Raphael Y. de Camargo

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Fabio KonFull Professor of Computer ScienceVerified email at ime.usp.br
Alfredo GoldmanAssociate Professor of Computer Science, University of São PauloVerified email at ime.usp.br
Denis TrystramProfessor of Computer Science, Grenoble Institute of Technology, FranceVerified email at imag.fr
David Correa Martins JrCentro de Matemática, Computação e Cognição - Universidade Federal do ABCVerified email at ufabc.edu.br
Marcos AmarisFederal University of ParáVerified email at ufpa.br
Daniel CordeiroUniversidade de São PauloVerified email at usp.br
Siang Wun SongUniversidade de São PauloVerified email at ime.usp.br
Andre M. CravoAssociate Professor of Cognitive Neuroscience, UFABC, BrazilVerified email at ufabc.edu.br
Marcelo Bussotti ReyesProfessor Associado, Centro de Matemática, Computação e CogniçãoVerified email at ufabc.edu.br
Renan RecioUniversidade Federal do ABCVerified email at ufabc.edu.br
Fernando CastorUtrecht University and Universidade Federal de PernambucoVerified email at cin.ufpe.br
Marcelo S. CaetanoAssociate Professor of Cognitive Science, Universidade Federal do ABCVerified email at ufabc.edu.br
Luis Sant AnaPHD candidate, Univesidade Federal do ABCVerified email at ufabc.edu.br
Louise Catheryne BarneInstitute of Cognitive Neuroscience, UCLVerified email at ucl.ac.uk
Renato CerqueiraIBM Research BrazilVerified email at br.ibm.com
Fabio M. CostaFull Professor - Computer Science - Instituto de Informatica - Universidade Federal de GoiásVerified email at inf.ufg.br
Marcelo FingerProfessor of Computer Science, Universidade de São PauloVerified email at ime.usp.br
Peter M. E. ClaessensAdjunct professor in Cognition, Universidade do ABCVerified email at ufabc.edu.br
João Ricardo SatoUniversidade Federal do ABC, BrazilVerified email at ufabc.edu.br
Paul GraceSenior Lecturer, Computer Science, Aston University, UKVerified email at aston.ac.uk

Raphael Y. de Camargo

Universidade Federal do ABC

Verified email at ufabc.edu.br

HPC Machine Learning Smart Cities Computational Neuroscience


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A comparison of GPU execution time prediction using machine learning and analytical modeling M Amaris, RY de Camargo, M Dyab, A Goldman, D Trystram 2016 IEEE 15th International Symposium on Network Computing and Applications …, 2016	49	2016
Obtaining dynamic scheduling policies with simulation and machine learning D Carastan-Santos, RY De Camargo Proceedings of the International Conference for High Performance Computing …, 2017	36	2017
Strategies for checkpoint storage on opportunistic grids RY de Camargo, F Kon, R Cerqueira IEEE Distributed Systems Online 7 (9), 1-1, 2006	36	2006
A simple BSP-based model to predict execution time in GPU applications M Amaris, D Cordeiro, A Goldman, RY De Camargo 2015 IEEE 22nd International Conference on High Performance Computing (HiPC …, 2015	35	2015
Checkpointing-based rollback recovery for parallel applications on the integrade grid middleware RY de Camargo, A Goldchleger, F Kon, A Goldman Proceedings of the 2nd workshop on Middleware for grid computing, 35-40, 2004	34	2004
Gene regulatory networks inference using a multi-GPU exhaustive search algorithm FF Borelli, RY de Camargo, DC Martins, L Rozante BMC bioinformatics 14 (18), 1-12, 2013	31	2013
One can only gain by replacing EASY Backfilling: A simple scheduling policies case study D Carastan-Santos, RY De Camargo, D Trystram, S Zrigui 2019 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid …, 2019	21	2019
Application execution management on the InteGrade opportunistic grid middleware FJS e Silva, F Kon, A Goldman, M Finger, RY De Camargo, F Castor Filho, ... Journal of Parallel and Distributed Computing 70 (5), 573-583, 2010	20	2010
A multi‐GPU algorithm for large‐scale neuronal networks RY De Camargo, L Rozante, SW Song Concurrency and Computation: Practice and Experience 23 (6), 556-572, 2011	17	2011
Portable checkpointing and communication for BSP applications on dynamic heterogeneous Grid environments RY De Camargo, F Kon, A Goldman 17th International Symposium on Computer Architecture and High Performance …, 2005	17	2005
A common representation of time across visual and auditory modalities LC Barne, JR Sato, RY de Camargo, PME Claessens, MS Caetano, ... Neuropsychologia 119, 223-232, 2018	16	2018
The Grid architectural pattern: Leveraging distributed processing capabilities RY de Camargo, A Goldchleger, M Carneiro, F Kon Pattern Languages of Program Design 5, 337-356, 2006	16	2006
Strategies for storage of checkpointing data using non-dedicated repositories on grid systems RY de Camargo, R Cerqueira, F Kon Proceedings of the 3rd international workshop on Middleware for grid …, 2005	16	2005
Finding exact hitting set solutions for systems biology applications using heterogeneous GPU clusters D Carastan-Santos, RY de Camargo, DC Martins Jr, SW Song, ... Future Generation Computer Systems 67, 418-429, 2017	15	2017
Exploiting a generic approach for constructing mobile device applications J Ueyama, VPV Pinto, ERM Madeira, P Grace, TMM Jonhson, ... Proceedings of the Fourth International ICST Conference on COMmunication …, 2009	15	2009
Design and implementation of a middleware for data storage in opportunistic grids RY De Camargo, F Kon Seventh IEEE International Symposium on Cluster Computing and the Grid …, 2007	15	2007
Checkpointing BSP parallel applications on the InteGrade Grid middleware RY De Camargo, A Goldchleger, F Kon, A Goldman Concurrency and Computation: Practice and Experience 18 (6), 567-579, 2006	14	2006
Distributed data storage for opportunistic grids RY de Camargo, F Kon Proceedings of the 3rd international Middleware doctoral symposium, 3, 2006	12	2006
Plb-hec: A profile-based load-balancing algorithm for heterogeneous cpu-gpu clusters L Sant'Ana, D Cordeiro, R Camargo 2015 IEEE International Conference on Cluster Computing, 96-105, 2015	11	2015
A parallel maximum subarray algorithm on gpus CS Ferreira, RY Camargo, SW Song 2014 International Symposium on Computer Architecture and High Performance …, 2014	11	2014

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