L. Greengard and V. Rokhlin, A fast algorithm for particle simulations, Journal of Computational Physics, vol.73, issue.2, 1987.

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

A. Björck, Numerical methods for least squares problems. Siam, 1996.

L. Stanisic, S. Thibault, A. Legrand, B. Videau, and J. Méhaut, Faithful performance prediction of a dynamic task-based runtime system for heterogeneous multi-core architectures, Concurrency and Computation: Practice and Experience, 2015.
DOI : 10.1002/cpe.3555

URL : https://hal.archives-ouvertes.fr/hal-01147997

E. Agullo, A. Buttari, A. Guermouche, and F. Lopez, Implementing Multifrontal Sparse Solvers for Multicore Architectures with Sequential Task Flow Runtime Systems, ACM Transactions on Mathematical Software, vol.43, issue.2, pp.2014-2017, 2014.
DOI : 10.1145/2898348

URL : https://hal.archives-ouvertes.fr/hal-01333645

C. Augonnet, S. Thibault, R. Namyst, and P. Wacrenier, StarPU: A unified platform for task scheduling on heterogeneous multicore architectures, Concurrency and Computation: Practice and Experience, 2011.
URL : https://hal.archives-ouvertes.fr/inria-00384363

H. Casanova, A. Giersch, A. Legrand, M. Quinson, and F. Suter, Versatile, scalable, and accurate simulation of distributed applications and platforms, Journal of Parallel and Distributed Computing, vol.74, issue.10, 2014.
DOI : 10.1016/j.jpdc.2014.06.008

URL : https://hal.archives-ouvertes.fr/hal-01017319

R. Allen and K. Kennedy, Optimizing Compilers for Modern Architectures: A Dependence-Based Approach, 2002.

R. M. Badia, J. R. Herrero, J. Labarta, J. M. Pérez, E. S. Quintana-ortí et al., Parallelizing dense and banded linear algebra libraries using SMPSs, Concurrency and Computation: Practice and Experience, 2009.
DOI : 10.1002/cpe.1463

J. Kurzak and J. Dongarra, Fully dynamic scheduler for numerical computing on multicore processors, LAPACK working note, 2009.

E. Hermann, B. Raffin, F. Faure, T. Gautier, and J. Allard, Multi-GPU and Multi-CPU Parallelization for Interactive Physics Simulations, Euro-Par, 2010.
DOI : 10.1007/978-3-642-15291-7_23

URL : https://hal.archives-ouvertes.fr/inria-00502448

]. G. Bosilca, A. Bouteiller, A. Danalis, T. Hérault, P. Lemarinier et al., DAGuE: A generic distributed DAG engine for high performance computing, Parallel Computing, vol.38, issue.1, 2012.

A. Duran, E. Ayguadé, R. M. Badia, J. Labarta, L. Martinell et al., OmpSs: A PROPOSAL FOR PROGRAMMING HETEROGENEOUS MULTI-CORE ARCHITECTURES, Parallel Processing Letters, vol.21, issue.02, 2011.
DOI : 10.1142/S0129626411000151

T. R. Scogland, W. Feng, B. Rountree, and B. R. De-supinski, CoreTSAR: Adaptive Worksharing for Heterogeneous Systems, Supercomputing -29th International Conference, 2014.
DOI : 10.1007/978-3-319-07518-1_11

G. Quintana-ortí, E. S. Quintana-ortí, R. A. Van-de-geijn, F. G. Van-zee, and E. Chan, Programming matrix algorithms-by-blocks for thread-level parallelism, ACM Transactions on Mathematical Software, vol.36, issue.3, 2009.
DOI : 10.1145/1527286.1527288

E. Agullo, J. Demmel, J. Dongarra, B. Hadri, J. Kurzak et al., Numerical linear algebra on emerging architectures: The PLASMA and MAGMA projects, Journal of Physics: Conference Series, vol.180, issue.1, p.12037, 2009.
DOI : 10.1088/1742-6596/180/1/012037

G. Bosilca, A. Bouteiller, A. Danalis, T. Herault, P. Luszczek et al., Dense linear algebra on distributed heterogeneous hardware with a symbolic DAG approach, Scalable Computing and Communications: Theory and Practice, 2013.

