1. B. Lathuilière. Domain decomposition method for the Simplified Transport Equation in neutronic. PhD thesis, Université Sciences et Technologies - Bordeaux I, February 2010. Keyword(s): Neutron. Abstract:

   The reactivity computations are an essential component for the simulation of the core of a nuclear plant. These computations lead to generalized eigenvalue problems solved by the inverse power iteration algorithm. At each iteration, an algebraic linear system is solved through an inner/outer process. With the solver Cocagne developed at EDF, it is difficult to take into account very fine discretisation, due to the memory requirement and the computation time. In this thesis, a domain decomposition method based on the Schur dual technique is studied. Several placements in the inner/outer process are possible. Two of them are implemented and the results analyzed. The second one, which uses the specificities of the Raviart Thomas finite elements and of the alternating directions algorithm, leads to very promising results. From these results the industrialization of the method can be considered.

   
   

   @phdthesis{t:LaBRI::BL10,
   TITLE = {{Domain decomposition method for the Simplified Transport Equation in neutronic}},
   AUTHOR = {Lathuili{\`e}re, B.},
   URL = {https://tel.archives-ouvertes.fr/tel-00468154},
   SCHOOL = {{Universit{\'e} Sciences et Technologies - Bordeaux I}},
   YEAR = {2010},
   MONTH = Feb,
   KEYWORDS = {Neutron},
   PDF = {https://tel.archives-ouvertes.fr/tel-00468154/file/these-LATHUILIERE.pdf},
   HAL_ID = {tel-00468154},
   HAL_VERSION = {v1},
   ABSTRACT = {The reactivity computations are an essential component for the simulation of the core of a nuclear plant. These computations lead to generalized eigenvalue problems solved by the inverse power iteration algorithm. At each iteration, an algebraic linear system is solved through an inner/outer process. With the solver Cocagne developed at EDF, it is difficult to take into account very fine discretisation, due to the memory requirement and the computation time. In this thesis, a domain decomposition method based on the Schur dual technique is studied. Several placements in the inner/outer process are possible. Two of them are implemented and the results analyzed. The second one, which uses the specificities of the Raviart Thomas finite elements and of the alternating directions algorithm, leads to very promising results. From these results the industrialization of the method can be considered.} 
   }
   

1. Y. Suzuki, N. Kushida, T. Tatekawa, N. Teshima, Y. Caniou, R. Guivarch, M. Dayde, and P. Ramet. Development of an International Matrix-Solver Prediction System on a French-Japanese International Grid Computing Environment. In Joint International Conference on Supercomputing in Nuclear Applications and Monte Carlo 2010 (SNA + MC2010), Tokyo, Japan, October 2010.
   

   @inproceedings{C:LaBRI::sna-mc-2010,
   Address = {Tokyo, Japan},
   Author = {Suzuki, Y. and Kushida, N. and Tatekawa, T. and Teshima, N. and Caniou, Y. and Guivarch, R. and Dayde, M. and Ramet, P.},
   Booktitle = {Joint International Conference on Supercomputing in Nuclear Applications and Monte Carlo 2010 (SNA + MC2010)},
   Month = {October},
   Title = {{Development of an International Matrix-Solver Prediction System on a French-Japanese International Grid Computing Environment}},
   Year = {2010} 
   }
   

1. M. Faverge, X. Lacoste, and P. Ramet. A NUMA Aware Scheduler for a Parallel Sparse Direct Solver. Technical report, 2010. Keyword(s): Sparse. Abstract:

   {O}ver the past few years, parallel sparse direct solvers made significant progress and are now able to solve efficiently industrial three-dimensional problems with several millions of unknowns. {T}o solve efficiently these problems, {P}a{S}ti{X} and {WSMP} solvers for example, provide an hybrid {MPI}-thread implementation well suited for {SMP} nodes or multi-core architectures. {I}t enables to drastically reduce the memory overhead of the factorization and improve the scalability of the algorithms. {H}owever, today's modern architectures introduce new hierarchical memory accesses that are not handle in these solvers. {W}e present in this paper three improvements on {P}a{S}ti{X} solver to improve the performance on modern architectures : memory allocation, communication overlap and dynamic scheduling and some results on numerical test cases will be presented to prove the efficiency of the approach on {NUMA} architectures.

