MKL

Intel® Math Kernel Library (Intel® MKL) 11.0 includes a wealth of routines to accelerate application performance and reduce development time. Today’s processors have increasing core counts, wider vector units and more varied architectures. The easiest way to take advantage of all of that processing power is to use a carefully optimized computing math library designed to harness that potential. Even the best compiler can’t compete with the level of performance possible from a hand-optimized library. Because Intel has done the engineering on these ready-to-use, royalty-free functions, you’ll not only have more time to develop new features for your application, but in the long run you’ll also save development, debug and maintenance time while knowing that the code you write today will run optimally on future generations of Intel processors. Intel® MKL includes highly vectorized and threaded Linear Algebra, Fast Fourier Transforms (FFT), Vector Math and Statistics functions. Through a single C or Fortran API call, these functions automatically scale across previous, current and future processor architectures by selecting the best code path for each.


References in zbMATH (referenced in 75 articles )

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  1. Klawonn, Axel; Kühn, Martin; Rheinbach, Oliver: Adaptive FETI-DP and BDDC methods with a generalized transformation of basis for heterogeneous problems (2018)
  2. Yang, Wangdong; Li, Kenli; Li, Keqin: A parallel computing method using blocked format with optimal partitioning for SpMV on GPU (2018)
  3. Baikov, Nikita: Algorithm and implementation details for complementary error function (2017)
  4. Belonosov, Mikhail; Dmitriev, Maxim; Kostin, Victor; Neklyudov, Dmitry; Tcheverda, Vladimir: An iterative solver for the 3D Helmholtz equation (2017)
  5. Chen, Cheng; Fang, Jianbin; Tang, Tao; Yang, Canqun: LU factorization on heterogeneous systems: an energy-efficient approach towards high performance (2017)
  6. Ferrer, Esteban: An interior penalty stabilised incompressible discontinuous Galerkin-Fourier solver for implicit large eddy simulations (2017)
  7. Ilin, Valery P.: Multi-preconditioned domain decomposition methods in the Krylov subspaces (2017)
  8. Jandron, Michael A.; Ruffa, Anthony A.; Baglama, James: An asynchronous direct solver for banded linear systems (2017)
  9. Khimich, A. N.; Popov, A. V.; Chistyakov, O. V.: Hybrid algorithms for solving the algebraic eigenvalue problem with sparse matrices (2017)
  10. Li, Ang; Serban, Radu; Negrut, Dan: Analysis of a splitting approach for the parallel solution of linear systems on GPU cards (2017)
  11. Marx, Alain; Lütjens, Hinrich: Hybrid parallelization of the XTOR-2F code for the simulation of two-fluid MHD instabilities in tokamaks (2017)
  12. Neto, D.M.; Oliveira, M.C.; Menezes, L.F.: Surface smoothing procedures in computational contact mechanics (2017)
  13. Schaerer, Roman Pascal; Bansal, Pratyuksh; Torrilhon, Manuel: Efficient algorithms and implementations of entropy-based moment closures for rarefied gases (2017)
  14. Stoykov, S.; Margenov, S.: Numerical methods and parallel algorithms for computation of periodic responses of plates (2017)
  15. Yeung, Yu-Hong; Pothen, Alex; Halappanavar, Mahantesh; Huang, Zhenyu: AMPS: an augmented matrix formulation for principal submatrix updates with application to power grids (2017)
  16. Agullo, Emmanuel; Buttari, Alfredo; Guermouche, Abdou; Lopez, Florent: Implementing multifrontal sparse solvers for multicore architectures with sequential task flow runtime systems (2016)
  17. Bolukbasi, Ercan Selcuk; Manguoglu, Murat: A multithreaded recursive and nonrecursive parallel sparse direct solver (2016)
  18. Butyugin, D.S.; Gurieva, Y.L.; Ilin, V.P.; Perevozkin, D.V.: Some geometric and algebraic aspects of domain decomposition methods (2016)
  19. Chen, Yuxin; Keyes, David; Law, Kody J.H.; Ltaief, Hatem: Accelerated dimension-independent adaptive metropolis (2016)
  20. Einkemmer, Lukas: A resistive magnetohydrodynamics solver using modern C++ and the Boost library (2016)

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