Alfredo Buttari

Table of Contents

CNRS researcher @ IRIT
2 rue Camichel, 31071 Toulouse, France (map)
telephone: +33 5 34 32 22 08
email: abuttari at n7 dot fr

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Bio

I am currently a CNRS (Centre National de la Recherche Scientifique) researcher at the IRIT (Institut de Recherche en Informatique de Toulouse) laboratory in Toulouse (France) where I am a member of the APO (Algorithmes Parallèles et Optimisation). My interest are in High Performance Computing, parallel computing, computational linear algebra (numerical software in general). Here is my CV and a short bio:

Research

Current projects

  • qr_mumps. I am the main developer of the qr_mumps software. qr_mumps is a software package for the solution of sparse, linear systems on multicore computers. It implements a direct solution method based on the QR factorization of the input matrix. Therefore, it is suited to solving sparse least-squares problems and to computing the minimum-norm solution of sparse, underdetermined problems. qr_mumps is a parallel, multithreaded software based on the OpenMP standard.Parallelism is achieved by dividing the workload into fine grained tasks that are arranged in a Direct Acyclic Graph (DAG). The execution of these tasks is guiged by an asynchronous and dynamic data-flow programming model which provides high efficiency and scalability. Here is a poster I presented at the 2011 Householder meeting. In this context I am currently working on the integration of runtime systems (such as StarPU or Parsec) and on the usage of GPUs.
  • MUMPS. I am a member of the MUltifrontal Massively Parallel sparse direct Solver (MUMPS) project. The MUMPS package implements a multifrontal (direct) solver for sparse linear systems. It is an SPMD parallel code based on MPI and provides a wide range of features. Within this project I am principally involved in investigating the use of Low-Rank approximation techniques to reduce the execution time as well as the memory footprint.
  • PSBLAS. I collaborate to the Parallel Sparse BLAS (PSBLAS) project. The PSBLAS software implements basic kernels for sparse computations as well as a bunch of iterative methods (CG, GMRES, BiCG,…) and preconditioners (Block-Jacobi, Additive-Schwarz, Multilevel,…) for the solution of sparse linear systems. It is an SPMD parallel code based on MPI and it is written in Fortran2003.

Past projects

Teaching

Algèbre Linéaire Creuse

Systèmes Concurrents

Publications

Journals

[1] A. Buttari. Fine-grained multithreading for the multifrontal qr factorization of sparse matrices. SIAM Journal on Scientific Computing, 35(4):C323-C345, 2013. [ DOI | arXiv | http ]
[2] L. Bouchet, P. Amestoy, A. Buttari, F.-H. Rouet, and M. Chauvin. INTEGRAL/SPI data segmentation to retrieve sources intensity variations. Astronomy & Astrophysics, 2013. to appear.
[3] L. Bouchet, P. Amestoy, A. Buttari, F.-H. Rouet, and M. Chauvin. Simultaneous analysis of large INTEGRAL/SPI datasets: optimizing the computation of the solution and its variance using sparse matrix algorithms. Astronomy and Computing, 2013. to appear.
[4] S. Filippone and A. Buttari. Object-oriented techniques for sparse matrix computations in Fortran 2003. ACM Transactions on Mathematical Software, 38(4):23:1-23:20, August 2012. [ http ]
[5] A. Buttari, J. Langou, J. Kurzak, and J. Dongarra. A class of parallel tiled linear algebra algorithms for multicore architectures. Parallel Comput., 35(1):38-53, 2009. [doi:10.1016/j.parco.2008.10.002]. [ DOI ]
[6] M. Baboulin, A. Buttari, J. Dongarra, J. Kurzak, J. Langou, J. Langou, P. Luszczek, and S. Tomov. Accelerating scientific computations with mixed precision algorithms. Computer Physics Communications, 180(12):2526-2533, 2009. [doi:10.1016/j.cpc.2008.11.005].
[7] A. Buttari, J. Dongarra, J. Kurzak, P. Luszczek, and S. Tomov. Using mixed precision for sparse matrix computations to enhance the performance while achieving 64-bit accuracy. ACM Trans. Math. Softw., 34(4):1-22, 2008. [doi:10.1145/1377596.1377597]. [ DOI ]
[8] J. Kurzak, A. Buttari, and J. Dongarra. Solving systems of linear equations on the cell processor using cholesky factorization. IEEE Trans. Parallel Distrib. Syst., 19(9):1175-1186, 2008. [doi:10.1109/TPDS.2007.70813]. [ DOI ]
[9] A. Buttari, J. Langou, J. Kurzak, and J. Dongarra. Parallel tiled QR factorization for multicore architectures. Concurr. Comput. : Pract. Exper., 20(13):1573-1590, 2008. [doi:10.1002/cpe.v20:13]. [ DOI ]
[10] J. Kurzak, A. Buttari, P. Luszczek, and J. Dongarra. The playstation 3 for high-performance scientific computing. Computing in Science and Eng., 10(3):84-87, 2008. [doi:10.1109/MCSE.2008.85]. [ DOI ]
[11] A. Buttari, P. D'Ambra, D. di Serafino, and S. Filippone. 2LEV-D2P4: a package of high-performance preconditioners for scientific and engineering applications. Appl. Algebra Eng., Commun. Comput., 18(3):223-239, 2007. [doi:10.1007/s00200-007-0035-z]. [ DOI ]
[12] A. Buttari, J. Dongarra, J. Langou, J. Langou, P. Luszczek, and J. Kurzak. Mixed precision iterative refinement techniques for the solution of dense linear systems. Int. J. High Perform. Comput. Appl., 21(4):457-466, 2007. [doi:10.1177/1094342007084026]. [ DOI ]
[13] A. Buttari, V. Eijkhout, J. Langou, and S. Filippone. Performance optimization and modeling of blocked sparse kernels. Int. J. High Perform. Comput. Appl., 21(4):467-484, 2007. [doi:10.1177/1094342007083801]. [ DOI ]