   
   

   @techreport{FAVERGE:2010:INRIA-00549827:1,
   HAL_ID = {inria-00549827},
   URL = {http://hal.archives-ouvertes.fr/inria-00549827/en/},
   title = { {A} {NUMA} {A}ware {S}cheduler for a {P}arallel {S}parse {D}irect {S}olver},
   author = {{F}averge, {M}. and {L}acoste, {X}. and {R}amet, {P}.},
   abstract = {{O}ver the past few years, parallel sparse direct solvers made significant progress and are now able to solve efficiently industrial three-dimensional problems with several millions of unknowns. {T}o solve efficiently these problems, {P}a{S}ti{X} and {WSMP} solvers for example, provide an hybrid {MPI}-thread implementation well suited for {SMP} nodes or multi-core architectures. {I}t enables to drastically reduce the memory overhead of the factorization and improve the scalability of the algorithms. {H}owever, today's modern architectures introduce new hierarchical memory accesses that are not handle in these solvers. {W}e present in this paper three improvements on {P}a{S}ti{X} solver to improve the performance on modern architectures : memory allocation, communication overlap and dynamic scheduling and some results on numerical test cases will be presented to prove the efficiency of the approach on {NUMA} architectures.},
   year = 2010,
   keywords = {Sparse},
   language = {{A}nglais},
   affiliation = {{I}nnovative {C}omputing {L}aboratory - {ICL} - {U}niversity of {T}ennessee - {BACCHUS} - {INRIA} {B}ordeaux - {S}ud-{O}uest - {INRIA} - {U}niversit{\'e} de {B}ordeaux - {CNRS} : {UMR}5251 - {CNRS} : {UMR}5800 - {L}aboratoire {B}ordelais de {R}echerche en {I}nformatique - {L}a{BRI} - {CNRS} : {UMR}5800 - {U}niversit{\'e} {S}ciences et {T}echnologies - {B}ordeaux {I} - {E}cole {N}ationale {S}up{\'e}rieure d'{E}lectronique, {I}nformatique et {R}adiocommunications de {B}ordeaux - {U}niversit{\'e} {V}ictor {S}egalen - {B}ordeaux {II} },
   URL = {http://hal.archives-ouvertes.fr/inria-00549827/PDF/RR-7498.pdf},
   
   }
   

1. P. Hénon and P. Ramet. Scalable direct and iterative solvers, June 2010. Note: Workshop INRIA-UUIC, Bordeaux, France. Keyword(s): Sparse.
   

   @Misc{C:LaBRI::UUIC,
   author = "H\'enon, P. and Ramet, P.",
   title = "Scalable direct and iterative solvers",
   note = "Workshop INRIA-UUIC, Bordeaux, France",
   OPTcrossref = {},
   OPTkey = {},
   OPTeditor = {},
   OPTvolume = {},
   OPTnumber = {},
   OPTseries = {},
   year = "2010",
   OPTorganization = {},
   OPTpublisher = {},
   address = {Bordeaux, France},
   month = jun,
   OPTpages = {},
   OPTnote = {},
   OPTannote = {},
   OPTURL = {},
   KEYWORDS = "Sparse",
   OPTABSTRACT = {} 
   }
   




2. P. Hénon and P. Ramet. Scalable direct and iterative solvers. SuperComputing'2010, New Orleans, USA, November 2010. Keyword(s): Sparse.
   

   @Misc{c:LaBRI::SC2010,
   author = "H\'enon, P. and Ramet, P.",
   title = "Scalable direct and iterative solvers",
   booktitle = "{SuperComputing}'2010",
   OPTcrossref = {},
   OPTkey = {},
   OPTeditor = {},
   OPTvolume = {},
   OPTnumber = {},
   OPTseries = {},
   year = "2010",
   OPTorganization = {},
   OPTpublisher = {},
   howpublished = {{SuperComputing}'2010, New Orleans, USA},
   month = nov,
   OPTpages = {},
   OPTnote = {},
   OPTannote = {},
   OPTURL = {http://www.labri.fr/~ramet/restricted/poster_sc2010.tar},
   KEYWORDS = "Sparse" 
   }
   




3. P. Ramet. Formation Parallélisme. CEMRACS'2010, Modèles numériques pour la fusion, Marseille, France, August 2010.
   

   @Misc{c:LaBRI::CEMRACS10,
   author = {Ramet, P.},
   title = {Formation Parall\'elisme},
   month = aug,
   year = {2010},
   howpublished = {CEMRACS'2010, Mod\`eles num\'eriques pour la fusion, Marseille, France} 
   }
   




4. P. Ramet. Ordonnancement dynamique dans le solveur PaStiX pour des machines NUMA et multicoeurs. Formation CNRS, Solveurs de Systèmes Linéaires de grande taille : les avancées récentes, Lyon, France, November 2010.
   

   @Misc{c:LaBRI::CNRS10,
   author = {Ramet, P.},
   title = {Ordonnancement dynamique dans le solveur PaStiX pour des machines NUMA et multicoeurs},
   month = nov,
   year = {2010},
   howpublished = {Formation CNRS, Solveurs de Syst\`emes Lin\'eaires de grande taille : les avanc\'ees r\'ecentes, Lyon, France} 
   }
   

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