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1
Autotuning CUDA compiler parameters for heterogeneous applications using the OpenTuner framework
Autotuning CUDA compiler parameters for heterogeneous applications using the OpenTuner framework
Material Type:
Article 		Add to e-Shelf

Autotuning CUDA compiler parameters for heterogeneous applications using the OpenTuner framework

Pedro Bruel Marcos Amarís; Alfredo Goldman

Concurrency and Computation: Practice and Experience v. 29, n. 22, p. 1-15, 2017

Chichester 2017

Available at IME - Inst. Matemática e Estatística    (PROD-2859520 )(GetIt)

2
Autotuning GPU compiler parameters using OpenTuner
Autotuning GPU compiler parameters using OpenTuner
Material Type:
Conference Paper 		Add to e-Shelf

Autotuning GPU compiler parameters using OpenTuner

Pedro Bruel Marcos Amarís; Alfredo Goldman; Simpósio em Sistemas Computacionais de Alto Desempenho (16. 2015 Florianópolis)

Anais Porto Alegre : SBC, 2015

Porto Alegre SBC 2015

Online access. The library also has physical copies.

3
A profile-based AI-assisted dynamic scheduling approach for heterogeneous architectures
A profile-based AI-assisted dynamic scheduling approach for heterogeneous architectures
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Article 		Add to e-Shelf

A profile-based AI-assisted dynamic scheduling approach for heterogeneous architectures

Tongsheng Geng Marcos Amarís; Stéphane Zuckerman; Alfredo Goldman; Guang R Gao; Jean-Luc Gaudiot

International Journal of Parallel Programming New York v. 50, n. 1, p. 115-151, 2022

New York Springer 2022

Available at IME - Inst. Matemática e Estatística    (PROD-3048700 )(GetIt)

4
A comparison of GPU execution time prediction using machine learning and analytical modeling
A comparison of GPU execution time prediction using machine learning and analytical modeling
Material Type:
Conference Paper 		Add to e-Shelf

A comparison of GPU execution time prediction using machine learning and analytical modeling

Marcos Amarís Raphael Yokoingawa de Camargo; Mohamed Dyab; Alfredo Goldman; Denis Trystram; International Symposium on Network Computing and Applications (NCA) (15. 2016 Cambridge, MA)

Proceedings Piscataway: IEEE, 2016

Piscataway IEEE 2016

Available at IME - Inst. Matemática e Estatística    (PROD-2850396 )(GetIt)

5
OpenMP is not as easy as it appears
OpenMP is not as easy as it appears
Material Type:
Conference Paper 		Add to e-Shelf

OpenMP is not as easy as it appears

Rogério Aparecido Gonçalves Marcos Amarís; Thiago Kenji Okada; Pedro Bruel; Alfredo Goldman; Hawaii International Conference on System Sciences - HICSS (49. 2016 Koloa, HI)

Proceedings Piscataway : IEEE, 2016

Piscataway IEEE 2016

Available at IME - Inst. Matemática e Estatística    (PROD-2766296 )(GetIt)

6
A simple BSP-based model to predict execution time in GPU applications
A simple BSP-based model to predict execution time in GPU applications
Material Type:
Conference Paper 		Add to e-Shelf

A simple BSP-based model to predict execution time in GPU applications

Marcos Amarís Daniel Cordeiro; Alfredo Goldman; Raphael Y. de Camargo; IEEE International Conference on High Performance Computing - HiPC (22. 2015 Bangalore)

Proceedings Los Alamitos: IEEE, 2016

Los Alamitos IEEE 2016

Available at IME - Inst. Matemática e Estatística    (PROD-2759638 )(GetIt)

7
PDAWL profile-based iterative dynamic adaptive workload balance on heterogeneous architectures
PDAWL profile-based iterative dynamic adaptive workload balance on heterogeneous architectures
Material Type:
Conference Paper 		Add to e-Shelf

PDAWL profile-based iterative dynamic adaptive workload balance on heterogeneous architectures

Tongsheng Geng Marcos Amarís; Stéphane Zuckerman; Alfredo Goldman; Guang R Gao; Jean-Luc Gaudiot; Workshop on Job Scheduling Strategies for Parallel Processing - JSSPP (23. 2020 New Orleans, LA, USA)

Proceedings Cham : Springer, 2020

Cham Springer 2020

Available at IME - Inst. Matemática e Estatística    (PROD-3013903 )(GetIt)

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by this author/creator:

  1. Amarís, M
  2. Goldman, A
  3. Bruel, P
  4. Gao, G
  5. Camargo, R

on this subject:

  1. Computação Gráfica
  2. Programação Paralela
  3. Aprendizado Computacional
  4. Scheduling
  5. Arquiteturas Paralelas

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