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Using machine learning techniques to analyze the performance of concurrent kernel execution on GPUs.

, , , , , and . Future Gener. Comput. Syst., (2020)

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Workshop 15: MPP Parallel Programming Models - Special Edition Machine Learning Performance and Security., , , and . IPDPS Workshops, page 838-839. IEEE, (2020)Video Processing on GPU: Analysis of Data Transfer Overhead., , and . SBAC-PAD (Workshops), page 18-23. IEEE Computer Society, (2014)Exploring parallelism in volume ray casting: understanding the programming issues of multithreaded accelerators., , , , and . PMAM, page 64-73. ACM, (2012)Parallel Volume Rendering for Ocean Visualization in a Cluster of PCs., , , , and . GEOINFO, page 291-304. INPE, (2004)Dynamic screen division for load balancing the raycasting of irregular data., , , and . CLUSTER, page 1-10. IEEE Computer Society, (2009)Analysis and Characterization of GPU Benchmarks for Kernel Concurrency Efficiency., , , , , and . CARLA, volume 796 of Communications in Computer and Information Science, page 71-86. Springer, (2017)Avaliação de Sistemas de Coerência de Cache em Arquiteturas ODBMs Cliente-Servidor., , , , and . SBBD, page 68-82. UFRGS, (2002)Towards a Dataflow Runtime Environment for Edge, Fog and In-Situ Computing., , , , , , and . SBAC-PAD (Workshops), page 115-120. IEEE Computer Society, (2017)Performance Analysis and Optimization of the Vector-Kronecker Product Multiplication., , , and . SBAC-PAD, page 265-272. IEEE, (2020)Work Distribution for Parallel ZSweep Algorithm., , , , and . SIBGRAPI, page 107-116. IEEE Computer Society, (2003)