- Stephen Cass (2017) The 2017 Top Programming Languages (IEEE Spectrum), https://spectrum.ieee.org/computing/software/the-2017-top-programming-languages, accessed: 2018-02-14. [Google Scholar]
- Van Rossum G., Drake F.L. (2003) Python language reference manual. Network Theory. [Google Scholar]
- van der Walt S., Colbert S.C., Varoquaux G. (2011) The NumPy Array: A structure for efficient numerical computation, Comput. Sci. Eng. 13, 2, 22–30, http://dx.doi.org/10.1109/MCSE.2011.37. [CrossRef] [Google Scholar]
- Jones E, Oliphant E., Peterson P. (2001) SciPy: Open source scientific tools for Python, http://www.scipy.org/ [Google Scholar]
- BLAS (Basic Linear Algebra Subprograms) Web page at http://www.netlib.org/blas/, accessed: 28th August, 2017. [Google Scholar]
- Anderson E., Bai Z.J., Bischof C., Susan Blackford L., Demmel J., Dongarra J., Du Croz J., Greenbaum A., Hammarling S., McKenney A., et al. (1999) LAPACK Users’ guide, SIAM. [Google Scholar]
- Dagum L., Menon R. (1998) OpenMP: An Industry-Standard API for Shared-Memory Programming, IEEE Comput. Sci. Eng. 5, 1, 46–55, https://doi.org/10.1109/99.660313. [Google Scholar]
- Threading Building Blocks (Intel®TBB) (2017) Web page at https://www.threadingbuildingblocks.org/, accessed: 28th August, 2017. [Google Scholar]
- Parallel Processing and Multiprocessing in Python (2017) Web page at https://wiki.python.org/moin/ParallelProcessing, accessed: 10th October, 2017. [Google Scholar]
- Parallel Python Software (2017) Web page at http://www.parallelpython.com, accessed: 10th October, 2017. [Google Scholar]
- Dalcín L., Paz R., Storti M. (2005) MPI for Python, J. Parallel. Distr. Com., http://www.sciencedirect.com/science/article/pii/S0743731505000560. [Google Scholar]
- Tejedor E., Becerra Y., Alomar G., Queralt A., Badia R.M., Torres J., Cortes T., Labarta J. (2017) PyCOMPSs: Parallel computational workflows in Python, Int. J. High Perform. Comput. Appl. 31, 1, 66–82. [CrossRef] [Google Scholar]
- Ramon-Cortes C., Serven A., Ejarque J., Lezzi D., Badia R.M. (2018) Transparent orchestration of task based parallel applications in containers platforms, J. Grid Computing 16, 1, 137–160. [CrossRef] [Google Scholar]
- Dask Development Team (2016) Dask: Library for dynamic task scheduling, http://dask.pydata.org [Google Scholar]
- PySpark (The Spark Python API (2017) Web page at https://spark.apache.org/docs/latest/api/python/index.html, accessed: 6th October, 2017. [Google Scholar]
- McKinney W. (2011) pandas: a foundational python library for data analysis and statistics, Python for High Performance and Scientific Computing 1–9. [Google Scholar]
- Zaharia M., Chowdhury M., Franklin M.J., Shenker S., Stoica I. (2010) Spark: Cluster Computing with Working Sets, in: Proceedings of the 2Nd USENIX Conference on Hot Topics in Cloud Computing, Berkeley, CA, USA. [Google Scholar]
- Conejero J., Corella S., Badia Rosa M., Labarta J. (2017) Taskbased programming in COMPSs to converge from HPC to big data, Int. J. High Perform. Comput. Appl. 4, https://doi.org/10.1177/1094342017701278. [Google Scholar]
- Extrae Web page at https://tools.bsc.es/extrae, accessed: 19th December, 2016. [Google Scholar]