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Conferences

[1] P. Amestoy, A. Buttari, G. Joslin, J.-Y. L'Excellent, M. Sid-Lakhdar, C. Weisbecker, M. Forzan, C. Pozza, R. Perrin, and V. Pellissier. Shared memory parallelism and low-rank approximation techniques applied to direct solvers in FEM simulation (regular paper). In IEEE International Conference on the Computation of Electromagnetic Fields (COMPUMAG), Budapest, Hungary, 30/06/2013-04/07/2013, http://www.ieee.org/, juin 2013. IEEE. to appear.
[2] L. Bouchet, P. Amestoy, A. Buttari, F.-H. Rouet, and M. Chauvin. INTEGRAL/SPI data segmentation to retrieve sources intensity variations (regular paper). In A. Goldwurm, F. Lebrun, and C. Winkler, editors, An INTEGRAL view of the high-energy sky (the first 10 years), Paris, France, 15/10/2012-19/10/2012, 2013. to appear.
[3] Emmanuel Agullo, Alfredo Buttari, Abdou Guermouche, and Florent Lopez. Multifrontal qr factorization for multicore architectures over runtime systems. In Felix Wolf, Bernd Mohr, and Dieter Mey, editors, Euro-Par 2013 Parallel Processing, volume 8097 of Lecture Notes in Computer Science, pages 521-532. Springer Berlin Heidelberg, 2013. [ DOI | http ]
[4] A. Buttari. Fine granularity sparse QR factorization for multicore based systems. In Proceedings of the 10th international conference on Applied Parallel and Scientific Computing - Volume 2, PARA'10, pages 226-236, Berlin, Heidelberg, 2012. Springer-Verlag. [doi:10.1007/978-3-642-28145-7_23]. [ DOI | http ]
[5] A. Buttari. Fine grained sparse QR factorization for multicore systems. June 2011. Poster at The Householder Symposium 2011.
[6] A. Buttari, J. Langou, J. Kurzak, and J. Dongarra. Parallel tiled QR factorization for multicore architectures. In PPAM'07: Proceedings of the 7th international conference on Parallel processing and applied mathematics, pages 639-648, Berlin, Heidelberg, 2008. Springer-Verlag. [doi:10.1007/978-3-540-68111-3_67].
[7] A. Buttari, J. Dongarra, P. Husbands, J. Kurzak, and K. Yelick. Multithreading for synchronization tolerance in matrix factorization. In Proceedings of the SciDAC 2007 Conference, Boston, Massachusetts, 2007. Journal of Physics: Conference Series. [doi:10.1088/1742-6596/78/1/012028]. [ DOI | http ]
[8] J. W. Demmel, J. Dongarra, B. Parlett, W. Kahan, M. Gu, D. Bindel, Y. Hida, X. S. Li, O. A. Marques, E. J. Riedy, C Vomel, J. Langou, P. Luszczek, J. Kurzak, A. Buttari, J. Langou, and S. Tomov. Prospectus for the next lapack and scalapack libraries. In PARA'06: State-of-the-Art in Scientific and Parallel Computing, Umeå, Sweden, June 2006. High Performance Computing Center North (HPC2N) and the Department of Computing Science, UmeåUniversity, Springer. [doi:10.1007/978-3-540-75755-9_2]. [ .pdf ]
[9] A. Buttari, P. D'Ambra, D. di Serafino, and S. Filippone. Extending PSBLAS to Build Parallel Schwarz Preconditioners. In Springer, editor, Applied Parallel Computing. State of the Art in Scientific Computing: 7th International Conference, PARA 2004, Lyngby, Denmark, June 20-23, 2004., volume 3732 of Lecture Notes in Computer Science, pages 593-602, February 2006. [doi:10.1007/11558958_71].
[10] J. Langou, J. Langou, P. Luszczek, J. Kurzak, A. Buttari, and J. Dongarra. Exploiting the performance of 32 bit floating point arithmetic in obtaining 64 bit accuracy (revisiting iterative refinement for linear systems). In SC '06: Proceedings of the 2006 ACM/IEEE conference on Supercomputing, page 113, New York, NY, USA, 2006. ACM. [doi:10.1145/1188455.1188573]. [ DOI ]
[11] A. Buttari, J. Dongarra, J. Kurzak, J. Langou, P. Luszczek, and S. Tomov. The impact of multicore on math software. In PARA, pages 1-10, 2006. [doi:10.1007/978-3-540-75755-9_1]. [ DOI ]
[12] G. Bella, A. Buttari, A. De Maio, F. Del Citto, S. Filippone, and F. Gasperini. FAST-EVP: an engine simulation tool. In Springer, editor, High Perfromance Computing and Communications. First International Conference, HPCC 2005, Proceedings, volume 3726 of Lecture Notes in Computer Science, pages 976-986, September 2005. [doi:10.1007/11557654_108].

