MKL CNR

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.

  • Developers
  • Professors
  • Students
  • 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:

  • Intel® Math Kernel Library
  • intel mkl
  • mkl 11.0
  • cnr
  • MKL CNR
  • conditional numerical reproducibility
  • intel math kernel library
  • Introduction to Conditional Numerical Reproducibility (CNR)

    Starting with 11.0 release,  Intel® MKL introduces a feature called Conditional Numerical Reproducibility (CNR) which provides functions for obtaining reproducible floating-point results when calling library functions from their application.  When using these new features, Intel MKL functions are designed to return the same floating-point results from run-to-run, subject to the following limitations:

  • Developers
  • Intermediate
  • Intel® Math Kernel Library
  • mkl 11.0
  • cnr
  • MKL CNR
  • MKL reproducible results
  • consistent results in MKL
  • MKL Conditional Numerical Reproducibility
  • Development Tools
  • Subscribe to MKL CNR