Publication in Conference Proceedings/Workshop

Tom Vander Aa, Xiangju Qin, Paul Blomstedt, Roel Wuyts, Wilfried Verachtert, Samuel Kaski. A High-Performance Implementation of Bayesian Matrix Factorization with Limited Communication. International Conference on Computational Science (ICCS 2020).


DOI: 10.1007/978-3-030-50433-5_1
Publication in Conference Proceedings/Workshop

Gureya, D., Neto, J., Karimi, R., Barreto, J, Bhatotia, P., Quema, V., Rodrigues, R., Romano, P., Vlassov, V. Bandwidth-Aware Page Placement in NUMA. 34th IEEE International Parallel & Distributed Processing Symposium (IPDPS), 2020. arXiv:2003.03304 [cs.DC]


Oral presentation

Antonio J. Peña. EPEEC’s Advances toward Programming Productivity for Heterogeneity at Large Scale. EuroExaScale 2020 (HiPEAC 2020 Conference).


Other

Manuel Aranz, Xavier Martorell and Antonio J. Peña. Programming Guidelines for Parellel Computing. EPEEC. May 2020.


Thesis/dissertation

Orestis R. Korakitis. Towards supporting Composability of Directive-based Programming Models for Heterogeneous Computing. 2020


Oral presentation

Tom Vander Aa. Exascale Matrix Factorization: Machine Learning on Supercomputers to Find New Drugs. EuroHPC Summit Week 2019.


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


DOI: 10.3233/IDA-184287
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.


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.


Oral presentation

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