• Performance meets programmabil...

*Result*: Performance meets programmabilty: Enabling native Python MPI tasks in PyCOMPSs

Title:
Performance meets programmabilty: Enabling native Python MPI tasks in PyCOMPSs
Authors:
Elshazly, Hatem Mohamed Abdelfattah Eid, Lordan Gomis, Francesc, Ejarque Artigas, Jorge, Badia Sala, Rosa Maria
Contributors:
Universitat Politècnica de Catalunya. Doctorat en Arquitectura de Computadors, Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Barcelona Supercomputing Center, Universitat Politècnica de Catalunya. CAP - Grup de Computació d'Altes Prestacions
Publisher Information:
Institute of Electrical and Electronics Engineers (IEEE)
Publication Year:
2020
Collection:
Universitat Politècnica de Catalunya, BarcelonaTech: UPCommons - Global access to UPC knowledge
Subject Terms:
Àrees temàtiques de la UPC::Informàtica::Arquitectura de computadors, Application program interfaces (Computer software), Parallel programming (Computer science), Electronic data processing -- Distributed processing, Hybrid programming models, Distributed computing, MPI, High performance computing, Task-based parallel programming models, Performance, Productivity, Python, Interfícies de programació d'aplicacions (Programari), Programació en paral·lel (Informàtica), Processament distribuït de dades
Document Type:
*Conference* conference object
File Description:
4 p.; application/pdf
Language:
English
Relation:
https://ieeexplore.ieee.org/document/9092419; info:eu-repo/grantAgreement/EC/H2020/800898/EU/EXAscale Quantification of Uncertainties for Technology and Science Simulation/ExaQUte; info:eu-repo/grantAgreement/MINECO//TIN2015-65316-P/ES/COMPUTACION DE ALTAS PRESTACIONES VII/; info:eu-repo/grantAgreement/AGAUR/V PRI/2014 SGR 1051; https://hdl.handle.net/2117/334333
DOI:
10.1109/PDP50117.2020.00016
Availability:
https://hdl.handle.net/2117/334333
https://doi.org/10.1109/PDP50117.2020.00016
Rights:
Open Access
Accession Number:
edsbas.AC76A0E9
Database:
BASE

*Further Information*

*©2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. ; The increasing complexity of modern and future computing systems makes it challenging to develop applications that aim for maximum performance. Hybrid parallel programming models offer new ways to exploit the capabilities of the underlying infrastructure. However, the performance gain is sometimes accompanied by increased programming complexity. We introduce an extension to PyCOMPSs, a high-level task-based parallel programming model for Python applications, to support tasks that use MPI natively as part of the task model. Without compromising application's programmability, using Native MPI tasks in PyCOMPSs offers up to 3x improvement in total performance for compute intensive applications and up to 1.9x improvement in total performance for I/O intensive applications over sequential implementation of the tasks. ; This work is partially supported by the European Union through the Horizon 2020 research and innovation programme under contracts 721865 (EXPERTISE Project) and 800898 (ExaQUte project), by the Spanish Government (TIN2015- 65316-P) and the Generalitat de Catalunya (contract 2014- SGR-1051). ; Peer Reviewed ; Postprint (author's final draft)*