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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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Using machine learning techniques to analyze the performance of concurrent kernel execution on GPUs., , , , , and . Future Gener. Comput. Syst., (2020)Kernel concurrency opportunities based on GPU benchmarks characterization., , , , , , and . Clust. Comput., 23 (1): 177-188 (2020)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)Maximizing the GPU resource usage by reordering concurrent kernels submission., , , , , , and . Concurr. Comput. Pract. Exp., (2019)A 3D Mathematical Breast Texture Model With Parameters Automatically Inferred From Clinical Breast CT Images., , , , , and . IEEE Trans. Medical Imaging, 42 (4): 1107-1120 (April 2023)Relatório da avaliação do sistema JEMS: equipe discovering usability., , , , and . IHC, page 87. ACM, (2006)Comparison of Lesion Extent and Contrast-Agent Uptake in Breast Tomosynthesis versus Cone-Beam Breast CT., , , , and . Digital Mammography / IWDM, volume 7361 of Lecture Notes in Computer Science, page 434-441. Springer, (2012)On the use of divergence balanced H(div)-L2 pair of approximation spaces for divergence-free and robust simulations of Stokes, coupled Stokes-Darcy and Brinkman problems., , and . Math. Comput. Simul., (2020)Analysis of Cedbt and CESM Performance Using a Realistic X-Ray Simulation Platform., , , , and . ISBI, page 1070-1073. IEEE, (2019)A Virtual Human Breast Phantom Using Surface Meshes and Geometric Internal Structures., , , , and . Digital Mammography / IWDM, volume 8539 of Lecture Notes in Computer Science, page 356-363. Springer, (2014)