Stanford HPC Summer Speaker Series: Building Faster Machine Learning Applications with Intel Performance Libraries – July 26@noon

Date for Tutorial: Tuesday, 26 July, 2016
Time: Noon
Duration: 1.5 hours
Location: d.school (Peterson Engineering Laboratory, 550 Panama Mall, Room 200)
 
Title: Building Faster Machine Learning Applications with Intel Performance Libraries

Presentation available here

Abstract:
The future of many industries, as well as many aspects of our lives, is being shaped by machine learning and related technologies. Intel software technologies are being used to enable solutions in these areas. This talk focuses on two Intel performance libraries, MKL and DAAL, which offer optimized building blocks for data analytics and machine learning algorithms.MKL is a collection of routines for linear algebra, FFT, vector math and statistics. It’s being used to speed up math processing in almost every kind of technical computing applications. DAAL is more focused on data applications and provides higher level, canned solutions for supervised and unsupervised learning. This session is an overview of the capability and performance advantages of these libraries in the context of machine learning and deep learning.

Speaker Bios:
Zhang Zhang is a Technical Consulting Engineer with the Software and Services Group at Intel. He provides technical support for Intel performance libraries, including MKL, DAAL, and IPP. He helps customers to adopt Intel software tools and enjoys troubleshooting performance and usage problems in user’s code. Zhang came from a background of high performance and parallel programming, cluster and distributed computing, and performance modeling and analysis. Zhang holds a Ph.D. in Computer Science from Michigan Technological University.

Shaojuan Zhu is a Technical Consulting Engineer at Intel supporting Intel performance libraries: DAAL, IPP and MKL. She has ten years of experience developing and supporting media products. Her expertise and interests include biologically inspired intelligent signal processing, machine learning and media. She holds a Ph.D. in Electrical and Computer Engineering from Oregon Health and Science University.