K. Akbudak, H. Ltaief, A. Mikhalev, and D. Keyes, Tile low rank cholesky factorization for climate/weather modeling applications on manycore architectures, Proceedings of ISC'17, 2017.

P. R. Amestoy, C. Ashcraft, O. Boiteau, A. Buttari, J. Excellent et al., Improving Multifrontal Methods by Means of Block Low-Rank Representations, SIAM Journal on Scientific Computing, vol.37, issue.3, pp.1451-1474, 2015.
DOI : 10.1137/120903476

URL : https://hal.archives-ouvertes.fr/hal-00776859

P. R. Amestoy, R. Brossier, A. Buttari, J. Excellent, T. Mary et al., Fast 3D frequency-domain full-waveform inversion with a parallel block low-rank multifrontal direct solver: Application to OBC data from the North Sea, GEOPHYSICS, vol.81, issue.6, pp.81-363, 2016.
DOI : 10.1190/geo2016-0052.1

URL : https://hal.archives-ouvertes.fr/hal-01349119

P. R. Amestoy, A. Buttari, I. S. Duff, A. Guermouche, J. Excellent et al., The multifrontal method, Encyclopedia of Parallel Computing, pp.1209-1216, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00787015

P. R. Amestoy, A. Buttari, J. Excellent, and T. Mary, Complexity and performance of the Block Low-Rank multifrontal factorization, SIAM Conference on Parallel Processing (SIAM PP16), 2016.

P. R. Amestoy, A. Buttari, J. Excellent, and T. Mary, On the complexity of the Block Low-Rank multifrontal factorization, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01322230

A. Aminfar, S. Ambikasaran, and E. Darve, A fast block low-rank dense solver with applications to finite-element matrices. CoRR, abs/1403, 2014.
DOI : 10.1016/j.jcp.2015.10.012

URL : http://arxiv.org/abs/1403.5337

A. Aminfar and E. Darve, A fast sparse solver for finite-element matrices. CoRR, abs, 1410.

J. Anton, C. Ashcraft, and C. Weisbecker, A Block Low-Rank multithreaded factorization for dense BEM operators, SIAM Conference on Parallel Processing (SIAM PP16), 2016.

M. Bebendorf, Approximation of boundary element matrices, Numerische Mathematik, vol.86, issue.4, pp.565-589, 2000.
DOI : 10.1007/PL00005410

M. Bebendorf, Hierarchical Matrices: A Means to Efficiently Solve Elliptic Boundary Value Problems, Lecture Notes in Computational Science and Engineering, vol.63, 2008.

S. Börm, L. Grasedyck, and W. Hackbusch, Introduction to hierarchical matrices with applications. Engineering analysis with boundary elements, pp.405-422, 2003.

S. Chandrasekaran, M. Gu, and T. Pals, A Fast $ULV$ Decomposition Solver for Hierarchically Semiseparable Representations, SIAM Journal on Matrix Analysis and Applications, vol.28, issue.3, pp.603-622, 2006.
DOI : 10.1137/S0895479803436652

S. Constable, Ten years of marine CSEM for hydrocarbon exploration, GEOPHYSICS, vol.75, issue.5, pp.75-67, 2010.
DOI : 10.1190/1.3483451

T. A. Davis and Y. Hu, The university of Florida sparse matrix collection, ACM Transactions on Mathematical Software, vol.38, issue.1, pp.1-125, 2011.
DOI : 10.1145/2049662.2049663

J. J. Dongarra, I. S. Duff, D. C. Sorensen, H. A. Van, and . Vorst, Numerical Linear Algebra for High-Performance Computers, 1998.
DOI : 10.1137/1.9780898719611

I. S. Duff, A. M. Erisman, and J. K. Reid, Direct Methods for Sparse Matrices, 1986.
DOI : 10.1093/acprof:oso/9780198508380.001.0001

I. S. Duff and S. Pralet, Towards Stable Mixed Pivoting Strategies for the Sequential and Parallel Solution of Sparse Symmetric Indefinite Systems, SIAM Journal on Matrix Analysis and Applications, vol.29, issue.3, pp.1007-1024, 2007.
DOI : 10.1137/050629598

I. S. Duff and J. K. Reid, The Multifrontal Solution of Indefinite Sparse Symmetric Linear, ACM Transactions on Mathematical Software, vol.9, issue.3, pp.302-325, 1983.
DOI : 10.1145/356044.356047

