Last Updated:
02
/
09
/
2010
GPU Computing Online Seminars
This series will cover the basics of data parallel computing on
GPU's leveraging NVIDIA's CUDA architecture . Tutorials will cover many topics
including C for CUDA, programming to the
OpenCLTMAPI , using DirectCompute and
performance optimization techniques, presented by NVIDIA Developer Technology
Engineering team and NVIDIA staff online to answer Questions.
Click here to view previously-recorded sessions.
NVIDIA's Feburary and March GPU Computing Webinars now open for
registration.
These webinars cover many topics including an introduction to C for CUDA,
the OpenCLTM API, and performance optimization techniques,
presented by NVIDIA DevTech Engineers with additional staff online to answer
questions.
Please follow the links to register for each webinar you would like to
attend. Advance registration is required. Please note all times are in Pacific Time
Register Now Using The Links Below:
GPU Computing using CUDA C – An Introduction, 2 hours
NVIDIA presents an introduction to the basics of GPU computing using NVIDIA
CUDA technology.
Topics covered include:
- Writing a small GPU computing program in C from scratch
- Data transfers
- Executing functions on the GPU
- Taking advantage of on-chip shared memory
- Coordinating CPU and GPU execution
Concepts will be illustrated with step-by-step walkthroughs of code samples,
which can be readily compiled and run. Little or no prior GPU Computing
experience required.
GPU Computing using CUDA C – Advanced 1, 2 hours
NVIDIA presents CUDA performance considerations.
Topics covered include:
- Basic optimization techniques
- GPU Compute HW architecture
- Data transfer considerations
- Data structure considerations
- Device memory optimization
- Memory access coalescing
Concepts will be illustrated using real code examples together with actual
performance gains.
GPU Computing using CUDA C - Advanced
2, 2 hours
NVIDIA presents advanced CUDA optimization tricks and tips
Topics covered include:
- Execution configuration optimization
- Instruction level optimization
- Warp-level optimization
- Multi-GPU usage
- Graphics API Interoperability
Concepts will be illustrated using SDK code samples
GPU Computing using OpenCL- An Introduction, 2 hours
NVIDIA presents an introduction to the OpenCL API leveraging NVIDIA's CUDA
parallel computing architecture
- Data parallel computing Introduction
- OpenCL and the CUDA architecture
- Overview of OpenCL API
- OpenCL memory hierarchy
- Code example walk through
The classes are offered at the following times:
GPU Computing using OpenCL Advanced 1, 2
hours
NVIDIA presents tricks and tips on how to write great OpenCL code.
- OpenCL and CUDA architecture
- Memory mapping and performance considerations
- Performance measurement
- Memory usage best practices and optimization
- Achieving best processor occupancy
- Instruction throughput considerations
The classes are offered at the following times:
Previously-recorded Sessions
Introduction to CUDA
Optimizing CUDA
Further Optimizing CUDA
OpenCL Introduction
Best Practices for OpenCL Programming
OpenCL is trademark of Apple Inc. used under license
to the Khronos Group Inc.
