A Software Ecosystem to Save Money in DRAM and Increase Performance with Optane DIMMs
Oral presentation

Antonio J. Peña.  A Software Ecosystem to Save Money in DRAM and Increase Performance with Optane DIMMs. Intel HPC+AI Pavilion. 2020.


A High-Performance Implementation of Bayesian Matrix Factorization with Limited Communication
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).


Repository link: https://arxiv.org/pdf/2004.02561.pdf
DOI: 10.1007/978-3-030-50433-5_1
Bandwidth-Aware Page Placement in NUMA
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. 2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS), New Orleans, LA, USA, 2020 pp. 546-556. doi: 10.1109/IPDPS47924.2020.00063


Repository link: https://arxiv.org/abs/2003.03304
DOI: 10.1109/IPDPS47924.2020.00063
EPEEC’s Advances toward Programming Productivity for Heterogeneity at Large Scale
Oral presentation

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


Programming Guidelines for Parallel Computing
Other

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


Towards supporting Composability of Directive-based Programming Models for Heterogeneous Computing
Thesis/dissertation

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


Exascale Matrix Factorization: Machine Learning on Supercomputers to Find New Drugs
Oral presentation

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


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
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)