conditional numerical reproducibility

Webinar: Getting Reproducible Results with Intel® MKL 11.0

Intel(R) MKL 11.0 introduces new functionality allowing users to balance the need for reproducible results with performance. The webinar recording and presentation linked below discusses the mechanisms that cause variability in floating point results, the new controls to limit these, and the performance trade-offs involved.

  • Développeurs
  • Professeurs
  • Étudiants
  • Bibliothèque Intel® Math Kernel Library
  • mkl 11.0
  • cnr
  • MKL CNR
  • conditional numerical reproducibility
  • Conditional Numerical Reproducibility (CNR) in Intel MKL 11.0

    New functionality in Intel MKL 11.0 now allows you to balance performance with reproducible results by allowing greater flexibility in code path choice and by ensuring that algorithms are deterministic. To learn more about Conditional Numerical Reproducibility (CNR) see the following resources:

  • Bibliothèque Intel® Math Kernel Library
  • intel mkl
  • mkl 11.0
  • cnr
  • MKL CNR
  • conditional numerical reproducibility
  • intel math kernel library
  • S’abonner à conditional numerical reproducibility