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.

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  1. Mohanty, Sraban Kumar; Sajith, G.: An input/output efficient algorithm for Hessenberg reduction (2019)
  2. Michailidis, Panagiotis D.; Margaritis, Konstantinos G.: Scientific computations on multi-core systems using different programming frameworks (2016)
  3. Schatz, Martin D.; van de Geijn, Robert A.; Poulson, Jack: Parallel matrix multiplication: a systematic journey (2016)
  4. Banerjee, Amartya S.; Elliott, Ryan S.; James, Richard D.: A spectral scheme for Kohn-Sham density functional theory of clusters (2015)
  5. Van Zee, Field G.; van de Geijn, Robert A.: BLIS: a framework for rapidly instantiating BLAS functionality (2015)
  6. D’Azevedo, Eduardo; Hu, Zhiang; Su, Shi-Quan; Wong, Kwai: Solving a large scale radiosity problem on GPU-based parallel computers (2014)
  7. 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)
  8. Zhu, Sheng-Xin; Gu, Tong-Xiang; Liu, Xing-Ping: Minimizing synchronizations in sparse iterative solvers for distributed supercomputers (2014)
  9. Petschow, M.; Peise, E.; Bientinesi, P.: High-performance solvers for dense Hermitian eigenproblems (2013)
  10. 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)
  11. Scherer, Philipp O. J.: Computational physics. Simulation of classical and quantum systems (2013)
  12. Lubin, Miles; Petra, Cosmin G.; Anitescu, Mihai: The parallel solution of dense saddle-point linear systems arising in stochastic programming (2012)
  13. 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)
  14. Tibbits, Matthew M.; Haran, Murali; Liechty, John C.: Parallel multivariate slice sampling (2011) ioport
  15. Tibbits, Matthew M.; Haran, Murali; Liechty, John C.: Parallel multivariate slice sampling (2011)
  16. Vömel, Christof: ScaLAPACK’s MRRR algorithm (2010)
  17. Baboulin, Marc; Giraud, Luc; Gratton, Serge; Langou, Julien: Parallel tools for solving incremental dense least squares problems: application to space geodesy (2009)
  18. 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)
  19. Gustavson, Fred G.; Karlsson, Lars; Kågström, Bo: Distributed SBP Cholesky factorization algorithms with near-optimal scheduling (2009)
  20. Quintana-Ortí, Gregorio; Quintana-Ortí, Enrique S.; Van De Geijn, Robert A.; Van Zee, Field G.; Chan, Ernie: Programming matrix algorithms-by-blocks for thread-level parallelism (2009)

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