Thursday, August 3, 2017
9:30 am - 4:00 pm
University of Chicago - John Crerar Library
5730 S Ellis Ave, Chicago, IL, 60637, Kathleen A. Zar Room 140
High-Performance Computing and Data Analytics: Programming Intel Architecture with C/C++ and Python
Intel and Colfax International are offering an updated and expanded hands-on training on code modernization for researchers and engineers in computational disciplines. This training provides the foundation needed to extract more of the parallel compute performance potential found in both Intel® Xeon® and Intel® Xeon Phi™ processors and coprocessors. The course materials and practical exercises are appropriate for developers beginning their journey to parallel programming, with enough detail to also cater to high-performance computing experts.
The training is conducted by Colfax International.
Registration is open to everyone free of charge thanks to Intel’s sponsorship.
Colfax Developer Training is far more than a lecture – it is an experiential learning program. That is because the training contains hands-on component in two forms:
- The instructor will demonstrate the methods taught in the course live, on servers with the latest Intel Xeon and Intel Xeon Phi processors.
- Attendees will receive remote access to these training servers for 1 day and a set of programming and optimization exercises.
Bring your own laptop to take advantage of this opportunity.
Colfax-Chicago-08-17-Slides.pdf (12 MB) — this file is available only to registered users. Register or Log In.
Registration, light breakfast (9:30 – 10:00 am)
Morning session (10:00 am – 1:00 pm)
- Sneak Peak: What will be covered today (30 min)
- Programming and Optimization by Example (2.5 hours)
- Demonstration of a case study: direct N-body simulation
- Intel processor architectures
- Task and data parallelism
- Memory organization
- Programming coprocessors and clusters
Lunch (1:00 pm – 1:30 pm)
Afternoon session (1:30 pm – 4:00 pm)
- Optimization Pointers (1 hour)
- Scalar tuning and using Intel compilers
- Automatic vectorization
- Multi-threading with OpenMP
- Optimizing cache usage and memory access
- Communication control
- Preparing for Intel Xeon Phi processors (30 min)
- Compiling with AVX-512
- Using high-bandwidth memory
- Leveraging clustering modes
- Intel Libraries (20 min)
- Intel Math Kernel Library (MKL): numerical methods
- Intel Data Analytics Acceleration Library (DAAL): machine learning
- Intel Distribution for Python (20 min)
- Brief intro to Intel Python (where to get it, installation, etc.)
- Discussing numpy, scipy and link with Intel MKL
- How to get the most out of numpy and scipy
- Intel-Optimized Deep Learning Frameworks (20 min)
- Deep learning frameworks in data analytics
- How to obtain Intel-optimized frameworks
- Deep neural networks on Intel Architecture in action
You must be logged in to register for this event.
This event is fully booked.