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 149 articles )

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  1. Chehrazad, Sahar; Roose, Dirk; Wauters, Tony: A fast and scalable bottom-left-fill algorithm to solve nesting problems using a semi-discrete representation (2022)
  2. Eslaminia, Mehran; Elmeliegy, Abdelrahman M.; Guddati, Murthy N.: Full waveform inversion through double-sweeping solver (2022)
  3. Hedayat, Mohammadali; Akbarzadeh, Amir M.; Borazjani, Iman: A parallel dynamic overset grid framework for immersed boundary methods (2022)
  4. Kang, Seung-Hoon; Kim, Yongse; Cho, Haeseong; Shin, SangJoon: Improved hyper-reduction approach for the forced vibration analysis of rotating components (2022)
  5. Röhrig-Zöllner, Melven; Thies, Jonas; Basermann, Achim: Performance of the low-rank TT-SVD for large dense tensors on modern multicore CPUs (2022)
  6. Torun, Tugba; Torun, F. Sukru; Manguoglu, Murat; Aykanat, Cevdet: Partitioning and reordering for spike-based distributed-memory parallel Gauss-Seidel (2022)
  7. Barthels, Henrik; Psarras, Christos; Bientinesi, Paolo: Linnea. Automatic generation of efficient linear algebra programs (2021)
  8. Curtin, Ryan R.; Edel, Marcus; Prabhu, Rahul Ganesh; Basak, Suryoday; Lou, Zhihao; Sanderson, Conrad: The ensmallen library for flexible numerical optimization (2021)
  9. Duan, Wenyang; Meng, Feiteng; Chen, Jikang: PTEBEM for wave drift forces based on hydrodynamic pressure integration (2021)
  10. Garstka, Michael; Cannon, Mark; Goulart, Paul: COSMO: a conic operator splitting method for convex conic problems (2021)
  11. Gradusov, Vitaly A.; Roudnev, Vladimir A.; Yarevsky, Evgeny A.; Yakovlev, Sergey L.: Solving the Faddeev-Merkuriev equations in total orbital momentum representation via spline collocation and tensor product preconditioning (2021)
  12. Hédin, Florent; Pichot, Géraldine; Ern, Alexandre: A hybrid high-order method for flow simulations in discrete fracture networks (2021)
  13. Hrga, Timotej; Lužar, Borut; Povh, Janez; Wiegele, Angelika: BiqBin: moving boundaries for NP-hard problems by HPC (2021)
  14. Huo, Zenan; Mei, Gang; Xu, Nengxiong: JuSFEM: a Julia-based open-source package of parallel smoothed finite element method (S-FEM) for elastic problems (2021)
  15. Jason Rumengan, Terry Yue Zhuo, Conrad Sanderson: PyArmadillo: a streamlined linear algebra library for Python (2021) arXiv
  16. Kačala, Viliam; Török, Csaba: Speedup of tridiagonal system solvers (2021)
  17. Lange, Nils; Hütter, Geralf; Kiefer, Björn: An efficient monolithic solution scheme for FE(^2) problems (2021)
  18. Lee, Chaemin; Kim, San; Lee, Phill-Seung: The strain-smoothed 4-node quadrilateral finite element (2021)
  19. Ramachandran, Prabhu; Bhosale, Aditya; Puri, Kunal; Negi, Pawan; Muta, Abhinav; Dinesh, A.; Menon, Dileep; Govind, Rahul; Sanka, Suraj; Sebastian, Amal S.; Sen, Ananyo; Kaushik, Rohan; Kumar, Anshuman; Kurapati, Vikas; Patil, Mrinalgouda; Tavker, Deep; Pandey, Pankaj; Kaushik, Chandrashekhar; Dutt, Arkopal; Agarwal, Arpit: PySPH: a Python-based framework for smoothed particle hydrodynamics (2021)
  20. Steel, Thijs; Camps, Daan; Meerbergen, Karl; Vandebril, Raf: A multishift, multipole rational QZ method with aggressive early deflation (2021)

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