Medical Image Processing

The invent of CUDA offered a new flexibility for scientific programming of the graphic card without the need to learn special graphic languages. However, getting your stuff to work with CUDA is not always that easy due to compiler limitations. For porting old code, you are most likely required to write complete parts new.

Intel announced already some time ago a new GPU code-named Larrabee. It is likely that Intel targets also the scientific programming community, as the platform has some nice benefits:

1. It is x86 compatible and your code will run with a simple recompilation.
2. Full support for OpenMP or Intel TBB.
3. Enhanced 512-bit vector processing units.

However, yet no hardware prototypes are available but writing code is already possible as Intel  provided some C++ implementation of the new Larrabee instruction set extension called LRBni. First prototypes are expected for the end of 2009.

First Medical Experiences at the Fully3D?

At this years Fully3D conference a high-performance workshop will take place. According to the topic list, Larrabee seems to be of some importance. Maybe we will see first prototype applications in Beijing 2009.

For those who don’t want to wait until then, check out these links for more information:

• First Look at the Larrabee New Instructions (LRBni)
• Intel fact sheet about Larrabee
• Intel’s SIGGRAPH 2008 paper on Larrabee

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.

Standard alternative

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!