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Marcos Amaris
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2020 – today
- 2024
- [i2]Rafael Ferreira da Silva, Deborah Bard, Kyle Chard, Shaun De Witt, Ian T. Foster, Tom Gibbs, Carole A. Goble, William F. Godoy, Johan Gustafsson, Utz-Uwe Haus, Stephen Hudson, Shantenu Jha, Laila Los, Drew Paine, Frédéric Suter, Logan T. Ward, Sean R. Wilkinson, Marcos Amaris, Yadu N. Babuji, Jonathan Bader, Riccardo Balin, Daniel Balouek, Sarah J. Beecroft, Khalid Belhajjame, Rajat Bhattarai, Wes Brewer, Paul Brunk, Silvina Caíno-Lores, Henri Casanova, Daniela Cassol, Jared Coleman, Tainã Coleman, Iacopo Colonnelli, Anderson Andrei Da Silva, Daniel de Oliveira, Pascal Elahi, Nour Elfaramawy, Wael R. Elwasif, Brian Etz, Thomas Fahringer, Wesley Ferreira, Rosa Filgueira, Jacob Fosso Tande, Luiz Gadelha, Andy Gallo, Daniel Garijo, Yiannis Georgiou, Philipp Gritsch, Patricia Grubel, Amal Gueroudji, Quentin Guilloteau, Carlo Hamalainen, Rolando P. Hong Enriquez, Lauren Huet, Kevin Hunter Kesling, Paula Iborra, Shiva Jahangiri, Jan Janssen, Joe Jordan, Sehrish Kanwal, Liliane N. O. Kunstmann, Fabian Lehmann, Ulf Leser, Chen Li, Peini Liu, Jakob Lüttgau, Richard Lupat, José M. Fernández, Ketan Maheshwari, Tanu Malik, Jack Marquez, Motohiko Matsuda, Doriana Medic, Somayeh Mohammadi, Alberto Mulone, John-Luke Navarro, Kin Wai Ng, Klaus Nölp, Bruno de Paula Kinoshita, Ryan Prout, Michael R. Crusoe, Sashko Ristov, Stefan A Robila, Daniel Rosendo, Billy Rowell, Jedrzej Rybicki, Hector Sanchez, Nishant Saurabh, Sumit Kumar Saurav, Tom Scogland, Dinindu Senanayake, Woong Shin, Raül Sirvent, Tyler J. Skluzacek, Barry Sly-Delgado, Stian Soiland-Reyes, Abel Souza, Renan Souza, Domenico Talia, Nathan R. Tallent:
Workflows Community Summit 2024: Future Trends and Challenges in Scientific Workflows. CoRR abs/2410.14943 (2024) - 2023
- [j4]Marcos Amaris
, Raphael Y. de Camargo, Daniel Cordeiro, Alfredo Goldman, Denis Trystram:
Evaluating execution time predictions on GPU kernels using an analytical model and machine learning techniques. J. Parallel Distributed Comput. 171: 66-78 (2023) - 2022
- [j3]Tongsheng Geng
A Profile-Based AI-Assisted Dynamic Scheduling Approach for Heterogeneous Architectures. Int. J. Parallel Program. 50(1): 115-151 (2022) - 2021
- [c6]Marcos Amaris
Efficient Prediction of Region-wide Traffic States in Public Bus Networks using LSTMs. ITSC 2021: 2215-2220 - 2020
- [c5]Tongsheng Geng, Marcos Amaris
PDAWL: Profile-Based Iterative Dynamic Adaptive WorkLoad Balance on Heterogeneous Architectures. JSSPP 2020: 145-162
2010 – 2019
- 2019
- [j2]Marcos Amaris
Generic algorithms for scheduling applications on heterogeneous platforms. Concurr. Comput. Pract. Exp. 31(15) (2019) - 2017
- [j1]Pedro Bruel, Marcos Amaris
Autotuning CUDA compiler parameters for heterogeneous applications using the OpenTuner framework. Concurr. Comput. Pract. Exp. 29(22) (2017) - [c4]Marcos Amaris
Generic Algorithms for Scheduling Applications on Hybrid Multi-core Machines. Euro-Par 2017: 220-231 - [i1]Marcos Amaris, Giorgio Lucarelli, Clément Mommessin, Denis Trystram:
Generic algorithms for scheduling applications on heterogeneous multi-core platforms. CoRR abs/1711.06433 (2017) - 2016
- [c3]Rogério Aparecido Gonçalves
OpenMP is Not as Easy as It Appears. HICSS 2016: 5742-5751 - [c2]Marcos Amaris
A comparison of GPU execution time prediction using machine learning and analytical modeling. NCA 2016: 326-333 - 2015
- [c1]Marcos Amaris
A Simple BSP-based Model to Predict Execution Time in GPU Applications. HiPC 2015: 285-294
Coauthor Index
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last updated on 2024-12-03 21:20 CET by the dblp team
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