ogst
Accueil Tous les numéros Volume 73 (2018) Oil & Gas Science and Technology - Rev. IFP Energies nouvelles, 73 (2018) 63 Références
Numerical methods and HPC
Open Access
Numéro
Oil & Gas Science and Technology - Rev. IFP Energies nouvelles
Volume 73, 2018
Numerical methods and HPC
Numéro d'article 63
Nombre de pages 15
DOI https://doi.org/10.2516/ogst/2018061
Publié en ligne 27 novembre 2018
  • National Institutes of Health. Brain research through advancing innovative neurotechnologies (brain). https://www.braininitiative.nih.gov/about/index.htm. [Google Scholar]
  • Ecole Polytechnique Federale de Lausanne (EPFL)The Blue Brain Project http://bluebrain.epfl.ch/. [Google Scholar]
  • Human Brain Project (HBP). European Commission Future and Emerging Technologies Flagship. https://www.humanbrainproject.eu/. [Google Scholar]
  • Diaz-Pier S., Naveau M., Butz-Ostendorf M., Morrison A. (2016) Automatic generation of connectivity for large-scale neuronal network models through structural plasticity, Front. Neuroanat. 10, 57. ISSN 1662-5129. doi: 10.3389/fnana.2016.00057. [CrossRef] [PubMed] [Google Scholar]
  • Hines M. (1984) Efficient computation of branched nerve equations, Int. J. Bio-med. Comput. 15, 1, 69–76. [CrossRef] [Google Scholar]
  • Conte S.D., De Boor C.W. (1980) Elementary numerical analysis: An algorithmic approach, 3rd edn., McGraw-Hill Higher Education. [Google Scholar]
  • Valero-Lara P., Pinelli A., Prieto-Matias M. (2014) Fast finite difference Poisson solvers on heterogeneous architectures, Comput. Phys. Commun. 185, 4, 1265–1272. [Google Scholar]
  • Valero-Lara P., Pinelli A., Favier J., Prieto-Matias M. (2012) Block tridiagonal solvers on heterogeneous architectures, in: 10th IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA, Leganes, Madrid, Spain, July, pp. 609–616. [Google Scholar]
  • Davidson A.A., Zhang Y., Owens J.D. (2011) An auto-tuned method for solving large tridiagonal systems on the GPU, in: 25th IEEE International Symposium on Parallel and Distributed Processing, IPDPS, Anchorage, Alaska, USA, May, pp. 956–965. [Google Scholar]
  • Zhang Y., Cohen J., Owens J.D. (2010) Fast tridiagonal solvers on the GPU, in: Proceedings of the 15th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPOPP, Bangalore, India, January, pp. 127–136. [Google Scholar]
  • NVIDIA. Nvidia-cuda toolkit documentation. http://docs.nvidia.com/cuda/cusparse/. [Google Scholar]
  • Ben-Shalom R., Liberman G., Korngreen A. (2013) Accelerating compartmental modeling on a graphical processing unit, Front. Neuroanat. 7, 4. [PubMed] [Google Scholar]
  • Stone H.S. (1973) An efficient parallel algorithm for the solution of a tridiagonal linear system of equations, J. ACM 20, 1, 27–38. [CrossRef] [Google Scholar]
  • Valero-Lara P., Pelayo F.L. (2011) Towards a more efficient use of GPUs, in: International Conference on Computational Science and Its Applications, ICCSA 2011, Santander, Spain, June 20–23, pp. 3–9. [Google Scholar]
  • Valero-Lara P., Pelayo F.L. (2013) Analysis in performance and new model for multiple kernels executions on many-core architectures, in: IEEE 12th International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2013, New York, NY, USA, July 16–18, pp. 189–194. [Google Scholar]
  • Valero-Lara P. (2014) Multi-gpu acceleration of DARTEL (early detection of alzheimer), in: 2014 IEEE International Conference on Cluster Computing, CLUSTER 2014, Madrid, Spain, September 22–26, pp. 346–354. [Google Scholar]
  • Valero-Lara P., Nookala P., Pelayo F.L., Jansson J., Dimitropoulos S., Raicu l. (2016) Many-task computing on many-core architectures, Scalable Computing: Practice and Experience 17, 1, 32–46. [CrossRef] [Google Scholar]