X. Lacoste, M. Faverge, P. Ramet, S. Thibault, and G. Bosilca, Taking Advantage of Hybrid Systems for Sparse Direct Solvers via Task-Based Runtimes, 2014 IEEE International Parallel & Distributed Processing Symposium Workshops, 2014.
DOI : 10.1109/IPDPSW.2014.9

URL : https://hal.archives-ouvertes.fr/hal-00925017

K. Kim and V. Eijkhout, A Parallel Sparse Direct Solver via Hierarchical DAG Scheduling, ACM Transactions on Mathematical Software, vol.41, issue.1, 2014.
DOI : 10.1145/2629641

A. Buttari, Fine-Grained Multithreading for the Multifrontal $QR$ Factorization of Sparse Matrices, SIAM Journal on Scientific Computing, vol.35, issue.4, 2013.
DOI : 10.1137/110846427

URL : https://hal.archives-ouvertes.fr/hal-01122471

X. S. Li, An overview of SuperLU, ACM Transactions on Mathematical Software, vol.31, issue.3, 2005.
DOI : 10.1145/1089014.1089017

P. Cicotti, X. S. Li, and S. B. Baden, Performance modeling tools for parallel sparse linear algebra computations, " in Parallel Computing: From Multicores and GPU's to Petascale, 2009.

I. S. Duff and J. K. Reid, The multifrontal solution of indefinite sparse symmetric linear systems, ACM Transactions On Mathematical Software, vol.9, 1983.

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

P. R. Amestoy, I. S. Duff, and C. Puglisi, Multifrontal QR factorization in a multiprocessor environment, Int. Journal of Num. Linear Alg. and Appl, vol.3, issue.4, 1996.

T. A. Davis, Algorithm 915, SuiteSparseQR, ACM Transactions on Mathematical Software, vol.38, issue.1, 2011.
DOI : 10.1145/2049662.2049670

A. Geist and E. G. Ng, Task scheduling for parallel sparse Cholesky factorization, International Journal of Parallel Programming, vol.27, issue.4, 1989.
DOI : 10.1007/BF01407861

E. Agullo, O. Beaumont, L. Eyraud-dubois, J. Herrmann, S. Kumar et al., Bridging the Gap between Performance and Bounds of Cholesky Factorization on Heterogeneous Platforms, 2015 IEEE International Parallel and Distributed Processing Symposium Workshop, 2015.
DOI : 10.1109/IPDPSW.2015.35

URL : https://hal.archives-ouvertes.fr/hal-01120507

L. Stanisic, A. Legrand, and V. Danjean, An effective git and orgmode based workflow for reproducible research, SIGOPS Oper. Syst. Rev, vol.49, issue.1, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01112795

L. , M. Schnorr, and A. Legrand, Visualizing More Performance Data Than What Fits on Your Screen, Tools for High Performance Computing 2012, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00737651

L. Marchal, O. Sinnen, and F. Vivien, Scheduling Tree-Shaped Task Graphs to Minimize Memory and Makespan, 2013 IEEE 27th International Symposium on Parallel and Distributed Processing, 2013.
DOI : 10.1109/IPDPS.2013.55

URL : https://hal.archives-ouvertes.fr/hal-00740105

C. Augonnet, O. Aumage, N. Furmento, R. Namyst, and S. Thibault, StarPU-MPI: Task programming over clusters of machines enhanced with accelerators, " in Recent Advances in the Message Passing Interface, ser, Lecture Notes in Computer Science, J. Träff, S. Benkner, and J. Dongarra, vol.7490