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 25 articles )

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  1. Beliakov, Gleb; Matiyasevich, Yuri: A parallel algorithm for calculation of determinants and minors using arbitrary precision arithmetic (2016)
  2. Chiang, Nai-Yuan; Zavala, Victor M.: An inertia-free filter line-search algorithm for large-scale nonlinear programming (2016)
  3. Michailidis, Panagiotis D.; Margaritis, Konstantinos G.: Scientific computations on multi-core systems using different programming frameworks (2016)
  4. Baboulin, M.; Dongarra, J.; Lacroix, R.: Computing least squares condition numbers on hybrid multicore/GPU systems (2015)
  5. Wong, Kwai; D’Azevedo, Eduardo; Hu, Zhiang; Kail, Andrew; Su, Shiquan: Solving a large-scale thermal radiation problem using an interoperable executive library framework on petascale supercomputers (2015)
  6. Ciznicki, Milosz; Kurowski, Krzysztof; Węglarz, Jan: Evaluation of selected resource allocation and scheduling methods in heterogeneous many-core processors and graphics processing units (2014)
  7. D’Azevedo, Eduardo; Hu, Zhiang; Su, Shi-Quan; Wong, Kwai: Solving a large scale radiosity problem on GPU-based parallel computers (2014)
  8. 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)
  9. Ghysels, P.; Ashby, T.J.; Meerbergen, K.; Vanroose, W.: Hiding global communication latency in the GMRES algorithm on massively parallel machines (2013)
  10. Obrecht, Christian; Kuznik, Frédéric; Tourancheau, Bernard; Roux, Jean-Jacques: Multi-GPU implementation of the lattice Boltzmann method (2013)
  11. Wang, Lu; Hu, Xiaozhe; Cohen, Jonathan; Xu, Jinchao: A parallel auxiliary grid algebraic multigrid method for graphic processing units (2013)
  12. Bosilca, George; Bouteiller, Aurelien; Danalis, Anthony; Herault, Thomas; Lemarinier, Pierre; Dongarra, Jack: DAGuE: A generic distributed DAG engine for high performance computing (2012)
  13. Du, Peng; Weber, Rick; Luszczek, Piotr; Tomov, Stanimire; Peterson, Gregory; Dongarra, Jack: From CUDA to opencl: towards a performance-portable solution for multi-platform GPU programming (2012)
  14. Igual, Francisco D.; Chan, Ernie; Quintana-Ortí, Enrique S.; Quintana-Ortí, Gregorio; Van De Geijn, Robert A.; Van Zee, Field G.: The FLAME approach: from dense linear algebra algorithms to high-performance multi-accelerator implementations (2012)
  15. Vömel, Christof; Tomov, Stanimire; Dongarra, Jack: Divide and conquer on hybrid GPU-accelerated multicore systems (2012)
  16. Weinbub, Josef; Rupp, Karl; Selberherr, Siegfried: Towards distributed heterogenous high-performance computing with ViennaCL (2012)
  17. Ltaief, Hatem; Tomov, Stanimire; Nath, Rajib; Du, Peng; Dongarra, Jack: A scalable high performant Cholesky factorization for multicore with GPU accelerators (2011)
  18. Mendiratta, Karan; Polizzi, Eric: A threaded SPIKE algorithm for solving general banded systems (2011)
  19. Michailidis, Panagiotis D.; Margaritis, Konstantinos G.: Parallel direct methods for solving the system of linear equations with pipelining on a multicore using OpenMP (2011)
  20. Petschow, M.; Bientinesi, P.: $MR^3-SMP$: a symmetric tridiagonal eigensolver for multi-core architectures (2011)

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