Dossier: Geosciences Numerical Methods
Open Access
Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles
Volume 69, Number 4, July-August 2014
Dossier: Geosciences Numerical Methods
Page(s) 753 - 766
Published online 29 January 2014
  • Saad Y. (2000) Iterative Methods for Sparse Linear Systems, second edition, SIAM, pp. 144-227. [Google Scholar]
  • Buck I., Foley T., Horn D., Sugerman J., Fatahalian K., Houston M., Hanrahan P. (2004) ACM Transactions on graphics 23, 777–786. [CrossRef] [Google Scholar]
  • NVIDIA Corp. [Google Scholar]
  • [Google Scholar]
  • Patterson D.A., Hennessy J.L. (2011) The Hardware/Software Interface, in Computer Organization and Design, E.D. Morgan Kaufmann Publishers Inc., Elsevier, fourth edition, Chap. 7 and Appendix A. [Google Scholar]
  • Culler D.E., Pal Singh J., Gupta A. (1997) A Hardware/Software Approach, in Parallel Computer Architecture, E.D. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, pp. 44–47. [Google Scholar]
  • Lacroix S., Vassilevski Yu, Wheeler J., Wheeler M.F. (2003) Iterative solution methods for modeling multiphase flow in porous media fully implicitly, SIAM Journal 25, 3, 905–926. [Google Scholar]
  • Chow E., Cleary A.J., Falgout R.D. (1998) Design of the hypre Preconditioner Library, in proceding of the SIAM Workshop on Object Oriented Methods for Inter-operable Scientific and Engineering Computing. [Google Scholar]
  • Balay S., Brown J., Buschelman K., Gropp W.D., Kaushik D., Knepley M.G., McInnes L.C., Smith B.F., Zhang H. (2012) PETSc Web page, [Google Scholar]
  • Gottschling P. et al. (2011) PMTL4 web page, [Google Scholar]
  • Gratien J.-M., Guignon T., Hacene M. (2011) Solveurs linéaires sur GPU pour la simulation d’écoulement en milieux poreux, in SMAI Mini symposia, Nouvelles tendances en méthodes numériques et calcul scientifique pour la simulation des écoulements polyphasiques, Guiel, France, 23-27 May, [Google Scholar]
  • Gratien J.-M., Guignon T., Magras J.-F., Quandalle P., Ricois O. (2006) How to Improve the Scalability of an Industrial Parallel Reservoir Simulator, Parallel and Distributed Computing and Systems, PDCS, 13-15 Nov., Dalles, Tx, 513-098 [Google Scholar]
  • Karp R.M. (1972) Reducibility Among Combinatorial Problems, in Complexity of Computer Computations, R.E. Miller, J.W. Thatcher (eds), Plenum, New York, pp. 85–103. [Google Scholar]
  • Karypis G., Kumar V. (1995) METIS - Unstructured Graph Partitioning and Sparse Matrix Ordering System, Version 2.0, Technical Report. [Google Scholar]
  • Gratien J.-M., Guignon T., Magras J.-F., Quandalle P., Ricois O. (2007) Scalability and load balancing problems in parallel reservoir simulation, in Proceeding of SPE-Reservoir Simulation Symposium, Houston, 26-28 Fev. [Google Scholar]
  • Gratien J.-M., Magras J.-F., Quandalle P., Ricois O. (2004) Introducing a new generation of reservoir simulation software, in Proceedings of ECMOR, European Conference on the Mathematics of Oil Recovery, Cannes, 30 Aug.-2 Sept. [Google Scholar]
  • Trottenberg U., Oosterlee C., Schüller A. (2001) Multigrid Methods, Academic Press. [Google Scholar]
  • Briggs W.L., Henson V.E., McCormick S.F. (2000) A Multigrid Tutorial, second edition, SIAM, pp. 137–159. [CrossRef] [Google Scholar]
  • Brezina M., Cleary A.J., Falgout R.D., Henson V.E., Jones J.E., Manteuffel T.A., McCormick S.F., Ruge J.W. (2000) Algebraic Multigrid Based on Element Interpolation (AMGe), SIAM Journal on Scientific Computing 22, 5, 1570–1592. [CrossRef] [MathSciNet] [Google Scholar]
  • De Sterck H., Yang U.M., Heys J. (2006) Reducing complexity parallel in algebraic multigrid preconditioners, in Proceeding of SIMAX 27, 1019–1039. [CrossRef] [Google Scholar]
