Guidelines for enhancing data locality in selected machine learning algorithms
Article in journal

Imen Chakroun, Tom Vander Aa, Tomas, J. Ashby. Guidelines for enhancing data locality in selected machine learning algorithms. Intelligent Data Analysis, vol. 23, no. 5, pp. 1003-1020, 2019


Repository link: https://128.84.21.199/abs/2001.03000
DOI: 10.3233/IDA-184287
Parallelware Tools: An Experimental Evaluation on POWER Systems
Publication in Conference Proceedings/Workshop

Arenaz M., Martorell X. (2019) Parallelware Tools: An Experimental Evaluation on POWER Systems. In: Weiland M., Juckeland G., Alam S., Jagode H. (eds) High Performance Computing. ISC High Performance 2019. Lecture Notes in Computer Science, vol 11887. Springer, Cham. https://doi.org/10.1007/978-3-030-34356-9_27


Repository link: https://arxiv.org/abs/2102.02582v1
DOI: https://doi.org/10.1007/978-3-030-34356-9_27
Reviewing Data Access Patterns and Computational Redundancy for Machine Learning Algorithms
Publication in Conference Proceedings/Workshop

Imen Chakroun, Tom Vander Aa, Tom Ashby. Reviewing Data Access Patterns and Computational Redundancy for Machine Learning Algorithms. 4th International Conference on Big Data Analytics, Data Mining and Computational Intelligence (BIGDACI2019). IADIS. 2019.


Repository link: https://arxiv.org/abs/1904.11203
SMURFF: a High-Performance Framework for Matrix Factorization
Oral presentation

Tom Vander Aa. SMURFF: a High-Performance Framework for Matrix Factorization. IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS2019)


SMURFF: a High-Performance Framework for Matrix Factorization
Publication in Conference Proceedings/Workshop

Tom Vander Aa, Imen Chakroun, Thomas J. Ashby. SMURFF: a High-Performance Framework for Matrix Factorization. 1st IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS 2019). IEEE. 2019.


Repository link: https://arxiv.org/abs/1904.02514
SMURFF: a High-Performance Framework for Matrix Factorization Methods (extended abstract)
Abstract

Tom Vander Aa, Imen Chakroun, Tom Ashby, Jaak Simm, Adam Arany, Yves Moreau, Thanh Le Van, José Felipe Golib Dzib, Jörg Wegner, Vladimir Chupakhin, Hugo Ceulemans, Roel Wuyts, Wilfried Verachtert. SMURFF: a High-Performance Framework for Matrix Factorization Methods (extended abstract). 31st Benelux Conference on Artificial Intelligence (BNAIC 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019). 2019.


Repository link: http://ceur-ws.org/Vol-2491/abstract48.pdf
Tasking in accelerators: performance evaluation
Publication in Conference Proceedings/Workshop

Toledo, L., Peña, A.J., Catalán, S., and Valero-Lara, P.. Tasking in accelerators: performance evaluation. A: International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT). "2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT): Gold Coast, Australia: 5-7 December 2019: proceeding". Institute of Electrical and Electronics Engineers (IEEE), 2019, p. 127-132. 


Repository link: https://upcommons.upc.edu/handle/2117/185805
DOI: 10.1109/PDCAT46702.2019.00034
The EPEEC H2020 Project: Programming Productivity at Exascale
Oral presentation

Antonio J. Peña. The EPEEC H2020 Project: Programming Productivity at Exascale. Supercomputing Conference 2019.


Leveraging Parallelware in MAESTRO and EPEEC
Oral presentation

Arenaz, M. Leveraging Parallelware in MAESTRO and EPEEC. Contributions by Appentra and Enhancements to Parallelware. Supercomputing Conference 2018.