Learn more about GPU computing and CUDA-enabled applications through these webinars

Upcoming Webinars

This series will cover the basics of data parallel computing on GPUs leveraging NVIDIA’s CUDA architecture. Tutorials will cover many topics including C for CUDA, programming to the OpenCL API, using Direct X Compute and performance optimization techniques, presented by NVIDIA Developer Technology Engineering team and NVIDIA staff online to answer questions.

All dates and time reference are for California, USA. Please follow the links to register for the webinars. The webinar system will send emails confirming and also reminding you of your registration.

• An Introduction to GPU Computing and CUDA Architecture (1.5 hours)
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NVIDIA presents an introduction to the basics of GPU computing using the NVIDIA CUDA technology. Topics 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

• Performance Considerations for CUDA Programming - Advanced 1(1 hour)
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NVIDIA presents CUDA optimization considerations. Topics include:
• Basic optimization techniques
• GPU Compute HW architecture
• Data Transfer considerations
• Data Structure considerations
• Device Memory optimization
• Memory access coalescing

• Further CUDA Optimization Techniques – Advanced 2 (1 hour)
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NVIDIA presents advanced CUDA optimization tricks and tips. Topics include:
• Execution configuration optimization
• Instruction level optimization
• Warp-level optimization
• Multi-GPU usage
• Graphics API Interoperability

• An Introduction to GPU Computing and OpenCL (1.5 hours)
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NVIDIA presents an introduction to the OpenCL API leveraging NVIDIA’s CUDA parallel computing architecture. Topics include:
• Data parallel computing Introduction
• OpenCL and the CUDA architecture
• Overview of OpenCL API
• OpenCL Memory Hierarchy
• Code example walk through

• Best Practices for OpenCL programming - Advanced (1.5 hours)
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NVIDIA presents tricks and tips on how to write great OpenCL code. Topics include:
• OpenCL and CUDA architecture
• Memory mapping and performance considerations
• Performance measurement
• Memory usage best practices and optimization
• Achieving Best Processor occupancy
• Instruction throughput considerations

Archived Webinars

• Run MATLAB 10x-50x Faster using GPUs and Jacket Plug-in
  June 17, 2009

Find out how the Jacket CUDA-plugin for NVIDIA’s GPUs accelerates MATLAB by 10x-50x for a range of applications like imaging, signal processing, life sciences, and bio-informatics.

• GPU Computing Tutorial Webinar Series
  This series will cover the basics of data parallel computing on GPUs leveraging NVIDIA’s CUDA architecture. Tutorials will include programming with C for CUDA, OpenCL API, Direct X Compute and performance optimization techniques. All dates and time reference are for California, USA. Please follow the links to register for the webinars.
  
  Archives of previous webinars are available here.

• Jacket: MATLAB Acceleration for Life Science Applications using CUDA-enabled GPUs
  April 16, 2009


Attend this webinar to learn about:
• The revolutionary high-performance CUDA architecture of NVIDIA GPUs
• Accelerating MATLAB using the Jacket engine from AccelerEyes
• Using Jacket to get 10x to 50x speed-up for several MATLAB life science applications, including genomics, medical image processing, and drug delivery modeling

• Cray CX1 Personal Supercomputer with NVIDIA Tesla GPU Computing
  (Download PDF of presentation)
  April 20, 2009

This Webinar will provide an overview of the new Cray CX1 personal supercomputer, and how coupled with NVIDIA, provides processing, visualization, and acceleration advancements in computing; as well as information about the CUDA parallel programming model. Space is limited.

• Jacket: MATLAB Acceleration for Image Processing using CUDA-enabled GPUs, March 10, 2009
• The revolutionary high-performance CUDA architecture of NVIDIA GPUs
• Accelerating MATLAB using the Jacket engine from AccelerEyes
• Using Jacket to get 10x to 50x speed-up for several MATLAB image processing functions

• Accelerating Applications using CUDA and GPU Computing, March 4, 2009
• Learn about GPU computing, how to program the GPU using C, and about Penguin’s Tesla-based pre-configured clusters, in addition to the use of GPUs in bio-informatics, signal processing, and quantum chemistry.
• Hosted by Penguin and NVIDIA

• Jacket: Accelerating MATLAB using CUDA-enabled GPUs, February 5, 2009
• Learn about the Jacket plugin that accelerates MATLAB on NVIDIA’s Tesla GPUs
• Hosted by AccelerEyes and NVIDIA

• Microway’s NVIDIA Tesla Solutions for high-performance computing, January 22, 2009
• Learn about GPU computing and how applications like molecular dynamics, quantum chemistry, computation fluid dynamics (CFD), electromagnetics, weather and ocean modeling, and computation finance get accelerated using CUDA-enabled GPUs.
• Also, we discuss the CUDA architecture and the support of OpenCL on the CUDA architecture.
• Hosted by Microway and NVIDIA,

• AMAX’s NVIDIA Tesla High-Performance Computing Solutions, December 18, 2008
• Learn about the Tesla data center and workstation products
• And the performance benefit of a CUDA-enabled GPU on FFT, BLAS, and Sparse Matrix calculations and a range of applications.
• Hosted by AMAX and NVIDIA

• Unleash your GPU’s Computing Power, October 22, 2008
• Learn what is GPU computing, what is the CUDA programming model, and how you can benefit from the Tesla Personal Supercomputer to improve the performance of your applications.
• Hosted by JRTI and NVIDIA