Get Your Code Future-Ready with FREE Technical Webinars

Sharpen your technical skills, get expert answers to specific questions, or dive into an entirely new area of development with webinars designed to deliver the goods.

Sign up today to attend LIVE SESSIONS covering the latest overviews, insights, and how-to’s on topics that drive our cross-architecture, heterogeneous-compute world—oneAPI, AI, HPC, rendering & ray tracing, video & media, IoT & embedded, and more.

Miss a session? No problem. All past sessions can be viewed and shared on-demand for your convenience. (All webinars are held on Wednesdays, with on-demand versions available on the following Mondays.)

Wednesday, November 3, 2021 9:00 am PT
#oneAPI

Drive Innovation and Performance into Your Python Scikit-learn ML Tasks

Python’s stock scikit-learn is an efficient machine learning library for predictive data analysis, but it’s slow on today’s powerful hardware. Find out how the software optimizations inherent in the Intel® Extension for Scikit-learn* delivers 2X better performance with just two lines of code.

Scikit-learn has many real-world applications as a general-purpose machine learning library for classification, regression, and clustering algorithms—e.g., support-vector machines (SVMs), random decision forests, gradient boosting, K-means clustering, and the data-clustering algorithm DBSCAN.

Unfortunately, Python’s stock scikit-learn library is inherently slow on hardware without additional software optimizations.

Enter the Intel® Extension for Scikit-learn, part of the Intel® oneAPI AI Analytics Toolkit. In this webinar, AI Technical Consulting Engineer Rachel Oberman will discuss the Intel extension, including:

  • How it can speed up scikit-learn in just two lines of code, delivering at least 2X better performance
  • How to improve scikit-learn memory access
  • An overview of running getting-started steps, plus a census use case

Sign up.

Download the software
Get the Intel® Extension for Scikit-learn as part of the Intel® oneAPI AI Analytics Toolkit —a specialized set of tools and frameworks to accelerate end-to-end data science pipelines.

  • Want Intel’s Scikit-learn Extension as a standalone? Get it here

Resources

  • Sign up for an Intel® DevCloud for oneAPI account—a free development sandbox with access to the latest Intel® hardware and oneAPI software.
  • Explore oneAPI, including developer opportunities and benefits
  • Subscribe to the POD—Code Together is an interview series that explores the challenges at the forefront of cross-architecture development. Each bi-weekly episode features industry VIPs who are blazing new trails through today’s data-centric world. Available wherever you get your podcasts.

 

 

Rachel Oberman, AI Technical Consulting Engineer, Intel Corporation

Rachel is an AI Technical Consulting Engineer who helps customers optimize their workflows with data analytics and machine learning algorithms from Intel. Prior to joining Intel in 2019, she focused on geospatial analysis and data science, and founded geoLab—an undergraduate research lab, serving as its Director. Rachel holds a bachelor’s degree in Computer Science and Data Science from the College of William & Mary in Virginia.

 

Wednesday, November 10, 2021 9:00 am PT
#oneAPI

Implementing the Fourier Correlation Algorithm in Just a Few Lines of DPC++ and oneMKL

Learn streamlined methods for implementing a 1D Fourier correlation to perform complex mathematical operations requiring multiple kernel functions.

Fourier correlation has many applications such as measuring the similarity of two signals, finding the best translation to overlay similar images, and volumetric medical image segmentation.

The Fourier correlation algorithm can be composed from Intel® oneAPI Math Kernel Library (oneMKL) functions for easy offload to a variety of accelerators such as GPUs and FPGAs.

In this session, Senior Principal Software Engineer Henry Gabb will:

  • Cover the basics of DPC++ queues; buffers, USM, and data movement; and implicit vs. explicit synchronization
  • Walk through buffered and USM implementations of a 1D Fourier correlation
  • Discuss the movement of data between host and device and ways to avoid unnecessary data movement

† correlation=MAXLOC(IDFT(signal1)*CONJG(DFT(SIGNAL2)))), where DFT is the discrete Fourier transform, IDFT is the inverse DFT, CONJG is the complex conjugate, and MAXLOC is the location of the maximum correlation score

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Download the software

Get DPC++ and oneMKL as part of the Intel® oneAPI Base Toolkit —a foundational set of tools and libraries for developing high-performance, data-centric applications across diverse architectures.