  • Valero-Lara P., Martínez-Perez I., Peña A.J., Martorell X., Sirvent R., Labarta J. (2017) CuHines-batch: Solving multiple Hines systems on GPUs Human Brain Project*, in: International Conference on Computational Science, ICCS 2017, Zurich, Switzerland, June 12–14, pp. 566–575. [Google Scholar]
  • Cumming B. (2010) Coreneuron overview. CSCS – Swiss National Supercomputing Center. [Google Scholar]
  • Zhang Y., Cohen J., Owens J.D. (2010) Fast tridiagonal solvers on the GPU, SIGPLAN Not. 45, 5, 127–136. [CrossRef] [Google Scholar]
  • Kim H.-S., Wu S.Z., Chang L.W., Hwu W.W. (2011) A scalable tridiagonal solver for GPUs, in: 2013 42nd International Conference on Parallel Processing, pp. 444–453. [Google Scholar]
  • Sakharnykh N. (2010) Efficient tridiagonal solvers for adi methods and fluid simulation in: NVIDIA GPU Technology Conference, September. [Google Scholar]
  • Davidson A., Zhang Y., Owens J.D. (2011) An autotuned method for solving large tridiagonal systems on the GPU in: IEEE International Parallel and Distributed Processing Symposium, May. [Google Scholar]
  • Valero-Lara P., Martínez-Pérez I., Sirvent R., Martorell X., Peña A.J. (2019) cuThomasBatch and cuThomasVBatch, CUDA Routines to compute batch of tridiagonal systems on NVIDIA GPUs, Concurrency and Computation: Practice and Experience. [Google Scholar]
  • Valero-Lara P., Martínez-Perez I., Sirvent R., Martorell X., Peña A.J. (2017) NVIDIA GPUs scalability to solve multiple (batch) tridiagonal systems implementation of cuThomasBatch in: Parallel Processing and Applied Mathematics - 12th International Conference, PPAM2017, Lublin, Poland, Revised Selected Papers, Part I, September 10–13, pp. 243–253. [Google Scholar]
  • Vooturi D.T., Kothapalli K., Bhalla U.S. (2017) Parallelizing Hines matrix solver in neuron simulations on GPU, in: 24th IEEE International Conference on High Performance Computing, HiPC 2017, Jaipur, India, December 18–21, pp. 388–397. [Google Scholar]
  • Dongarra J.J., Hammarling S., Higham N.J., Relton S.D., Valero-Lara P., Zounon M. (2017) The design and performance of batched BLAS on modern high-performance computing systems, in: International Conference on Computational Science, ICCS 2017, Zurich, Switzerland, June 12–14, pp. 495–504. [Google Scholar]
  • Intel. Intel(r) math kernel library - introducing vectorized compact routines, https://software.intel.com/en-us/articles/intelr-math-kernel-library-introducing-vectorized-compact-routines [Google Scholar]
  • Kim K.J., Costa T.B., Deveci M., Bradley A.M., Hammond S.D., Guney M.E., Knepper S., Story S., Rajamanickam S. (2017) Designing vector-friendly compact BLAS and LAPACK kernels, in: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2017, Denver, CO, USA, November 12–17, pp. 55:1–55:12. [Google Scholar]
  • Chang L.-W., Stratton J.A., Kim H.-S., Hwu W.-W. (2012) A scalable, numerically stable, high-performance tridiagonal solver using GPUs, in: SC Conference on High Performance Computing Networking, Storage and Analysis, SC ‘12, Salt Lake City, UT, USA, November 11–15, p. 27. [Google Scholar]
  • Zhang Y., Cohen J., Owens J.D. (2010) Fast tridiagonal solvers on the GPU, in: Proceedings of the 15th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPOPP 2010, Bangalore, India, January 9–14, pp. 127–136. [Google Scholar]
  • László E., Giles M.B., Appleyard J. (2016) Many-core algorithms for batch scalar and block tridiagonal solvers, ACMTrans. Math. Softw. 42, 4, 311–3136. [Google Scholar]

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.