PLASMA

The Parallel Linear Algebra for Scalable Multi-core Architectures (PLASMA) project aims to address the critical and highly disruptive situation that is facing the Linear Algebra and High Performance Computing community due to the introduction of multi-core architectures. PLASMA’s ultimate goal is to create software frameworks that enable programmers to simplify the process of developing applications that can achieve both high performance and portability across a range of new architectures. The development of programming models that enforce asynchronous, out of order scheduling of operations is the concept used as the basis for the definition of a scalable yet highly efficient software framework for Computational Linear Algebra applications.

Keywords for this software

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References in zbMATH (referenced in 45 articles , 1 standard article )

Showing results 1 to 20 of 45.
Sorted by year (citations)

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  1. Marrakchi, Sirine; Jemni, Mohamed: Static scheduling with load balancing for solving triangular band linear systems on multicore processors (2021)
  2. Duff, Iain; Hogg, Jonathan; Lopez, Florent: A new sparse (LDL^T) solver using a posteriori threshold pivoting (2020)
  3. Bylina, Beata; Bylina, Jarosław: The parallel tiled WZ factorization algorithm for multicore architectures (2019)
  4. Dongarra, Jack; Gates, Mark; Haidar, Azzam; Kurzak, Jakub; Luszczek, Piotr; Wu, Panruo; Yamazaki, Ichitaro; Yarkhan, Asim; Abalenkovs, Maksims; Bagherpour, Negin; Hammarling, Sven; Šístek, Jakub; Stevens, David; Zounon, Mawussi; Relton, Samuel D.: PLASMA: Parallel linear algebra software for multicore using OpenMP (2019)
  5. Fodor, Szabina; Németh, Zoltán: Numerical analysis of parallel implementation of the reorthogonalized ABS methods (2019)
  6. Carson, Erin; Higham, Nicholas J.: Accelerating the solution of linear systems by iterative refinement in three precisions (2018)
  7. Duff, Iain; Hogg, Jonathan; Lopez, Florent: Experiments with sparse Cholesky using a sequential task-flow implementation (2018)
  8. Elmar Peise; Paolo Bientinesi: Algorithm 979: Recursive Algorithms for Dense Linear Algebra - The ReLAPACK Collection (2017) not zbMATH
  9. Van Zee, Field G.; Smith, Tyler M.: Implementing high-performance complex matrix multiplication via the 3m and 4m methods (2017)
  10. Abdelfattah, A.; Anzt, H.; Dongarra, J.; Gates, M.; Haidar, A.; Kurzak, J.; Luszczek, P.; Tomov, S.; Yamazaki, I.; YarKhan, A.: Linear algebra software for large-scale accelerated multicore computing (2016)
  11. Abdelfattah, Ahmad; Keyes, David; Ltaief, Hatem: KBLAS: an optimized library for dense matrix-vector multiplication on GPU accelerators (2016)
  12. Agullo, Emmanuel; Buttari, Alfredo; Guermouche, Abdou; Lopez, Florent: Implementing multifrontal sparse solvers for multicore architectures with sequential task flow runtime systems (2016)
  13. Beliakov, Gleb; Matiyasevich, Yuri: A parallel algorithm for calculation of determinants and minors using arbitrary precision arithmetic (2016)
  14. Chiang, Nai-Yuan; Zavala, Victor M.: An inertia-free filter line-search algorithm for large-scale nonlinear programming (2016)
  15. Ghysels, Pieter; Li, Xiaoye S.; Rouet, François-Henry; Williams, Samuel; Napov, Artem: An efficient multicore implementation of a novel HSS-structured multifrontal solver using randomized sampling (2016)
  16. Iwen, M. A.; Ong, B. W.: A distributed and incremental SVD algorithm for agglomerative data analysis on large networks (2016)
  17. Liao, Xiangke; Li, Shengguo; Cheng, Lizhi; Gu, Ming: An improved divide-and-conquer algorithm for the banded matrices with narrow bandwidths (2016)
  18. Michailidis, Panagiotis D.; Margaritis, Konstantinos G.: Scientific computations on multi-core systems using different programming frameworks (2016)
  19. Rupp, Karl; Tillet, Philippe; Rudolf, Florian; Weinbub, Josef; Morhammer, Andreas; Grasser, Tibor; Jüngel, Ansgar; Selberherr, Siegfried: ViennaCL-linear algebra library for multi- and many-core architectures (2016)
  20. Sukkari, Dalal; Ltaief, Hatem; Keyes, David: A high performance QDWH-SVD solver using hardware accelerators (2016)

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Further publications can be found at: http://icl.cs.utk.edu/plasma/pubs/index.html