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.
Keywords for this software
References in zbMATH (referenced in 41 articles )
Showing results 1 to 20 of 41.
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- Michailidis, Panagiotis D.; Margaritis, Konstantinos G.: Scientific computations on multi-core systems using different programming frameworks (2016)
- Mikhalev, A.Yu.; Oseledets, I.V.: Iterative representing set selection for nested cross approximation. (2016)
- Riesinger, Christoph; Neckel, Tobias; Rupp, Florian: Solving random ordinary differential equations on GPU clusters using multiple levels of parallelism (2016)
- Bosner, Nela: Efficient algorithm for simultaneous reduction to the $m$-Hessenberg-triangular-triangular form (2015)
- Jakovčević Stor, Nevena; Slapničar, Ivan; Barlow, Jesse L.: Accurate eigenvalue decomposition of real symmetric arrowhead matrices and applications (2015)
- Lemoine, A.; Caltagirone, J.-P.; Azaïez, M.; Vincent, S.: Discrete Helmholtz-Hodge decomposition on polyhedral meshes using compatible discrete operators (2015)
- 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)
- Birk, Matthias; Dapp, Robin; Ruiter, N.V.; Becker, J.: GPU-based iterative transmission reconstruction in 3D ultrasound computer tomography (2014)
- Di Napoli, Edoardo; Fabregat-Traver, Diego; Quintana-Ortí, Gregorio; Bientinesi, Paolo: Towards an efficient use of the BLAS library for multilinear tensor contractions (2014)
- Duy, Truong Vinh Truong; Ozaki, Taisuke: A decomposition method with minimum communication amount for parallelization of multi-dimensional ffts (2014)
- Lessmann, Markus; Würtz, Rolf P.: Learning invariant object recognition from temporal correlation in a hierarchical network (2014)
- Li, Shengguo; Gu, Ming; Cheng, Lizhi; Chi, Xuebin; Sun, Meng: An accelerated divide-and-conquer algorithm for the bidiagonal SVD problem (2014)
- Sarra, Scott A.: Regularized symmetric positive definite matrix factorizations for linear systems arising from RBF interpolation and differentiation (2014)
- Talebi, Hossein; Silani, Mohammad; Bordas, Stéphane P.A.; Kerfriden, Pierre; Rabczuk, Timon: A computational library for multiscale modeling of material failure (2014)
- Dahlberg, Carl F.O.; Faleskog, Jonas: An improved strain gradient plasticity formulation with energetic interfaces: theory and a fully implicit finite element formulation (2013)
- Ltaief, Hatem; Luszczek, Piotr; Dongarra, Jack: High-performance bidiagonal reduction using tile algorithms on homogeneous multicore architectures (2013)
- Sanchez, Luis Miguel; Fernandez, Javier; Sotomayor, Rafael; Escolar, Soledad; Garcia, J.Daniel: A comparative study and evaluation of parallel programming models for shared-memory parallel architectures (2013)