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 45 articles )

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  1. Stoykov, S.; Margenov, S.: Numerical methods and parallel algorithms for computation of periodic responses of plates (2017)
  2. Butyugin, D.S.; Gurieva, Y.L.; Ilin, V.P.; Perevozkin, D.V.: Some geometric and algebraic aspects of domain decomposition methods (2016)
  3. Chen, Yuxin; Keyes, David; Law, Kody J.H.; Ltaief, Hatem: Accelerated dimension-independent adaptive metropolis (2016)
  4. Gholami, Amir; Malhotra, Dhairya; Sundar, Hari; Biros, George: FFT, FMM, or multigrid? A comparative study of state-of-the-art Poisson solvers for uniform and nonuniform grids in the unit cube (2016)
  5. Laptyeva, T.V.; Kozinov, E.A.; Meyerov, I.B.; Ivanchenko, M.V.; Denisov, S.V.; Hänggi, P.: Calculating Floquet states of large quantum systems: a parallelization strategy and its cluster implementation (2016)
  6. Michailidis, Panagiotis D.; Margaritis, Konstantinos G.: Scientific computations on multi-core systems using different programming frameworks (2016)
  7. Mikhalev, A.Yu.; Oseledets, I.V.: Iterative representing set selection for nested cross approximation. (2016)
  8. Riesinger, Christoph; Neckel, Tobias; Rupp, Florian: Solving random ordinary differential equations on GPU clusters using multiple levels of parallelism (2016)
  9. Takahashi, Toru; Shimba, Yuta; Isakari, Hiroshi; Matsumoto, Toshiro: An efficient blocking M2L translation for low-frequency fast multipole method in three dimensions (2016)
  10. Zhang, Peng; Lee, Hyunsuk; Lee, Deokjung: A general solution strategy of modified power method for higher mode solutions (2016)
  11. Bosner, Nela: Efficient algorithm for simultaneous reduction to the $m$-Hessenberg-triangular-triangular form (2015)
  12. Jakovčević Stor, Nevena; Slapničar, Ivan; Barlow, Jesse L.: Accurate eigenvalue decomposition of real symmetric arrowhead matrices and applications (2015)
  13. Lemoine, A.; Caltagirone, J.-P.; Azaïez, M.; Vincent, S.: Discrete Helmholtz-Hodge decomposition on polyhedral meshes using compatible discrete operators (2015)
  14. McBride, A.; Bargmann, S.; Reddy, B.D.: A computational investigation of a model of single-crystal gradient thermoplasticity that accounts for the stored energy of cold work and thermal annealing (2015)
  15. Yavuz, M.M.; Kanber, B.: On the usage of tetrahedral background cells in nodal integration of RPIM for 3D elasto-static problems (2015)
  16. Birk, Matthias; Dapp, Robin; Ruiter, N.V.; Becker, J.: GPU-based iterative transmission reconstruction in 3D ultrasound computer tomography (2014) ioport
  17. Di Napoli, Edoardo; Fabregat-Traver, Diego; Quintana-Ortí, Gregorio; Bientinesi, Paolo: Towards an efficient use of the BLAS library for multilinear tensor contractions (2014)
  18. Duy, Truong Vinh Truong; Ozaki, Taisuke: A decomposition method with minimum communication amount for parallelization of multi-dimensional ffts (2014)
  19. Lessmann, Markus; Würtz, Rolf P.: Learning invariant object recognition from temporal correlation in a hierarchical network (2014) ioport
  20. Li, Shengguo; Gu, Ming; Cheng, Lizhi; Chi, Xuebin; Sun, Meng: An accelerated divide-and-conquer algorithm for the bidiagonal SVD problem (2014)

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