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Book Chapters

[1] P. Amestoy, A. Buttari, I. Duff, A. Guermouche, J.-Y. L'Excellent, and B. Uçar. MUMPS. In David Padua, editor, Encyclopedia of Parallel Computing. Springer Verlag, 2011.
[2] P. Amestoy, A. Buttari, I. Duff, A. Guermouche, J.-Y. L'Excellent, and B. Uçar. The Multifrontal Method. In David Padua, editor, Encyclopedia of Parallel Computing. Springer Verlag, 2011.
[3] J. Demmel et al. Prospectus for a linear algebra software library for dense matrix problems. In Sanguthevar Rajasekaran and John Reif, editors, Handbook of Parallel Computing: Models, Algorithms and Applications, volume 17 of Chapman & HallCRC Computer & Information Science. CRC Press, 1 edition, December 2007. ISBN: 9781584886235.
[4] A. Buttari, J. Dongarra, J. Kurzak, J. Langou, J. Langou, P. Luszczek, and S. Tomov. Exploiting mixed precision floating point hardware in scientific computations. In L. Grandinetti, editor, High Performance Computing and Grids in Action. IOS Press, 2007. [PDF].
[5] A. Buttari, J. Dongarra, J. Kurzak, and J. Langou. Parallel dense linear algebra software in the multicore era. In Junwei Cao, editor, Cyberinfrastructure Technologies and Applications. Nova Science Publishers, 2007.

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Technical reports

[1] E. Agullo, A. Buttari, A. Guermouche, and F. Lopez. Multifrontal QR factorization for multicore architectures over runtime systems. Technical Report RT-APO-13-3, IRIT, Université Paul Sabatier, Toulouse, February 2013. Accepted at EuroPar 2013 [PDF].
[2] A. Buttari, P. Luszczek, J. Kurzak, J. Dongarra, and G. Bosilca. SCOP3: A rough guide to scientific computing on the PlayStation 3. version 0.1. Technical Report UT-CS-07-595, Innovative Computing Laboratory, University of Tennessee Knoxville, April 2007.
[3] A. Buttari, J. Kurzak, and J. Dongarra. Limitations of the PlayStation 3 for High Performance Cluster Computing. Technical Report UT-CS-07-597, Innovative Computing Laboratory, University of Tennessee Knoxville, April 2007. LAPACK Working Note 185.

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