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Exploiting Task-Parallelism on GPU Clusters via OmpSs and rCUDA Virtualization.

, , , and . TrustCom/BigDataSE/ISPA (3), page 160-165. IEEE, (2015)978-1-4673-7952-6.

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Programming models and scheduling techniques for heterogeneous architectures.. Polytechnic University of Catalonia, Spain, (2015)Productive Programming of GPU Clusters with OmpSs., , , , , , and . IPDPS, page 557-568. IEEE Computer Society, (2012)Heterogeneous Streaming., , , , , , , , , and 8 other author(s). IPDPS Workshops, page 611-620. IEEE Computer Society, (2016)Hierarchical Task-Based Programming With StarSs., , , and . Int. J. High Perform. Comput. Appl., 23 (3): 284-299 (2009)Exploring the interoperability of remote GPGPU virtualization using rCUDA and directive-based programming models., , , , , and . J. Supercomput., 74 (11): 5628-5642 (2018)Optimizing the Exploitation of Multicore Processors and GPUs with OpenMP and OpenCL., , , , , , , , and . LCPC, volume 6548 of Lecture Notes in Computer Science, page 215-229. Springer, (2010)The SONATA data format for efficient description of large-scale network models., , , , , , , , , and 7 other author(s). PLoS Comput. Biol., (2020)P-means, a parallel clustering algorithm for a heterogeneous multi-processor environment., , , and . HPCS, page 239-248. IEEE, (2011)SSMART: smart scheduling of multi-architecture tasks on heterogeneous systems., , , and . WACCPD@SC, page 1:1-1:11. ACM, (2015)Ompss: a Proposal for Programming Heterogeneous Multi-Core Architectures., , , , , , and . Parallel Process. Lett., 21 (2): 173-193 (2011)