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Artigo de periodico
Heart rate variability predicts the subject-driven cognitive states (2022)
Farfán, Carlos Enrique Paucar ; Bruel, Pedro ; Goldman, Alfredo ; Takahashi, Daniel Yasumasa ; Fujita, André
Source: Research Square. Unidade: IME
Assunto: BIOINFORMÁTICA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
FARFÁN, Carlos Enrique Paucar et al. Heart rate variability predicts the subject-driven cognitive states. Research Square, 2022Tradução . . Disponível em: https://doi.org/10.21203/rs.3.rs-1957712/v1. Acesso em: 03 out. 2024.
APA
Farfán, C. E. P., Bruel, P., Goldman, A., Takahashi, D. Y., & Fujita, A. (2022). Heart rate variability predicts the subject-driven cognitive states. Research Square. doi:10.21203/rs.3.rs-1957712/v1
NLM
Farfán CEP, Bruel P, Goldman A, Takahashi DY, Fujita A. Heart rate variability predicts the subject-driven cognitive states [Internet]. Research Square. 2022 ;[citado 2024 out. 03 ] Available from: https://doi.org/10.21203/rs.3.rs-1957712/v1
Vancouver
Farfán CEP, Bruel P, Goldman A, Takahashi DY, Fujita A. Heart rate variability predicts the subject-driven cognitive states [Internet]. Research Square. 2022 ;[citado 2024 out. 03 ] Available from: https://doi.org/10.21203/rs.3.rs-1957712/v1
Trabalho de evento
Autotuning LLVM optimization passes for matrix multiplication in Rust (2020)
Sousa, Emanuel Lima de ; Bruel, Pedro ; Goldman, Alfredo
Source: Anais. Conference titles: Escola Regional de Alto Desempenho de São Paulo - ERAD-SP. Unidades: IME, IF
Subjects: MONTADORES E COMPILADORES, GERAÇÃO DE CÓDIGO, ARQUITETURA DE SOFTWARE
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SOUSA, Emanuel Lima de e BRUEL, Pedro e GOLDMAN, Alfredo. Autotuning LLVM optimization passes for matrix multiplication in Rust. 2020, Anais.. Porto Alegre: SBC, 2020. Disponível em: https://doi.org/10.5753/eradsp.2020.16880. Acesso em: 03 out. 2024.
APA
Sousa, E. L. de, Bruel, P., & Goldman, A. (2020). Autotuning LLVM optimization passes for matrix multiplication in Rust. In Anais. Porto Alegre: SBC. doi:10.5753/eradsp.2020.16880
NLM
Sousa EL de, Bruel P, Goldman A. Autotuning LLVM optimization passes for matrix multiplication in Rust [Internet]. Anais. 2020 ;[citado 2024 out. 03 ] Available from: https://doi.org/10.5753/eradsp.2020.16880
Vancouver
Sousa EL de, Bruel P, Goldman A. Autotuning LLVM optimization passes for matrix multiplication in Rust [Internet]. Anais. 2020 ;[citado 2024 out. 03 ] Available from: https://doi.org/10.5753/eradsp.2020.16880
Trabalho de evento
Autotuning under tight budget constraints: a transparent design of experiments approach (2019)
Bruel, Pedro; Masnada, Steven Quinito; Videau, Brice; Legrand, Arnaud; Vincent, Jean-Marc; Goldman, Alfredo
Source: Proceedings. Conference titles: IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing - CCGRID. Unidade: IME
Subjects: COMPUTAÇÃO GRÁFICA, CIRCUITOS INTEGRADOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
BRUEL, Pedro et al. Autotuning under tight budget constraints: a transparent design of experiments approach. 2019, Anais.. Piscataway: IEEE, 2019. Disponível em: https://doi.org/10.1109/CCGRID.2019.00026. Acesso em: 03 out. 2024.
APA
Bruel, P., Masnada, S. Q., Videau, B., Legrand, A., Vincent, J. -M., & Goldman, A. (2019). Autotuning under tight budget constraints: a transparent design of experiments approach. In Proceedings. Piscataway: IEEE. doi:10.1109/CCGRID.2019.00026
NLM
Bruel P, Masnada SQ, Videau B, Legrand A, Vincent J-M, Goldman A. Autotuning under tight budget constraints: a transparent design of experiments approach [Internet]. Proceedings. 2019 ;[citado 2024 out. 03 ] Available from: https://doi.org/10.1109/CCGRID.2019.00026
Vancouver
Bruel P, Masnada SQ, Videau B, Legrand A, Vincent J-M, Goldman A. Autotuning under tight budget constraints: a transparent design of experiments approach [Internet]. Proceedings. 2019 ;[citado 2024 out. 03 ] Available from: https://doi.org/10.1109/CCGRID.2019.00026
Artigo de periodico
Autotuning CUDA compiler parameters for heterogeneous applications using the OpenTuner framework (2017)
Bruel, Pedro; Amarís, Marcos; Goldman, Alfredo
Source: Concurrency and Computation: Practice and Experience. Unidade: IME
Assunto: COMPUTAÇÃO GRÁFICA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
BRUEL, Pedro e AMARÍS, Marcos e GOLDMAN, Alfredo. Autotuning CUDA compiler parameters for heterogeneous applications using the OpenTuner framework. Concurrency and Computation: Practice and Experience, v. 29, n. 22, p. 1-15, 2017Tradução . . Disponível em: https://doi.org/10.1002/cpe.3973. Acesso em: 03 out. 2024.
APA
Bruel, P., Amarís, M., & Goldman, A. (2017). Autotuning CUDA compiler parameters for heterogeneous applications using the OpenTuner framework. Concurrency and Computation: Practice and Experience, 29( 22), 1-15. doi:10.1002/cpe.3973
NLM
Bruel P, Amarís M, Goldman A. Autotuning CUDA compiler parameters for heterogeneous applications using the OpenTuner framework [Internet]. Concurrency and Computation: Practice and Experience. 2017 ; 29( 22): 1-15.[citado 2024 out. 03 ] Available from: https://doi.org/10.1002/cpe.3973
Vancouver
Bruel P, Amarís M, Goldman A. Autotuning CUDA compiler parameters for heterogeneous applications using the OpenTuner framework [Internet]. Concurrency and Computation: Practice and Experience. 2017 ; 29( 22): 1-15.[citado 2024 out. 03 ] Available from: https://doi.org/10.1002/cpe.3973

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