Intel: Accelerating Python across the range of applications: the right tools for the job | Sponsor tutorials
Python's popularity has given way to its use in many areas--from web frameworks all the way to machine learning and scientific computing. However, getting the best performance from Python requires an intimate knowledge of the right tools and techniques that are available today. In this tutorial, participants will learn how to measure, tune and accelerate Python workflows across various domains. This tutorial will cover the following topics: -Performance speedups for scientific computing using Intel® Distribution for Python, multithreading with Intel® Threading Building Blocks library, Numba, and Intel® VTune Amplifier -Data Analytics and machine learning acceleration with pyDAAL -Web framework, scripting, and infrastructure acceleration using the PyPy JIT
David Liu
David is a Technical Consultant Engineer at Intel Corporation in Austin, TX, where he represents Intel's Python products and projects. He is focused on solving customer problems in Python while simultaneously developing and shaping Intel's software products to match customer needs. In the past, he worked as a software engineer utilizing Python in machine learning, network infrastructure, and web work. David holds an MS in Software Engineering from the University of Texas at Austin.
Room B110-111
Thursday, 18th May, 09:00 - 10:30
