Alfredo Buttari

• 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.

• PLASMA.
• LAPACK / ScaLAPACK.

• Slides
• Optimized dense matrix-vector product for x86 with SSE
• The dynamic, multithreaded Cholesky code

Slides

Slides

[1]	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.
[2]	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.
[3]	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 ]
[4]	A. Buttari. Fine-grained multithreading for the multifrontal QR factorization of sparse matrices. 2011. To appear on SIAM SISC. APO technical report number RT-APO-11-6 [PDF].
[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 ]
[14]	G. Bella, F. del Citto, S. Filippone, A. Buttari, and A. de Maio. FAST-EVP: Parallel high performance computing in engine applications. International Journal of Computational Science and Engineering (IJCSE), 2006. To appear.

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[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]	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 ]
[4]	A. Buttari. Fine grained sparse QR factorization for multicore systems. June 2011. Poster at The Householder Symposium 2011.
[5]	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].
[6]	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 ]
[7]	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 ]
[8]	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].
[9]	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 ]
[10]	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 ]
[11]	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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[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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[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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