Is Python almost as fast as Native Code? Believe it!

Join us to see Intel® Distribution for Python in action, taking an application to the next level of performance, using native libraries, performance analysis, and optimization of Python/C/C++ code.
You’ll learn:

  • How to get the best of both worlds: C-like performance within the productivity of Python
  • The 3 steps to faster Python applications
  • Before and after performance improvements on an application, including how it was done
Nathan Greeneltch, PhD, Data Scientist and Machine Learning Engineer, Intel Corporation

Nathan joined the Intel Technical Computing, Analyzers and Runtimes group in 2017 as a technical consulting engineer. His role is to help drive customer engagements for Python* as well as Intel® Performance Libraries, leveraging the synergies between Python and the Intel® Math Kernel Library (Intel® MKL). Before joining the TCAR team, Nathan spent 3 years in the processor development side of Intel where he was a ML practitioner in the defects division, identifying and predicting failure areas in the coming generations of Intel® processors. Nathan has a PhD in physical chemistry from Northwestern University, where he worked on nanoscale lithography of metal wave-guides for amplification of laser-initiated vibrational signal in small molecules.

For more complete information about compiler optimizations, see our Optimization Notice.