Module 1: Aggregations, universal functions, and broadcasting

Bob Chesebrough and Praveen Kundurthy, Intel
ALP Graphic Module 1

This is the first module in the newest Aurora Learning Paths series - Accelerate Python Loops with the Intel AI Analytics Toolkit. Ignite performance of common Python & pandas constructs by using NumPy powered by oneAPI. This webinar and workshop series will go into detail about how to apply key Intel architectural innovations and libraries via the smart application of NumPy techniques to achieve amazing performance gains.  We’ll delve into NumPy: aggregations, universal functions, broadcasting and fancy slicing, sorting, and other techniques powered by oneAPI. Learn how to achieve performance gains by replacing Python loop centric or list comprehension applications with smarter equivalents that are more maintainable, more efficient, and much faster on current and future innovations in Intel hardware and oneAPI software libraries!

 Learning Outcomes:

  • Apply NumPy constructs as a Python loop replacement strategy that improves readability, maintainability, performs fasts on current hardware and readies code for future HW & SW accelerations that Intel builds into silicon and which are exposed via NumPy
  • Apply NumPy aggregations, reductions, broadcasting, and sorting to significantly accelerate your Python code
  • Understand the value of NumPy to accelerate Python loops