Oral presentation

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

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).

This paper is included in the Public Health Emergency #COVID19 Initiative repository

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. 2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS), New Orleans, LA, USA, 2020 pp. 546-556. doi: 10.1109/IPDPS47924.2020.00063

DOI: 10.1109/IPDPS47924.2020.00063
Oral presentation

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

Publication in Conference Proceedings/Workshop

Pavanakumar Mohanamuraly and Gabriel Staffelbach. 2020. Hardware Locality-Aware Partitioning and Dynamic Load-Balancing of Unstructured Meshes for Large-Scale Scientific Applications. In Proceedings of the Platform for Advanced Scientific Computing Conference (PASC ’20). Association for Computing Machinery, New York, NY, USA, Article 7, 1–10. DOI:https://doi.org/10.1145/3394277.3401851

DOI: https://doi.org/10.1145/3394277.3401851

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


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.