CUDA/OpenCL Training by Acceleware in Association with Colfax International
Acceleware, in partnership with Colfax now offers customized CUDA/OpenCL training courses. Clients can access top rated training on techniques for parallel programming in CUDA, OpenCL, MPI and many others. Acceleware's customized CUDA/OpenCL training consists of classroom lectures and several practical hands-on exercises. We recommend that the attendees have a background C/C++ (2 or more years) in order to get the most out of the course.
Fixed Dates
Schedule
Acceleware, in partnership with Colfax now offers customized CUDA/OpenCL training courses. Clients can access top rated training on techniques for parallel programming in CUDA, OpenCL, MPI and many others. Acceleware's customized CUDA/OpenCL training consists of classroom lectures and several practical hands-on exercises. We recommend that the attendees have a background C/C++ (2 or more years) in order to get the most out of the course.
Fixed Dates
| Date Options: |
January 17-21, 2011 |
| Location: | Colfax International 750 Palomar Ave Sunnyvale, CA 94085 (view map) |
| Cost: | $4000 USD |
| Space is limited - Please register early to guarantee your spot Click here to register |
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* Classes may be subject to minimum enrollment
Custom Dates
Custom dates available for small focused groups (4-10). Contact us with indicative dates to schedule your training session.
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Chris Mason - Acceleware Product Manager Chris is the product manager for the linear algebra solver product line at Acceleware. He has been responsible for the successful launch of Acceleware products used by companies world-wide. His previous experience includes parallelization of algorithms on digital signal processors (DSPs) for cellular phones and base stations. Chris has a Masters in Electrical Engineering from Stanford University. |
Schedule
- Mon-Thu: 9:00AM – 5:00PM (incl. 1 hour lunch)
- Fri: 9:00AM – 12:00PM
- Day 1:
- Lecture: Overview of GPU Computing
- Hands-on-Exercise: Memory Allocation and Memory Transfers
- Lecture: Data-Parallel Architectures and the GPU Programming Model
- Hands-on-Exercise: Simple Kernels
- Lecture: The GPU Memory Model & Thread Cooperation
- Hands-on-Exercise: Shared Memory and Constant Memory
- Day 2:
- Lecture and Hands-on-Exercise: Textures
- Lecture and Hands-on-Exercise: Asynchronous Operations
- Lecture: Other GPU Features
- Lecture: Libraries
- Lecture: Debugging GPU Programs
- Hands-on-Exercise: Debugging Tools and Techniques
- Day 3:
- Lecture: Introduction to Optimization
- Hands-on-Exercise: Arithmetic Optimization
- Lecture: Resource Management, Latency and Occupancy
- Lecture and Hands-on-Exercise: Memory Performance Optimizations
- Day 4 & 5:
- More Hands-on-Exercises: Building GPU Prototypes and Specific Client Applications
- Running on Servers and Clusters
Download training course detailed outline
All lectures are a combination of teaching and hands-on tutorials
NVIDIA’s foundational training material is augmented with Acceleware’s experience and with examples specific to an HPC audience