Bench Press

We’ve talked before about researchers using PlayStation game consoles and gaming graphics cards to perform scientific computing, but we hadn’t heard too much about Microsoft’s XBox. Until now, that is, when University of Warwick researcher Dr. Simon Scarle demonstrated the use of the graphical horsepower on an XBox360 in high performance computing. By taking advantage of the parallel processing power of the on-board GPU, Dr. Scarle was able to use an Xbox360 to aid in his research and sidestepped the need to reserve time on a dedicated parallel processing computer or shell out thousands for a parallel network of PC’s.

Armed with his gaming console, Dr. Scarle used the Xbox’s GPU computing power to calculate and even predict cardiac arrhythmias based on his model of electric excitations of the heart. The result? A paper titled Implications of the Turing completeness of reaction-diffusion models, informed by GPGPU simulations on an XBox 360: Cardiac arrhythmias, re-entry and the Halting problem.

This is a highly effective way of carrying out high end parallel computing on “domestic” hardware for cardiac simulations. Although major reworking of any previous code framework is required, the Xbox 360 is a very easy platform to develop for and this cost can easily be outweighed by the benefits in gained computational power and speed, as well as the relative ease of visualization of the system.

So much attention thus far has focused on using the PlayStation 3 in distributed computing projects like Folding@Home — maybe its time that Microsoft release some sort of software to let the legions of XBox360 owners out there show the PS3 users that their machines are good for more than just gaming?

An impressive demonstration of the power of graphics cards is the use of graphics processing units (GPUs) in ray tracing. For those of you not in the know, ray tracing refers to a technique for rendering graphics by tracking how rays of light behave as they reflect from surface to surface, allowing you to create photorealistic images that have complicated reflections and shadows which traditional graphics methods fail to deliver. The flip side of this photorealism is that these techniques are incredibly taxing on computational systems, and it has been something of a “holy grail” for GPU and CPU makers to demonstrate so-called “real time” ray tracing on their systems.

Of course, while ray tracing is an impressive computational feat, most of these demonstrations only show off the aesthetic benefits of being able to implement ray tracing quickly. There are much more real-world impacts in the scientific and medical domains, such as in the field of radiotherapy.

In a nutshell, the idea behind radiotherapy as a cancer therapy is that you use strong bursts of radiation to kill off a tumor while minimizing the side effects of radiation exposure to the surrounding tissue. This balance is extremely difficult to manage as the calculations necessary to understand the effect of applying radiation from an external source on a complex three-dimensional maze of organs and liquids like the human body are highly sophisticated. Interestingly, these calculations actually resemble a ray tracing problem, as the problem of understanding radiotherapy dosage is one of understanding how individual “beams” of radiation travel and interact with the human body.

The result is that computer models which have been used to do dosage calculations are slow (making it impractical for physicians to consider multiple regimens or use more sophisticated “adaptive”/modulated radiation therapies), error-prone from the introduction of assumptions to “gloss over” some of the more sophisticated calculations, and very expensive given the need for large clusters of computational power.

What researchers at the University of Amsterdam have demonstrated is an implementation of a ray tracing algorithm targeted at the radiotherapy dosage question using GPU maker NVIDIA’s CUDA toolkit for performing mathematical calculations using the power of a graphics processor. The researchers used the fact that the power of a GPU rests in its ability to split up complicated math problems into many simpler problems to have the GPU calculate the paths of multiple “rays” of radiation simultaneously, resulting in a performance increase over a non-GPU accelerated technique ranging from 50% faster to 6 times faster! Amazingly, because of the way the GPU does its calculations (mainly that it avoids using a look-up table the CPU-driven algorithm needs), the GPU’s results are also more accurate, despite a single GPU implementation being both faster and cheaper than traditional techniques.

The implications to medicine? To quote the paper:

“The developed GPU algorithm now enables dose calculations at a speed that will be experienced as real time for conventional forward planning based on clinically relevant datasets. this can lead to a major reduction in the workload of radiotherapy treatment planning. Moreover, the presented GPU algorithm can be used to accelerate more advanced treatment planning optimization techniques.”

Paper: M. de Greef et al, “Accelerated Ray Tracing for Radiotherapy Dose Calculations on a GPU.” Medical Physics, Vol 36, Issue 9 (link: http://dx.doi.org/10.1118/1.3190156)

Presentation: http://www.amc.nl/upload/teksten/radiotherapie/hyperthermie/RayForDose-NVIDIA.pdf

(Image credit – Ray tracing schema) (Image from presentation)