The Advanced Programming of High-Performance GPU Architectures training course provides experienced students with advanced knowledge and hands-on experience in developing and analyzing high performance applications software for processors with massively parallel computing resources (graphics processing units and multicore processors). By end of the training, participants will: understand algorithm styles that are suitable for accelerators, understand the most important architectural performance considerations to developing applications, be exposed to computational thinking skills for accelerating applications in science and engineering and gain the ability to engage computing accelerators on science and engineering breakthroughs.
The DevelopIntelligence remote lab environment utilizes Nvidia hardware (Nvidia GTX480 and Tesla C2070) to illustrate CUDA/OpenCL concepts and to allow training participants to
experimentally investigate performance issues, debugging techniques, and code examples.
Course Summary
Workshops are instructor-led lab-intensives focused on the practical application of technologies through the facilitation of a project-related lab. Workshops are just the opposite of Seminars. They deliver the highest level of knowledge transfer of any format. Think wide (breadth) and deep (depth).
What You'll Learn
In the Advanced Programming of High-Performance GPU Architectures training course you’ll learn:
- Synchronization
- Heterogeneous Parallel Programming
- OpenCL Programming Model
- CUDA Programming Model
- CUDA Application Case Study Code Examples
- NVIDIA Product/Processor Overview
- CUDA Optimization Techniques
- GPU Optimization
- Trends in GPU Architectures
Meet Your Instructor
Dan ConnorsMeet Dan Connors »Dr. Dan Connors is a veteran of the high performance microprocessor and scientific computing field. He received his Ph.D. in Computer Engineering from the University of Illinois at Urbana-Champaign in the year 2000. As a professor at the University of Colorado in the Department of Computer Science and Electrical Engineering, Dr. Connors investigates parallel programming models, compiler optimization, fault tolerance, and design of multicore architectures.
For his commitment to teaching, Dr. Connors was...
