Free Access
RAIRO-Oper. Res.
Volume 55, Number 2, March-April 2021
Page(s) 545 - 559
Published online 31 March 2021
  • A. Abdelfattah, H. Anzt, J. Dongarra, M. Gates, A. Haidar, J. Kurzak, P. Luszczek, S. Tomov, I. Yamazaki and A. YarKhan, Linear algebra software for large-scale accelerated multicore computing, Acta Numer. 25 (2016) 1–160. [Google Scholar]
  • E. Agullo, J. Demmel, J. Dongarra, B. Hadri, J. Kurzak, J. Langou, H. Ltaief, P. Luszczek and S. Tomov, Numerical linear algebra on emerging architectures: the plasma and magma projects. In: Vol. 180 ofJournal of Physics: Conference Series. IOP Publishing, Bristol, UK (2009) 012037. [Google Scholar]
  • M. Belmabrouk and M. Marrakchi, Optimal parallel scheduling for resolution a triangular system with availability constraints. In: 2015 IEEE/ACS 12th International Conference of Computer Systems and Applications (AICCSA) , IEEE, Piscataway, NJ, USA (2015) 1–7. [Google Scholar]
  • M. Belmabrouk and M. Marrakchi, Comparison of parallel scheduling for triangular system resolution on multi-core processors. In: 2017 4th International Conference on Control, Decision and Information Technologies (CoDIT). IEEE, Piscataway, NJ, USA (2017) 0651–0656. [Google Scholar]
  • A. Buttari, J. Langou, J. Kurzak and J. Dongarra, A class of parallel tiled linear algebra algorithms for multicore architectures. Parallel Comput. 35 (2009) 38–53. [Google Scholar]
  • A. Charara, D. Keyes and H. Ltaief, A framework for dense triangular matrix kernels on various manycore architectures. Concurrency Comput. Pract. Experience 29 (2017). [Google Scholar]
  • E.G. Coffman and P.J. Denning, Operating Systems Theory. Prentice-Hall Englewood Cliffs, NJ, USA (1973). [Google Scholar]
  • J. Dongarra, M. Gates, A. Haidar, J. Kurzak, P. Luszczek, P. Wu, I. Yamazaki, A. YarKhan, M. Abalenkovs, N. Bagherpour and S. Hammarling, Plasma: Parallel linear algebra software for multicore using openmp. ACM Trans. Math. Softw. (TOMS) 45 (2019) 1–35. [Google Scholar]
  • C.A. Floudas and X. Lin, Mixed integer linear programming in process scheduling: modeling, algorithms, and applications. Ann. Oper. Res. 139 (2005) 131–162. [Google Scholar]
  • J. González-Domínguez, M.J. Martín, G.L. Taboada and J. Tourino, Dense triangular solvers on multicore clusters using upc. Proc. Comput. Sci. 4 (2011) 231–240. [Google Scholar]
  • Grid’5000, [online] (2007). [Google Scholar]
  • R. Iakymchuk, D. Defour, S. Collange and S. Graillat, Reproducible triangular solvers for high-performance computing. In: 2015 12th International Conference on Information Technology-New Generations. IEEE, Piscataway, NJ, USA (2015) 353–358. [Google Scholar]
  • IBM ILOG CPLEX Optimization Studio CPLEX Users Manual (1999). [Google Scholar]
  • IBM Knowlege Center, Solution of triangular system of equations with a single right-hand side. [online] [Google Scholar]
  • X. Jin, T. Yang and X. Tang, A comparison of cache blocking methods for fast execution of ensemble-based score computation. In: Proceedings of the 39th International ACM SIGIR Conference On Research and Development in Information Retrieval (2016) 629–638. [Google Scholar]
  • C.C. Kjelgaard Mikkelsen, A.B. Schwarz andL. Karlsson, Parallel robust solution of triangular linear systems. Concurrency Comput. Pract. Experience 31 (2019) e5064. [Google Scholar]
  • M. Marrakchi, Optimal parallel scheduling for the 2-steps graph with constant task cost. Parallel Comput. 18 (1992) 169–176. [Google Scholar]
  • P.D. Michailidis and K.G. Margaritis, Parallel direct methods for solving the system of linear equations with pipelining on a multicore using openmp. J. Comput. Appl. Math. 236 (2011) 326–341. [Google Scholar]
  • N.M. Missirlis and F. Tjaferis, Parallel matrix factorizations on a shared memory mimd computer. In: International Conference on Supercomputing. Vol. 297 of: Lecture Notes in Computer Science. Springer, Berlin-Heidelberg (1987) 926–938. [Google Scholar]
  • OpenMP, The OpenMP API specification for parallel programming. [online] (1997). [Google Scholar]
  • PLASMA, [online] (2009). [Google Scholar]
  • H. Shioda, K. Konishi and S. Shin, Optimal task scheduling algorithm for parallel processing. In: Proceedings of the 2011 2nd International Congress on Computer Applications and Computational Science. Vol. 145 of: Advances in Intelligent and Soft Computing. Springer, Berlin-Heidelberg (2012) 79–87. [Google Scholar]
  • C.F. Van Loan and G.H. Golub, Matrix Computations. Johns Hopkins University Press, Baltimore, MD, USA (1983). [Google Scholar]
  • T. Wicky, E. Solomonik and T. Hoefler, Communication-avoiding parallel algorithms for solving triangular systems of linear equations. In: 2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS). IEEE, Piscataway, NJ, USA (2017) 678–687. [Google Scholar]
  • A. YarKhan, J. Kurzak, P. Luszczek and J. Dongarra, Porting the plasma numerical library to the openmp standard. Int. J. Parallel Program. 45 (2017) 612–633. [Google Scholar]

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