Resources

  • Sign up for an Intel® DevCloud for oneAPI account—a free development sandbox with access to the latest Intel® hardware and oneAPI software.
  • Explore oneAPI, including developer opportunities and benefits
  • Subscribe to the POD—Code Together is an interview series that explores the challenges at the forefront of cross-architecture development. Each bi-weekly episode features industry VIPs who are blazing new trails through today’s data-centric world. Available wherever you get your podcasts.

 

 

 

Henry Gabb, Senior Principal Software Engineer, Intel Corporation

Henry Gabb is a senior principal engineer in the Intel Architecture, Graphics, and Software Group. He first joined Intel in 2000 to help drive parallel computing inside and outside the company. Prior to joining Intel, Henry was Director of Scientific Computing at the U.S. Army Engineer Research and Development Center MSRC, a Department of Defense high-performance computing facility. Henry holds a BS in biochemistry from Louisiana State University, an MS in medical informatics from the Northwestern Feinberg School of Medicine, a PhD in molecular genetics from the University of Alabama at Birmingham School of Medicine, and a PhD in information science from the University of Illinois at Urbana-Champaign. He has published extensively in computational life science and high-performance computing. Henry is also the editor of The Parallel Universe, Intel’s quarterly magazine for software innovation.

Wednesday, November 17, 2021 9:00 am PT
#oneAPI

High-Performance GPU Acceleration – Part 2: Tuning for Offload Performance

Have software ready for pre-release GPU offload? Attend this session and learn how to tune it for optimal performance once the hardware is available using the performance-analysis workhorse, Intel® VTune™ Profiler.

Developers who deploy applications across both CPUs to GPUs are often challenged to find the best methods for analyzing and optimizing offload performance.

In Part 2 of this webinar series, Technical Consulting Engineer Kevin O’Leary will focus on tuning software for optimal performance once hardware is available. The tool: Intel® VTune™ Profiler, a performance analyzer that takes the guesswork out of cross-architecture improvements.

Using a sample application written in DPC++, Kevin will demonstrate how VTune can …

  • Profile DPC++, OpenMP offload, and code running on both host and GPU processors
  • Collect the right data and turn it into rich, easily interpretable analysis
  • Identify the hotspots in your compute kernels, including which are key areas for optimization
  • Show how the GPU resources are being utilized and locate hardware bottlenecks

Sign up.

Download the software
Get Intel® VTune™ Profiler as part of the Intel® oneAPI Base Toolkit —a foundational set of tools and libraries for developing high-performance, data-centric applications across diverse architectures.

Resources

  • Sign up for an Intel® DevCloud for oneAPI account—a free development sandbox with access to the latest Intel® hardware and oneAPI software.
  • Explore oneAPI, including developer opportunities and benefits
  • Subscribe to the POD—Code Together is an interview series that explores the challenges at the forefront of cross-architecture development. Each bi-weekly episode features industry VIPs who are blazing new trails through today’s data-centric world. Available wherever you get your podcasts.
Kevin O’Leary, Lead Technical Consulting Engineer, Intel Corporation

Kevin O’Leary is a Senior Software Developer and Lead Technical Consulting Engineer whose expertise includes compilers, debuggers, and software performance tools. He’s currently responsible for performance optimization using Intel® Advisor and Intel® VTune™ Profiler and was a key developer of the Intel® Parallel Studio XE development suite. Prior to joining Intel, Kevin spent many years as a debugger engineer for IBM/Rational Software.

Kevin holds a Bachelor’s in Computer Science from University of Massachusetts and a Master’s in Computer Science from Oregon Health and Science University.

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