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.

References in zbMATH (referenced in 34 articles )

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  1. Elmar Peise; Paolo Bientinesi: Algorithm 979: Recursive Algorithms for Dense Linear Algebra - The ReLAPACK Collection (2017)
  2. 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)
  3. Abdelfattah, Ahmad; Keyes, David; Ltaief, Hatem: KBLAS: an optimized library for dense matrix-vector multiplication on GPU accelerators (2016)
  4. Agullo, Emmanuel; Buttari, Alfredo; Guermouche, Abdou; Lopez, Florent: Implementing multifrontal sparse solvers for multicore architectures with sequential task flow runtime systems (2016)
  5. Beliakov, Gleb; Matiyasevich, Yuri: A parallel algorithm for calculation of determinants and minors using arbitrary precision arithmetic (2016)
  6. Chiang, Nai-Yuan; Zavala, Victor M.: An inertia-free filter line-search algorithm for large-scale nonlinear programming (2016)
  7. 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)
  8. Iwen, M.A.; Ong, B.W.: A distributed and incremental SVD algorithm for agglomerative data analysis on large networks (2016)
  9. Michailidis, Panagiotis D.; Margaritis, Konstantinos G.: Scientific computations on multi-core systems using different programming frameworks (2016)
  10. 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)
  11. Sukkari, Dalal; Ltaief, Hatem; Keyes, David: A high performance QDWH-SVD solver using hardware accelerators (2016)
  12. Granat, Robert; Kågström, Bo; Kressner, Daniel; Shao, Meiyue: Algorithm 953: Parallel library software for the multishift QR algorithm with aggressive early deflation (2015)
  13. Kolberg, Mariana; Bohlender, Gerd; Fernandes, Luiz Gustavo: An efficient approach to solve very large dense linear systems with verified computing on clusters. (2015)
  14. Muchmore, Patrick; Marjoram, Paul: Exact likelihood-free Markov chain Monte Carlo for elliptically contoured distributions (2015)
  15. Tillenius, Martin: Superglue: a shared memory framework using data versioning for dependency-aware task-based parallelization (2015)
  16. Van Zee, Field G.; van de Geijn, Robert A.: BLIS: a framework for rapidly instantiating BLAS functionality (2015)
  17. Ciznicki, Milosz; Kurowski, Krzysztof; Węglarz, Jan: Evaluation of selected resource allocation and scheduling methods in heterogeneous many-core processors and graphics processing units (2014)
  18. D’Azevedo, Eduardo; Hu, Zhiang; Su, Shi-Quan; Wong, Kwai: Solving a large scale radiosity problem on GPU-based parallel computers (2014)
  19. 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)
  20. Ghysels, P.; Ashby, T.J.; Meerbergen, K.; Vanroose, W.: Hiding global communication latency in the GMRES algorithm on massively parallel machines (2013)

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