AMD is offering a series OpenCL webinars to discuss and answer questions about data parallel computing on GPUs leveraging the OpenCL™ architecture. These webinars will include beginning and advanced tracks offered at varying times for your convenience.
Yes is the short answer. An informative run down on where OpenCL is in the all important buzz word of the day: Cloud-computing.
The new Bullet 2.77 Physics SDK features OpenCL cloth simulation, contributed by AMD under the permissive ZLib license. The OpenCL implementation has been tested on AMD and NVIDIA GPUs for Windows and Linux as well as the Apple OpenCL implementation for GPUs on Mac OSX Snow Leopard. The full source code and precompiled Windows executable demos are available for download.
Enj appears to be enjoying the GTC 2010 Conference this week. He brings us an inside view of the conference, and a feel of the different talks on OpenCL and CUDA. If you have 5 minutes, pop over to enja.org, it'll be worth your time.
At the GPU Technology Conference in San Jose on Monday, September 20th, join Mark Kilgard for a pre-conference tutorial on NVIDIA's OpenGL support. Mark will discuss and demonstrate features of OpenGL 4.1 including programmable tessellation.
Acceleware Corp announced their fall 2010 comprehensive CUDA/OpenCL training schedule co-sponsored by Microsoft Corp. Acceleware's training classes are designed to support the HPC communities GPU programmers using CUDA and OpenCL, along with instruction on Microsoft's HPC Server 2008 cluster operating systems. There are currently five course schedule between mid-October and mid-December, with each course lasting 5 days. You can find an overview of the courses on our Khronos Group events page. Complete details are available on the Acceleware training website.
AMD Developer Central has a new section that offers OpenCL Code Samples. Although only one sample right now, this looks like a promising page for those getting started with OpenCL. The Khronos Group also has a section with numerous OpenCL samples and tutorials on their site.
OpenCL framework to accelerate an EMRI modeling application using the hardware accelerators – Cell BE and Tesla CUDA GPU. The main goal of this work is to evaluate an emerging computational platform, OpenCL, for scientific computation. Results show OpenCL binary on a par with CUDA SDK. Baseline is an AMD Phenom 2.5Ghz CPU.
In this episode of Adventures in OpenCL tutorials, we cover OpenCL context sharing with OpenGL. We make a simple particle system to demonstrate this feature. One of the most important aspects of this feature is the time we can save by doing rendering and calculations on the same memory in the GPU, this means we don’t need to copy data back and forth!
AMD Developer central just released an OpenCL optimization case study on SImple Reductions. Strategies examined for efficiently mapping reductions onto the ATI Radeon™ HD 5870 GPU and AMD Phenom™ II X4 965 CPU. Taking advantage of properties of the reduction being performed, as well as matching the style of reduction to the hardware platform, can result in performance improvements of up to 15x, compared to naive code.