  • Bolz J., Farmer I., Grinspun E., Schröder P. (2003) Sparse matrix solvers on the GPU: conjugate gradients and multigrid, ACM Trans. Graph. 22, 3, 917–924. [CrossRef] [Google Scholar]
  • Buatois L., Caumon G., Lévy B. (2007) Concurrent Number Cruncher: An Efficient Sparse Linear Solver on the GPU, in High Performance Computing and Communications, Houstan, 26-28 Sept, Lecture Notes in Computer Science 4782, 358–371. [Google Scholar]
  • Bell N., Garland M. (2008) Efficient Sparse Matrix-Vector Multiplication on CUDA, NVIDIA Technical Report. [Google Scholar]
  • Emans M., Liebmann M., Basara B. (2012) Steps towards GPU Accelerated Aggregation AMG, 11th International Symposium on Parallel and Distribuled Computing – ISPDC 2012, Munich, 25-29 June, pp. 79-86. [Google Scholar]
  • Minden V., Smith B.F., Knepley M.G. (2010) Preliminary implementation of PETSc using GPUs, Proceedings of the International Workshop of GPU Solutions to Multiscale Problems in Science and Engineering, GMP-SMP 2010, Harbin, 26-28 July. [Google Scholar]
  • NVAMG Reference Manual (2013). [Google Scholar]
  • Heroux M.A., Bartlett R.A., Howle V.E., Hoekstra R.J., Hu J.J., Kolda T.G., Lehoucq R.B., Long K.R., Pawlowski R.P., Phipps E.T., Salinger A.G., Thornquist H.K., Tuminaro R.S., Willenbring J.M., Williams A., Stanley K.S. (2005) An overview of the Trilinos project, ACM Trans. Math. Softw. 31, 397–423. [CrossRef] [Google Scholar]
  • Ricois O. (2011) Vision Général du simulateur ARCEOR, IFPEN Technical Report number 62096. [Google Scholar]
  • Grospellier G., Lelandais B. (2009) The Arcane development framework, Proceedings of the 8th workshop on Parallel/High-Performance Object-Oriented Scientific Computing, POOSC ’09, 978-1-60558-547-5, Genova, Italy, pp. 4:1-4:11. [Google Scholar]
  • NVIDIA’s Next Generation CUDATM Compute Architecture: Kepler TM GK110, The Fastest, Most Efficient HPC Architecture Ever Built, White paper [Google Scholar]
  • Christie M.A., Blunt M.J. (2001) Tenth SPE Comparative Solution Project: A Comparison of Upscaling Techniques, in SPE Reservoir Simulation Symposium, Houston, Tx, 11-14 Fev. [Google Scholar]
  • Willien F., Chetvchenko I., Masson R., Quandalle P., Agelas L., Requena S. (2009) AMG preconditioning for sedimentary basin simulations in Temis calculator, Marine and Petroleum Geology Journal 26, 4, 519–524. [CrossRef] [Google Scholar]
  • Bohbot J., Gillet N., Benkenida A. (2009) IFP-C3D: An unstructured parallel solver for reactive compressible gas flow with spray, Oil Gas Sci. Technol. 64, 309–336. [CrossRef] [EDP Sciences] [Google Scholar]
  • Stüben K. (2001) Algebraic Multigrid: An Introduction for Positive Definite Problems with Applications, Academic Press, pp. 413–532. [Google Scholar]
  • Gottschling P., Hoefler T. (2012) Productive Parallel Linear Algebra Programming with Unstructured Topology Adaption, International Symposium on Cluster, Cloud and Grid Computing 2012, Ottawa, Canada, May, ACM/IEEE. [Google Scholar]
  • Gottschling P., Steinhardt C. (2010) Meta-Tuning in MTL4, ICNAAM 2010: International Conference of Numerical Analysis and Applied Mathematics, 1281, 778–782, Sept., 10.1061.3498599, Proc. ICNAAM ‘10 – Symposium Automated Computing. [Google Scholar]
  • Hysom D., Pothen A. (2001) A scalable parallel algorithm for incomplete factor preconditioning, SIAM Journal on Scientific Computing 22, 6, 2194–2215. [CrossRef] [Google Scholar]
  • Barnard S.T., Bernardo L.M., Simon H.D. (1999) An MPI implementation of the SPAI preconditioner on the t3E, Int. J. High Perf. Comput. Appl. J. 13, 107–128. [CrossRef] [Google Scholar]

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