Marcos Amaris

Orcid: 0000-0002-8171-4931

According to our database1, Marcos Amaris authored at least 11 papers between 2015 and 2023.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2023
Evaluating execution time predictions on GPU kernels using an analytical model and machine learning techniques.
J. Parallel Distributed Comput., January, 2023

2022
A Profile-Based AI-Assisted Dynamic Scheduling Approach for Heterogeneous Architectures.
Int. J. Parallel Program., 2022

2021
Efficient Prediction of Region-wide Traffic States in Public Bus Networks using LSTMs.
Proceedings of the 24th IEEE International Intelligent Transportation Systems Conference, 2021

2020
PDAWL: Profile-Based Iterative Dynamic Adaptive WorkLoad Balance on Heterogeneous Architectures.
Proceedings of the Job Scheduling Strategies for Parallel Processing, 2020

2019
Generic algorithms for scheduling applications on heterogeneous platforms.
Concurr. Comput. Pract. Exp., 2019

2017
Generic algorithms for scheduling applications on heterogeneous multi-core platforms.
CoRR, 2017

Autotuning CUDA compiler parameters for heterogeneous applications using the OpenTuner framework.
Concurr. Comput. Pract. Exp., 2017

Generic Algorithms for Scheduling Applications on Hybrid Multi-core Machines.
Proceedings of the Euro-Par 2017: Parallel Processing - 23rd International Conference on Parallel and Distributed Computing, Santiago de Compostela, Spain, August 28, 2017

2016
A comparison of GPU execution time prediction using machine learning and analytical modeling.
Proceedings of the 15th IEEE International Symposium on Network Computing and Applications, 2016

OpenMP is Not as Easy as It Appears.
Proceedings of the 49th Hawaii International Conference on System Sciences, 2016

2015
A Simple BSP-based Model to Predict Execution Time in GPU Applications.
Proceedings of the 22nd IEEE International Conference on High Performance Computing, 2015


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