Amaris, Marcos; Camargo, Raphael; Cordeiro, Daniel de Angelis 
; Goldman, Alfredo ; Trystram, Denise
Fonte: Journal of Parallel and Distributed Computing. Unidades: EACH, IME
Assunto: APRENDIZADO COMPUTACIONAL
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AMARIS, Marcos et al. Evaluating execution time predictions on GPU kernels using an analytical model and machine learning techniques. Journal of Parallel and Distributed Computing, v. 171, n. ja 2023, p. 66-78, 2023Tradução . . Disponível em: https://doi.org/10.1016/j.jpdc.2022.09.002. Acesso em: 03 nov. 2024.APA
Amaris, M., Camargo, R., Cordeiro, D. de A., Goldman, A., & Trystram, D. (2023). Evaluating execution time predictions on GPU kernels using an analytical model and machine learning techniques. Journal of Parallel and Distributed Computing, 171( ja 2023), 66-78. doi:10.1016/j.jpdc.2022.09.002NLM
Amaris M, Camargo R, Cordeiro D de A, Goldman A, Trystram D. Evaluating execution time predictions on GPU kernels using an analytical model and machine learning techniques [Internet]. Journal of Parallel and Distributed Computing. 2023 ; 171( ja 2023): 66-78.[citado 2024 nov. 03 ] Available from: https://doi.org/10.1016/j.jpdc.2022.09.002Vancouver
Amaris M, Camargo R, Cordeiro D de A, Goldman A, Trystram D. Evaluating execution time predictions on GPU kernels using an analytical model and machine learning techniques [Internet]. Journal of Parallel and Distributed Computing. 2023 ; 171( ja 2023): 66-78.[citado 2024 nov. 03 ] Available from: https://doi.org/10.1016/j.jpdc.2022.09.002