- Pillet V., et al. (1995) Paraver: A tool to visualize and analyze parallel code, Transputer and Occam Developments 4, 17–32, http://www.bsc.es/paraver. [Google Scholar]
- Paraver: a flexible performance analysis tool. Web page at https://tools.bsc.es/paraver, accessed: 19th December, 2016. [Google Scholar]
- Lordan F., Tejedor E., Ejarque J., Rafanell R., Álvarez J., Marozzo F., Lezzi D., Sirvent R., Talia D., Badia R.M. (2014) ServiceSs: An Interoperable Programming Framework for the Cloud, J. Grid Computing 12, 1, 67–91, https://doi.org/10.1007/s10723-013-9272-5. [CrossRef] [Google Scholar]
- Liang S. (1999) Java Native Interface: Programmer’s Guide and Reference, 1st edn, Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, ISBN 0201325772. [Google Scholar]
- Intel Corporation (2015) Intel Math Kernel Library. Reference Manual, Intel Corporation, Santa Clara, USA, ISBN 630813-054US. [Google Scholar]
- Che S., Boyer M., Boyer M., Meng J., Tarjan D., Sheaffer J.W., Lee S.-H., Skadron K. (2009) Rodinia: A benchmark suite for heterogeneous computing, in:Proceedings of the 2009 IEEE International Symposium on Workload Characterization (IISWC), IISWC ’09 IEEE Computer Society, pp. 44–54, http://dx.doi.org/10.1109/IISWC.2009.5306797. [CrossRef] [Google Scholar]
- Djemame K., Armstrong D., Kavanagh R.E., Deprez J.-C., Ferrer A.J., García-Pérez D., Badia R.M., Sirvent R., Ejarque J., Georgiou Y. (2016) TANGO: Transparent heterogeneous hardware Architecture deployment for eNergy Gain in Operation, http://arxiv.org/abs/1603.01407. [Google Scholar]
- Chan E., Van Zee F.G., Bientinesi P., Quintana-Orti E.S., Quintana-Orti G., van de Geijn R. (2008) SuperMatrix: a multithreaded runtime scheduling system for algorithms-by-blocks, in: Proceedings of the 13th ACM SIGPLAN Symposium on Principles and practice of parallel programming, PPoPP ’08, ACM, pp. 123–132, http://doi.acm.org/10.1145/1345206.1345227. [Google Scholar]
- Agullo E., Demmel J., Dongarra J., Hadri B., Kurzak J., Langou J., Ltaief H., Luszczek P., Tomov S. (2009) Numerical linear algebra on emerging architectures: The PLASMA and MAGMA projects, J. Phys. Conf. Ser. 180, 1, 012–037, http://stacks.iop.org/1742-6596/180/i=1/a=012037. [CrossRef] [Google Scholar]
- Blackford L.S., Choi J., Cleary A., D’Azevedo E., Demmel J., Dhillon I., Dongarra J., Hammarling S., Henry G., Petitet A., Stanley K., Walker D., Whaley R.C. (1997) ScaLAPACK Users’ Guide, Society for Industrial and, Applied Mathematics. [Google Scholar]
- Gunnels J.A., Gustavson F.G., Henry G.M., van de Geijn R.A. (2001) FLAME: Formal Linear Algebra Methods Environment, ACM Trans. Math. Softw. 27, 4, 422–455, http://doi.acm.org/10.1145/504210.504213. [CrossRef] [Google Scholar]
- Klöckner A., Pinto N., Lee Y., Catanzaro B., Ivanov P., Fasih A. (2012) PyCUDA and PyOpenCL: A scripting-based approach to GPU run-time code generation, Parallel Computing 38, 3, 157–174, ISSN 0167-8191. https://doi.org/10.1016/j.parco.2011.09.001. [CrossRef] [Google Scholar]
