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 31 articles )

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  1. Cedric Nugteren: CLBlast: A Tuned OpenCL BLAS Library (2017) arXiv
  2. Agullo, Emmanuel; Buttari, Alfredo; Guermouche, Abdou; Lopez, Florent: Implementing multifrontal sparse solvers for multicore architectures with sequential task flow runtime systems (2016)
  3. Beliakov, Gleb; Matiyasevich, Yuri: A parallel algorithm for calculation of determinants and minors using arbitrary precision arithmetic (2016)
  4. Chiang, Nai-Yuan; Zavala, Victor M.: An inertia-free filter line-search algorithm for large-scale nonlinear programming (2016)
  5. 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)
  6. Iwen, M.A.; Ong, B.W.: A distributed and incremental SVD algorithm for agglomerative data analysis on large networks (2016)
  7. Michailidis, Panagiotis D.; Margaritis, Konstantinos G.: Scientific computations on multi-core systems using different programming frameworks (2016)
  8. 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)
  9. Sukkari, Dalal; Ltaief, Hatem; Keyes, David: A high performance QDWH-SVD solver using hardware accelerators (2016)
  10. Baboulin, M.; Dongarra, J.; Lacroix, R.: Computing least squares condition numbers on hybrid multicore/GPU systems (2015)
  11. 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)
  12. Ciznicki, Milosz; Kurowski, Krzysztof; Węglarz, Jan: Evaluation of selected resource allocation and scheduling methods in heterogeneous many-core processors and graphics processing units (2014)
  13. D’Azevedo, Eduardo; Hu, Zhiang; Su, Shi-Quan; Wong, Kwai: Solving a large scale radiosity problem on GPU-based parallel computers (2014)
  14. 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)
  15. Costa, Anthony B.; Green, Jason R.: Extending the length and time scales of Gram-Schmidt Lyapunov vector computations (2013)
  16. Ghysels, P.; Ashby, T.J.; Meerbergen, K.; Vanroose, W.: Hiding global communication latency in the GMRES algorithm on massively parallel machines (2013)
  17. Obrecht, Christian; Kuznik, Frédéric; Tourancheau, Bernard; Roux, Jean-Jacques: Multi-GPU implementation of the lattice Boltzmann method (2013)
  18. Wang, Lu; Hu, Xiaozhe; Cohen, Jonathan; Xu, Jinchao: A parallel auxiliary grid algebraic multigrid method for graphic processing units (2013)
  19. Bosilca, George; Bouteiller, Aurelien; Danalis, Anthony; Herault, Thomas; Lemarinier, Pierre; Dongarra, Jack: DAGuE: A generic distributed DAG engine for high performance computing (2012) ioport
  20. 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) ioport

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