Bangalore, India"}]}],"member":"179","published-online":{"date-parts":[[2014,8,12]]},"reference":[{"key":"bibr1-1094342014545546","doi-asserted-by":"publisher","DOI":"10.1109\/HIPC.1997.634498"},{"key":"bibr2-1094342014545546","doi-asserted-by":"publisher","DOI":"10.1016\/0021-9991(84)90073-1"},{"key":"bibr3-1094342014545546","series-title":"Advances in Parallel Computing","volume-title":"Applications, Tools and Techniques on the Road to Exascale Computing","volume":"22","author":"Blazewicz M","year":"2012"},{"key":"bibr4-1094342014545546","doi-asserted-by":"publisher","DOI":"10.1287\/ijoc.1070.0250"},{"key":"bibr5-1094342014545546","doi-asserted-by":"publisher","DOI":"10.1007\/11557654_103"},{"key":"bibr6-1094342014545546","doi-asserted-by":"publisher","DOI":"10.1086\/317361"},{"key":"bibr7-1094342014545546","isbn-type":"print","volume-title":"Computers and Intractability: A Guide to the Theory of NP-Completeness","author":"Garey M","year":"1979","ISBN":"https:\/\/id.crossref.org\/isbn\/0716710447"},{"key":"bibr8-1094342014545546","doi-asserted-by":"publisher","DOI":"10.1145\/2335755.2335791"},{"key":"bibr9-1094342014545546","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511791253"},{"key":"bibr10-1094342014545546","doi-asserted-by":"publisher","DOI":"10.1016\/S0010-4655(99)00501-9"},{"volume-title":"Knapsack Problems: Algorithms and Computer Implementations","year":"1990","author":"Martello S","key":"bibr11-1094342014545546"},{"key":"bibr12-1094342014545546","unstructured":"NVIDIA (2014) C Programming Guide Version 6.0. 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