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European joint Effort toward a Highly Productive Programming Environment for Heterogeneous Exascale Computing (EPEEC)


Training Plan

This deliverable will specify all training needs, activities, and materials needed during the project. This plan will also include exploiting the existing training channels such as PRACE training centres, among others to participate actively with the suggested trainings, and trying to accommodate the courses to their requirements, if any. The initial idea is to organise two courses each year about the hot topics defined by the various work package leaders, as well as to include sessions in summer schools such as PUMPS, ISC/SC tutorials or BoF sessions, or the organisation of a dedicated PATC course at BSC and the participation in the yearly hackathon.

Initial report of OmpSs+OpenACC/OpenMP interoperability

Initial report of the proposed syntax for the combination of OmpSs and accelerator kernels defined with OpenACC and OpenMP syntaxes. Includes semantics and syntax compatibility of OmpSs and OpenACC/OpenMP accelerator kernel definitions, as well as proposals of interoperability improvement for the OpenACC and OpenMP specifications. This is an outcome of Task 3.2.

Dissemination and exploitation plan

The Dissemination and Exploitation Plan will provide tools for the marketing activities that will include the website, customer leaflets, social media, press strategy, scientific publications policy, online material strategy, list of key events to be attended, and calendar of activities. This deliverable will include the initial analysis of the exploitation context, business opportunities, and exploitable results.

First dissemination report

The dissemination report will include the various dissemination activities from the first and a half years and its analysis.

Initial application specification and porting report

This document will report on the main programming features of application codes and the definition of simplified test programs that are representative of the main compute-intensive, data-intensive, and extreme-data parts of the full application codes. It will also report on the first development actions on these simplified test programs. This is an outcome of Task 5.2.

Specification report on requirements of application codes

This document will describe the required developments on the application codes in view of adopting the GASPI+OmpSs and OmpSs@ArgoDSM models, together with a porting scenario. This is an outcome of Task 5.1.

Initial software prototypes WP4

Early prototype releases of enhanced GPI, ArgoDSM, OmpSs with ArgoDSM support, and BSC tools. This is an outcome of Tasks 4.1 to 4.3.

Testing and development platform setup

This is an outcome of Task 4.4 and will be leveraged during the rest of the project and beyond.

Intermediate software prototypes WP4

Updates from Tasks 4.1 to 4.3.

Data management plan

This deliverable describes the life cycle for all data sets being updated regularly during the development of the project.

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SMURFF: a High-Performance Framework for Matrix Factorization

Author(s): Tom Vander Aa, Imen Chakroun, Thomas J. Ashby
Published in: 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), 2019, Page(s) 304-308
DOI: 10.1109/aicas.2019.8771607


Author(s): Imen Chakroun, Tom Vander Aa, Tom Ashby
Published in: 4th International Conference on Big Data Analytics, Data Mining and Computational Intelligence, 2019, Page(s) 31-38

Bandwidth-Aware Page Placement in NUMA

Author(s): David Gureya, João Neto, Reza Karimi, João Barreto, Pramod Bhatotia, Vivien Quema, Rodrigo Rodrigues, Paolo Romano, Vladimir Vlassov
Published in: Proceedings of the 34th IEEE International Parallel & Distributed Processing Symposium (IPDPS), 2020, Issue 2020, 2020

Tasking in Accelerators: Performance Evaluation

Author(s): Leonel Toledo, Antonio J. Pena, Sandra Catalan, Pedro Valero-Lara
Published in: 2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT), 2019, Page(s) 127-132
DOI: 10.1109/PDCAT46702.2019.00034

Guidelines for enhancing data locality in selected machine learning algorithms

Author(s): Imen Chakroun, Tom Vander Aa, Tomas J. Ashby
Published in: Intelligent Data Analysis, Issue 23/5, 2019, Page(s) 1003-1020, ISSN 1088-467X
DOI: 10.3233/ida-184287