P. Ghysels, X. S. Li, F. Rouet, S. Williams, and A. Napov, An Efficient Multicore Implementation of a Novel HSS-Structured Multifrontal Solver Using Randomized Sampling, SIAM Journal on Scientific Computing, vol.38, issue.5, 2016.
DOI : 10.1137/15M1010117

A. Gillman, P. Young, and P. Martinsson, A direct solver with O(N) complexity for integral equations on one-dimensional domains, Frontiers of Mathematics in China, vol.17, issue.4, pp.217-247, 2012.
DOI : 10.1007/s11464-012-0188-3

W. Hackbusch, A Sparse Matrix Arithmetic Based on $\Cal H$ -Matrices. Part I: Introduction to ${\Cal H}$ -Matrices, Computing, vol.62, issue.2, pp.89-108, 1999.
DOI : 10.1007/s006070050015

N. Halko, P. Martinsson, and J. A. Tropp, Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions, SIAM Review, vol.53, issue.2, pp.217-288, 2011.
DOI : 10.1137/090771806

URL : http://arxiv.org/abs/0909.4061

J. L. Excellent and M. W. Sid-lakhdar, A study of shared-memory parallelism in a multifrontal solver, Parallel Computing, vol.40, pp.3-434, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01060322

E. Liberty, F. Woolfe, P. Martinsson, V. Rokhlin, and M. Tygert, Randomized algorithms for the low-rank approximation of matrices, Proceedings of the National Academy of Sciences, pp.20167-20172, 2007.
DOI : 10.1073/pnas.0709640104

J. W. Liu, The Role of Elimination Trees in Sparse Factorization, SIAM Journal on Matrix Analysis and Applications, vol.11, issue.1, pp.134-172, 1990.
DOI : 10.1137/0611010

J. W. Liu, The Multifrontal Method for Sparse Matrix Solution: Theory and Practice, SIAM Review, vol.34, issue.1, pp.82-109, 1992.
DOI : 10.1137/1034004

G. Pichon, E. Darve, M. Faverge, P. Ramet, and J. Roman, Sparse supernodal solver using Block Low-Rank compression, Proceedings of 31st International Parallel and Distributed Processing Symposium (IPDPS'17), 2017.
URL : https://hal.archives-ouvertes.fr/hal-01502215

H. Pouransari, P. Coulier, and E. Darve, Fast hierarchical solvers for sparse matrices using low-rank approximation. ArXiv e-prints, 2015.

R. Schreiber, A New Implementation of Sparse Gaussian Elimination, ACM Transactions on Mathematical Software, vol.8, issue.3, pp.256-276, 1982.
DOI : 10.1145/356004.356006

D. Shantsev, P. Jaysaval, S. De-la-kethulle-de-ryhove, P. R. Amestoy, A. Buttari et al., Large-scale 3D EM modeling with a Block Low- Rank multifrontal direct solver, Geophysical Journal International, 2017.
DOI : 10.1093/gji/ggx106

W. M. Sid-lakhdar and E. Lyon, Scaling multifrontal methods for the solution of large sparse linear systems on hybrid shared-distributed memory architectures, 2014.

A. Tarantola, Inversion of seismic reflection data in the acoustic approximation, GEOPHYSICS, vol.49, issue.8, pp.1259-1266, 1984.
DOI : 10.1190/1.1441754

C. Weisbecker, Improving multifrontal solvers by means of algebraic block lowrank representations, 2013.
URL : https://hal.archives-ouvertes.fr/tel-00934939

S. Williams, A. Waterman, and D. Patterson, Roofline, Communications of the ACM, vol.52, issue.4, pp.65-76, 2009.
DOI : 10.1145/1498765.1498785

J. Xia, Efficient Structured Multifrontal Factorization for General Large Sparse Matrices, SIAM Journal on Scientific Computing, vol.35, issue.2, pp.832-860, 2013.
DOI : 10.1137/120867032

J. Xia, S. Chandrasekaran, M. Gu, and X. S. Li, Superfast Multifrontal Method for Large Structured Linear Systems of Equations, SIAM Journal on Matrix Analysis and Applications, vol.31, issue.3, pp.311382-1411, 2009.
DOI : 10.1137/09074543X

J. Xia, S. Chandrasekaran, M. Gu, and X. S. Li, Fast algorithms for hierarchically semiseparable matrices, Numerical Linear Algebra with Applications, vol.34, issue.6, pp.953-976, 2010.
DOI : 10.1002/nla.691

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.420.2753