MAGMA

The MAGMA project aims to develop a dense linear algebra library similar to LAPACK but for heterogeneous/hybrid architectures, starting with current ”Multicore+GPU” systems. The MAGMA research is based on the idea that, to address the complex challenges of the emerging hybrid environments, optimal software solutions will themselves have to hybridize, combining the strengths of different algorithms within a single framework. Building on this idea, we aim to design linear algebra algorithms and frameworks for hybrid manycore and GPU systems that can enable applications to fully exploit the power that each of the hybrid components offers.


References in zbMATH (referenced in 52 articles )

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  1. Bylina, Beata; Bylina, Jarosław: The parallel tiled WZ factorization algorithm for multicore architectures (2019)
  2. 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)
  3. Fodor, Szabina; Németh, Zoltán: Numerical analysis of parallel implementation of the reorthogonalized ABS methods (2019)
  4. Carson, Erin; Higham, Nicholas J.: Accelerating the solution of linear systems by iterative refinement in three precisions (2018)
  5. Duff, Iain; Hogg, Jonathan; Lopez, Florent: Experiments with sparse Cholesky using a sequential task-flow implementation (2018)
  6. Cedric Nugteren: CLBlast: A Tuned OpenCL BLAS Library (2017) arXiv
  7. Chen, Cheng; Fang, Jianbin; Tang, Tao; Yang, Canqun: LU factorization on heterogeneous systems: an energy-efficient approach towards high performance (2017)
  8. Filippone, Salvatore; Cardellini, Valeria; Barbieri, Davide; Fanfarillo, Alessandro: Sparse matrix-vector multiplication on GPGPUs (2017)
  9. Jonsson, Thorsteinn H.; Manolescu, Andrei; Goan, Hsi-Sheng; Abdullah, Nzar Rauf; Sitek, Anna; Tang, Chi-Shung; Gudmundsson, Vidar: Efficient determination of the Markovian time-evolution towards a steady-state of a complex open quantum system (2017)
  10. Khimich, A. N.; Popov, A. V.; Chistyakov, O. V.: Hybrid algorithms for solving the algebraic eigenvalue problem with sparse matrices (2017)
  11. Van Zee, Field G.; Smith, Tyler M.: Implementing high-performance complex matrix multiplication via the 3m and 4m methods (2017)
  12. Abdelfattah, Ahmad; Keyes, David; Ltaief, Hatem: KBLAS: an optimized library for dense matrix-vector multiplication on GPU accelerators (2016)
  13. Agullo, Emmanuel; Buttari, Alfredo; Guermouche, Abdou; Lopez, Florent: Implementing multifrontal sparse solvers for multicore architectures with sequential task flow runtime systems (2016)
  14. Beliakov, Gleb; Matiyasevich, Yuri: A parallel algorithm for calculation of determinants and minors using arbitrary precision arithmetic (2016)
  15. Charara, Ali; Ltaief, Hatem; Keyes, David: Redesigning triangular dense matrix computations on GPUs (2016)
  16. Chiang, Nai-Yuan; Zavala, Victor M.: An inertia-free filter line-search algorithm for large-scale nonlinear programming (2016)
  17. 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)
  18. Iwen, M. A.; Ong, B. W.: A distributed and incremental SVD algorithm for agglomerative data analysis on large networks (2016)
  19. Michailidis, Panagiotis D.; Margaritis, Konstantinos G.: Scientific computations on multi-core systems using different programming frameworks (2016)
  20. 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)

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