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 117 articles )

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  1. Hrga, Timotej; Lužar, Borut; Povh, Janez; Wiegele, Angelika: BiqBin: moving boundaries for NP-hard problems by HPC (2021)
  2. Kačala, Viliam; Török, Csaba: Speedup of tridiagonal system solvers (2021)
  3. Ahrens, Peter; Demmel, James; Nguyen, Hong Diep: Algorithms for efficient reproducible floating point summation (2020)
  4. Carcenac, Manuel; Redif, Soydan: Application of the sequential matrix diagonalization algorithm to high-dimensional functional MRI data (2020)
  5. Cinal, M.: Highly accurate numerical solution of Hartree-Fock equation with pseudospectral method for closed-shell atoms (2020)
  6. Çuğu, İlke; Manguoğlu, Murat: A parallel multithreaded sparse triangular linear system solver (2020)
  7. Frison, Gianluca; Sartor, Tommaso; Zanelli, Andrea; Diehl, Moritz: The BLAS API of BLASFEO: optimizing performance for small matrices (2020)
  8. Il’in, V. P.; Kazantcev, G. Y.: Iterative solution of saddle-point systems of linear equations (2020)
  9. Katharina Boguslawski, Aleksandra Leszczyk, Artur Nowak, Filip Brzęk, Piotr Szymon Żuchowski, Dariusz Kędziera, Paweł Tecmer: Pythonic Black-box Electronic Structure Tool (PyBEST). An open-source Python platform for electronic structure calculations at the interface between chemistry and physics (2020) arXiv
  10. Phalippou, P.; Bouabdallah, S.; Breitkopf, P.; Villon, P.; Zarroug, M.: `On-the-fly’ snapshots selection for proper orthogonal decomposition with application to nonlinear dynamics (2020)
  11. Sashikumaar Ganesan, Manan Shah: SParSH-AMG: A library for hybrid CPU-GPU algebraic multigrid and preconditioned iterative methods (2020) arXiv
  12. Van Zee, Field G.: Implementing high-performance complex matrix multiplication via the 1M method (2020)
  13. Bylina, Beata; Bylina, Jarosław: The parallel tiled WZ factorization algorithm for multicore architectures (2019)
  14. Cho, Haeseong; Gong, DuHyun; Lee, Namhun; Shin, SangJoon; Lee, Seungsoo: Combined co-rotational beam/shell elements for fluid-structure interaction analysis of insect-like flapping wing (2019)
  15. Gebhardt, Cristian Guillermo; Hofmeister, Benedikt; Hente, Christian; Rolfes, Raimund: Nonlinear dynamics of slender structures: a new object-oriented framework (2019)
  16. Grigori, Laura; Tissot, Olivier: Scalable linear solvers based on enlarged Krylov subspaces with dynamic reduction of search directions (2019)
  17. Kopaničáková, Alena; Krause, Rolf; Tamstorf, Rasmus: Subdivision-based nonlinear multiscale cloth simulation (2019)
  18. Matthieu Ancellin; Frédéric Dias: Capytaine: a Python-based linear potential flow solver (2019) not zbMATH
  19. von Wahl, Henry; Richter, Thomas; Lehrenfeld, Christoph; Heiland, Jan; Minakowski, Piotr: Numerical benchmarking of fluid-rigid body interactions (2019)
  20. Dongarra, Jack; Gates, Mark; Haidar, Azzam; Kurzak, Jakub; Luszczek, Piotr; Tomov, Stanimire; Yamazaki, Ichitaro: The singular value decomposition: anatomy of optimizing an algorithm for extreme scale (2018)

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