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PyCOMPSs: Parallel computational workflows in Python
dc.contributor.author | Tejedor, Enric |
dc.contributor.author | Becerra Fontal, Yolanda |
dc.contributor.author | Alomar, Guillem |
dc.contributor.author | Queralt Calafat, Anna |
dc.contributor.author | Badia Sala, Rosa Maria |
dc.contributor.author | Torres Viñals, Jordi |
dc.contributor.author | Cortés, Toni |
dc.contributor.author | Labarta Mancho, Jesús José |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors |
dc.contributor.other | Barcelona Supercomputing Center |
dc.date.accessioned | 2017-11-16T07:53:14Z |
dc.date.available | 2017-11-16T07:53:14Z |
dc.date.issued | 2017-01-01 |
dc.identifier.citation | Tejedor, E., Becerra, Y., Alomar, G., Queralt, A., Badia, R. M., Torres, J., Cortés, T., Labarta, J. PyCOMPSs: Parallel computational workflows in Python. "International journal of high performance computing applications", 1 Gener 2017, vol. 31, núm. 1, p. 66-82. |
dc.identifier.issn | 1094-3420 |
dc.identifier.uri | http://hdl.handle.net/2117/110724 |
dc.description.abstract | The use of the Python programming language for scientific computing has been gaining momentum in the last years. The fact that it is compact and readable and its complete set of scientific libraries are two important characteristics that favour its adoption. Nevertheless, Python still lacks a solution for easily parallelizing generic scripts on distributed infrastructures, since the current alternatives mostly require the use of APIs for message passing or are restricted to embarrassingly parallel computations. In that sense, this paper presents PyCOMPSs, a framework that facilitates the development of parallel computational workflows in Python. In this approach, the user programs her script in a sequential fashion and decorates the functions to be run as asynchronous parallel tasks. A runtime system is in charge of exploiting the inherent concurrency of the script, detecting the data dependencies between tasks and spawning them to the available resources. Furthermore, we show how this programming model can be built on top of a Big Data storage architecture, where the data stored in the backend is abstracted and accessed from the application in the form of persistent objects. |
dc.description.sponsorship | This work has been supported by the following institutions: the Spanish Government with grant SEV-2011-00067 of Severo Ochoa Program and contract Computaci´on de Altas Prestaciones VI (TIN2012-34557); by the SGR programme (2014-SGR-1051) of the Catalan Government; by the project The Human Brain Project, funded by the European Commission under contract 604102; and by the Intel-BSC Exascale Lab collaboration. |
dc.format.extent | 17 p. |
dc.language.iso | eng |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Llenguatges de programació |
dc.subject.lcsh | Big data |
dc.subject.lcsh | Parallel programming (Computer science) |
dc.subject.other | Scientific computing |
dc.subject.other | Parallel programming models |
dc.subject.other | Python |
dc.subject.other | Big data storage |
dc.title | PyCOMPSs: Parallel computational workflows in Python |
dc.type | Article |
dc.subject.lemac | Macrodades |
dc.subject.lemac | Programació en paral·lel (Informàtica) |
dc.contributor.group | Universitat Politècnica de Catalunya. CAP - Grup de Computació d'Altes Prestacions |
dc.identifier.doi | 10.1177/1094342015594678 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | http://journals.sagepub.com/doi/10.1177/1094342015594678 |
dc.rights.access | Open Access |
local.identifier.drac | 21604412 |
dc.description.version | Postprint (author's final draft) |
dc.relation.projectid | info:eu-repo/grantAgreement/MINECO/6PN/TIN2012-34557 |
dc.relation.projectid | info:eu-repo/grantAgreement/AGAUR/V PRI/2014 SGR 1051 |
dc.relation.projectid | info:eu-repo/grantAgreement/EC/FP7/604102/EU/The Human Brain Project/HBP |
local.citation.author | Tejedor, E.; Becerra, Y.; Alomar, G.; Queralt, A.; Badia, R. M.; Torres, J.; Cortés, T.; Labarta, J. |
local.citation.publicationName | International journal of high performance computing applications |
local.citation.volume | 31 |
local.citation.number | 1 |
local.citation.startingPage | 66 |
local.citation.endingPage | 82 |
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