Larrabee, the future computing platform for hardware accelerated programming was planned to be released as a kind of graphics card. However, due to delays in the development of the product Intel decided to change plans and not to release a Larrabee graphic card. Read the rest of this entry »
Yesterday, NVIDIA announced, to release an OpenCL (Open Computing Language) driver and software development kit (SDK) to developers participating in its OpenCL Early Access Program. NVIDIA is providing this release to solicit early feedback in advance of a beta release which will be made available to all GPU Computing Registered Developers in the coming months.
OpenCL is being created by the Khronos Group with the participation of many industry-leading companies and institutions including AMD, Apple, Broadcom, IBM, Intel, NVIDIA and many more. OpenCL aims to be the first royalty-free standard for general-purpose parallel programming of heterogeneous systems. It provides a uniform programming environment for software developers to write efficient, portable code for high-performance compute servers, desktop computer systems and handheld devices using a diverse mix of multi-core CPUs, GPUs, Cell-type architectures and other parallel processors such as DSPs.
Developers can apply to become a GPU Computing Registered Developer at: www.nvidia.com/opencl. I registered for it. So look forward to some interesting OpenCL code in a future post.
Getting CUDA to run on your notebook is not always an easy task even if you have a CUDA enabled graphic card. In my Laptop there is a NVIDIA Quadro FX 570M (256 MB). However, the laptops require you to have a vendor-specific driver otherwise you get the nice error message “no supported hardware detected” when trying to install the latest CUDA driver.
However, NVIDIA seems to realize that this situation is not amusing to any Laptop-Programmer (like me). So yesterday I found on the download site the following item:
Beta Notebook Driver for Developers (181.22)
So downloaded, installed, and same error as with the other drivers. Well, at least it is a step into the right direction. Maybe it works for others.
However, there is still the option to install a modified driver for your notebook. This worked for me but is not the clean solution. Those drivers can be found at http://www.laptopvideo2go.com/
My current work includes the evaluation of a parametric motion field which is based on cubic B-splines. Already some time ago I ported the straight forward B-spline evaluation on the graphic card using CUDA. However, it required the evaluation of multiple nested for loops and took even on the graphic card some time.
So the quest of today was: Find a fast way for cubic B-spline evaluation using CUDA!
Of course Google helped me out and pointed meto the following papers:
Daniel Ruijters, Bart M. ter Haar Romeny, and Paul Suetens, “ Accuracy of GPU-based B-Spline Evaluation,” In Proc. Tenth IASTED International Conference on Computer Graphics and Imaging (CGIM), Innsbruck, Austria, pp. 117-122, February 13-15, 2008.
Christian Sigg and Markus Hadwiger, “ Fast Third-Order Texture Filtering,” In GPU Gems 2: Programming Techniques for High-Performance Graphics and General-Purpose Computation, Matt Pharr (ed.), Addison-Wesley; chapter 20, pp. 313-329, 2005.
They showed that a fast and accurate evaluation of B-splines is possible by replacing the nearest neighbor lookups during the B-spline evaulation by linear interpolation which is hard-wired on the GPU.
Finally the best of all: Daniel Ruijters provides some clean CUDA code for download on a website. It helped me out very well. In overall I gained a speed-up factor of 7 in comparison to my first naive implementation!
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