Suchergebnisse

Our society is generating an increasing amount of data at an unprecedented scale, variety, and speed. This also applies to numerous research areas, such as genomics, high energy physics, and astronomy, for which large-scale data processing has become crucial. However, there is still a gap between the traditional scientific computing ecosystem and big data analytics tools and frameworks. On the one hand, high performance computing (HPC) programming models lack productivity, and do not provide means for processing large amounts of data in a simple manner. On the other hand, existing big data processing tools have performance issues in HPC environments, and are not general-purpose. In this paper, we propose and evaluate PyCOMPSs, a task-based programming model for Python, as an excellent solution for distributed big data processing in HPC infrastructures. Among other useful features, PyCOMPSs offers a highly productive general-purpose programming model, is infrastructure-agnostic, and provides transparent data management with support for distributed storage systems. We show how two machine learning algorithms (Cascade SVM and K-means) can be developed with PyCOMPSs, and evaluate PyCOMPSs' productivity based on these algorithms. Additionally, we evaluate PyCOMPSs performance on an HPC cluster using up to 1,536 cores and 320 million input vectors. Our results show that PyCOMPSs achieves similar performance and scalability to MPI in HPC infrastructures, while providing a much more productive interface that allows the easy development of data analytics algorithms. ; This work has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement H2020-MSCA-COFUND2016-754433. This work has been supported by the Spanish Government (SEV2015-0493), by the Spanish Ministry of Science and Innovation (contract TIN2015-65316-P), by Generalitat de Catalunya, Spain (contract 2014-SGR-1051). The research leading to these results has also received funding from the collaboration between Fujitsu and BSC (Script Language Platform). ; Peer Reviewed ; Postprint (published version)

7 pags., 4 figs., 2 tabs. -- Open Access funded by Creative Commons Atribution Licence 4.0 ; The quasi-free scattering reactions C(p,2p) and C(p,pn) have been studied in inverse kinematics at beam energies of 300–400 MeV/u at the RB-LAND setup. The outgoing proton-proton and proton-neutron pairs were detected in coincidence with the reaction fragments in kinematically complete measurements. The efficiency to detect these pairs has been obtained from GEANT4 simulations which were tested using the C(p,2p) and C(p,pn) reactions. Experimental cross sections and momentum distributions have been obtained and compared to DWIA calculations based on eikonal theory. The new results reported here are combined with previously published cross sections for quasi-free scattering from oxygen and nitrogen isotopes and together they enable a systematic study of the reduction of single-particle strength compared to predictions of the shell model over a wide neutron-to-proton asymmetry range. The combined reduction factors show a weak or no dependence on isospin asymmetry, in contrast to the strong dependency reported in nucleon-removal reactions induced by nuclear targets at lower energies. However, the reduction factors for (p,2p) are found to be 'significantly smaller than for (p,pn) reactions for all investigated nuclei. ; This work was supported by the German Federal Ministry of Education and Research (BMBF projects 05P2015RDFN1 and 05P15WOFNA), through the GSI-TU Darmstadt cooperation agreement, by the State of Hesse through the LOEWE center HIC for FAIR, and the Helmholtz-Gemeinschaft through the graduate school HGS-HIRe. This work was supported by the European Union by means of the European Commission within its Seventh Framework Program (FP7) via ENSAR (Contract No. 262010), and the Spanish CICYT research grants FPA2012-32443, FPA2015-64969-07387, and FPA2015-69640-C2-1-P. This work has supported by the Swedish Research Council under contract number 621-2011-5324. C.A.B. acknowledges support from the U.S. NSF grant No. 1415656 and the U.S. DOE grant No. DE-FG02-08ER41533. Supported by the Portuguese FCT under the project PTDC/FIS/ 103902/2008.