- Givon L.E., Unterthiner T., Benjamin Erichson N., Wei Chiang D., Larson E., Pfister L., Dieleman S., Lee G.R., van der Walt S., Menn B., Mihai Moldovan T., Bastien F., Shi X., Schlüter J., Thomas B., Capdevila C., Rubinsteyn A., Forbes M.M., Frelinger J., Klein T., Merry B., Merill N., Pastewka L., Clarkson S., Rader M., Taylor S., Bergeron A., Ukani N.H., Wang F., Zhou Y. (2015) scikit-cuda 0.5.1: a Python interface to GPU-powered libraries, http://dx.doi.org/10.5281/zenodo.40565. [Google Scholar]
- Bientinesi P., Gunter B., van de Geijn R.A. (2008) Families of Algorithms Related to the Inversion of a Symmetric Positive Definite Matrix, ACM Trans. Math. Softw. 35, 1, 1–3, http://doi.acm.org/10.1145/1377603.1377606. [CrossRef] [Google Scholar]
- Ltaief H., Tomov S., Nath R., Du P., Dongarra J. (2011) A scalable high performant Cholesky factorization for multicore with GPU accelerators, in: Proceedings of the 9th International Conference on High Performance Computing for Computational Science, VECPAR’10, Springer-Verlag, pp. 93–101, http://dl.acm.org/citation.cfm?id=1964238.1964251 [Google Scholar]
- Quintana-Orti G., Quintana-Orti E.S., Chan E., van de Geijn R.A., Van Zee F.G. (2008) Scheduling of QR Factorization Algorithms on SMP and Multi-Core Architectures, in: Proceedings of the 16th Euromicro Conference on Parallel, Distributed and Network-Based Processing (PDP 2008), PDP ’08, IEEE Computer Society, pp. 301–310, http://dx.doi.org/10.1109/PDP.2008.37. [CrossRef] [Google Scholar]
- Golub Gene H., Van Loan C.F. (1996) Matrix Computations, 3rd edn., Johns Hopkins University Press, Baltimore, MD, USA, ISBN 0-8018-5414-8. [Google Scholar]
- Quintana-Ortí E.S., van de Geijn R.A. (2008) Updating an LU factorization with pivoting, ACM Trans. Math. Softw. 35, 2, 1–11, http://doi.acm.org/10.1145/1377612.1377615. [CrossRef] [Google Scholar]
- Demmel James W., Higham Nicholas J. (1992) Stability of Block Algorithms with Fast Level-3 BLAS, ACM Trans. Math. Softw. 18, 3, 274–291, http://doi.acm.org/10.1145/131766.131769. [CrossRef] [Google Scholar]
- MareNostrum III User’s Guide Web page at https://www.bsc.es/support/MareNostrum3-ug.pdf, accessed: 21st August, 2017. [Google Scholar]
- Architecting a High Performance Storage System Web page at https://www.intel.com/content/dam/www/public/us/en/documents/white-papers/architecting-lustre-storage-white-paper.pdf, accessed: 21st August, 2017. [Google Scholar]
- Intel ®Xeon ®Processor E5-2600 Series Web page at http://download.intel.com/support/processors/xeon/sb/xeon_E5-2600.pdf, accessed: 21st August, 2017. [Google Scholar]
Open Access
| Numéro |
Oil & Gas Science and Technology - Rev. IFP Energies nouvelles
Volume 73, 2018
Numerical methods and HPC
|
|
|---|---|---|
| Numéro d'article | 47 | |
| Nombre de pages | 17 | |
| DOI | https://doi.org/10.2516/ogst/2018047 | |
| Publié en ligne | 24 octobre 2018 | |
Les statistiques affichées correspondent au cumul d'une part des vues des résumés de l'article et d'autre part des vues et téléchargements de l'article plein-texte (PDF, Full-HTML, ePub... selon les formats disponibles) sur la platefome Vision4Press.
Les statistiques sont disponibles avec un délai de 48 à 96 heures et sont mises à jour quotidiennement en semaine.
Le chargement des statistiques peut être long.