OpenCL Training in Mountain View, CA

Costs for this course can be paid online at the time of registration. To register, please fill out all the fields below.

Clicking Submit will redirect you to our PayPal payment page to purchase the course either by Credit Card or your PayPal account.

* .. proceed to PayPal to pay via
PayPal account or Credit Card.



Acceleware Online Payments accept
Acceleware accepts PayPal, VISA, MasterCard, AMEX
Sponsored by
Acceleware OpenCL Training Sponsored by AMD
Date: June 26-29, 2012
Location: 800 West El Camino Real
Suite 180
Mountain View
California
94040
United States of America
Contact: Acceleware
403.249.9099 x 356, services@acceleware.com
Cost: $3250 USD
Early Bird Offer: Register before Jun 5th, 2012 and receive $200 off the registration fee! (enter discount code: AXTEB2012)
 
Your fee includes:
  • Use of a laptop equipped with AMD Fusion APU
  • Manual of all lectures
  • CD copy of lab exercises
  • OpenCL Quick Reference Card
  • Certificate of Completion
   

Schedule

Tue and Wed: 9:00AM – 5:00PM (incl. 1 hour lunch)
 

Agenda

  • Day 1: Introduction to GPU Programming and GPU Architectures
    • Overview of GPU Computing
    • OpenCLL Software
    • Data-Parallel Architectures and the OpenCL Programming Model
    • The OpenCL Memory Model & Work-item Cooperation
    • Hands-on-Exercises: Buffer Allocation and Buffer Transfers, Simple Kernels and Local and Constant Memory
  • Day 2: Advanced GPU Programming and Debugging
    • Task Concurrency and Synchronization
    • Images and Graphics Interoperability
    • Debugging GPU Programs and Numerical Accuracy
    • Hands-on-Exercises: Asynchronous Operations, Images and Graphics Interoperability and Debugging
  • Day 3: Introduction to Optimizations
    • Latency and the Execution Model
    • Arithmetic Optimizations
    • Memory Optimizations
    • Hands-on-Exercises: Arithmetic Optimizations and Correcting memory access pattern transfers and local memory bank conflicts
  • Day 4: Advanced Optimizations and Profiling
    • Profiling applications
    • Case Study – 3D Convolution or Other Case Study
    • Multiple GPUs, Cluster of GPUs
    • Hands-on-Exercises: Using the Stream Profiler to optimize a kernel and 3D Convolution – Putting it all together

Space is limited - Please register early to guarantee your spot