PLAPACK is a library infrastructure for the parallel implementation of linear algebra algorithms and applications on distributed memory supercomputers such as the Intel Paragon, IBM SP2, Cray T3D/T3E, SGI PowerChallenge, and Convex Exemplar. This infrastructure allows library developers, scientists, and engineers to exploit a natural approach to encoding so-called blocked algorithms, which achieve high performance by operating on submatrices and subvectors. This feature, as well as the use of an alternative, more application-centric approach to data distribution, sets PLAPACK apart from other parallel linear algebra libraries, allowing for strong performance and significanltly less programming by the user.

References in zbMATH (referenced in 55 articles )

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  1. Michailidis, Panagiotis D.; Margaritis, Konstantinos G.: Scientific computations on multi-core systems using different programming frameworks (2016)
  2. Schatz, Martin D.; van de Geijn, Robert A.; Poulson, Jack: Parallel matrix multiplication: a systematic journey (2016)
  3. Banerjee, Amartya S.; Elliott, Ryan S.; James, Richard D.: A spectral scheme for Kohn-Sham density functional theory of clusters (2015)
  4. Van Zee, Field G.; van de Geijn, Robert A.: BLIS: a framework for rapidly instantiating BLAS functionality (2015)
  5. D’Azevedo, Eduardo; Hu, Zhiang; Su, Shi-Quan; Wong, Kwai: Solving a large scale radiosity problem on GPU-based parallel computers (2014)
  6. Schatz, Martin D.; Low, Tze Meng; van de Geijn, Robert A.; Kolda, Tamara G.: Exploiting symmetry in tensors for high performance: multiplication with symmetric tensors (2014)
  7. Zhu, Sheng-Xin; Gu, Tong-Xiang; Liu, Xing-Ping: Minimizing synchronizations in sparse iterative solvers for distributed supercomputers (2014)
  8. Petschow, M.; Peise, E.; Bientinesi, P.: High-performance solvers for dense Hermitian eigenproblems (2013)
  9. Poulson, Jack; Marker, Bryan; van de Geijn, Robert A.; Hammond, Jeff R.; Romero, Nichols A.: Elemental, a new framework for distributed memory dense matrix computations (2013)
  10. Scherer, Philipp O. J.: Computational physics. Simulation of classical and quantum systems (2013)
  11. Lubin, Miles; Petra, Cosmin G.; Anitescu, Mihai: The parallel solution of dense saddle-point linear systems arising in stochastic programming (2012)
  12. Badia, J.M.; Movilla, J.L.; Climente, J.I.; Castillo, M.; Marqués, M.; Mayo, R.; Quintana-Ortí, E.S.; Planelles, J.: Large-scale linear system solver using secondary storage: self-energy in hybrid nanostructures (2011)
  13. Tibbits, Matthew M.; Haran, Murali; Liechty, John C.: Parallel multivariate slice sampling (2011)
  14. Tibbits, Matthew M.; Haran, Murali; Liechty, John C.: Parallel multivariate slice sampling (2011)
  15. Baboulin, Marc; Giraud, Luc; Gratton, Serge; Langou, Julien: Parallel tools for solving incremental dense least squares problems: application to space geodesy (2009)
  16. Castillo, Maribel; Igual, Francisco D.; Marqués, Mercedes; Mayo, Rafael; Quintana-Ortí, Enrique S.; Quintana-Ortí, Gregorio; Rubio, Rafael; van de Geijn, Robert: Out-of-core solution of linear systems on graphics processors (2009)
  17. Zhang, Yu; Sarkar, Tapan K.: Parallel solution of integral equation-based EM problems in the frequency domain. With contributions from Hongsik Moon, Mary Taylor, Robert A. van de Geijn. (2009)
  18. Benner, Peter; Quintana-Ortí, Enrique S.; Quintana-Ortí, Gregorio: Solving linear-quadratic optimal control problems on parallel computers (2008)
  19. Béreux, Natacha: Out-of-core implementations of Cholesky factorization: loop-based versus recursive algorithms (2008)
  20. Sala, Marzio; Stanley, Kendall S.; Heroux, Michael A.: On the design of interfaces to sparse direct solvers. (2008)

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