So where does nVidia's Tesla fit into all of this?
Initially, in order to process all of this data and to help create the 3D imagery they first used CPUs. When this device was in its first inception, they used Intel Pentium II clusters which took approximately a week to generate one image. After that, they decided to upgrade to Pentium M which brought it down to four and a half hours. But the problem was that their goal was to be able to get the scan of the patient done and processed before the patient even left the doctor's office so that there would be no more waiting for the result. They then began to add more and more nodes [after 6 or 7 nodes, the speed gains were no longer noticeable] by 12 they were beginning to lose speed. They looked at possibly using the IBM Cell processor, but that was too expensive. They also looked at possibly implementing DSPs but they were not fast enough either.
Back in 2006, CUDA was announced they did an experiment to see whether it was worth it. They simulated the ultrasound as it passed through the tissue. The FFTs
took up the majority of the processing power so they tried offloading the FFTs to the GeForce 8800GTX GPU. The FFT was 8 times faster on the GPU compared to a single core on the Intel Core 2 architecture. After that point, more optimizations were done in order to make it even faster. At one point, 2D FFTs were running at 16x the speed of the CPUs. Initially there was resistance towards this change, so they began to check all of the results that came from the GPUs with the CPUs just to prove that the data and results acquired from the GPUs was verifiable by the CPUs.
The original hardware structure was cluster based and this presented some problems. The problems were that the CPU clusters generated a lot of heat, created a lot of noise, used a lot of power, and took up a lot of space. Implementing CUDA was different in that the data acquisition structure was the same but the processing CPU node was now one CPU with two Tesla GPUs. This led to reduced noise due to the GPU fans automatically throttling up when needed. It also meant fewer processors. And when not processing and sitting idle, they draw significantly less power. For comparison, the old cluster drew 600W idle and the new Tesla-based system pulled 300W at idle, effectively pulling half the power during idle. Not only was idle power consumption improved, they also improved load power consumption as well by switching to nVidia Tesla GPUs. The power draw from the Tesla GPUs was 700W in comparison to 850-900W on the old cluster at load.
Techniscan did not stop there, once the newest Tesla C1060 cards came out, they reduced the cluster from 4 GPUs to 2 GPUs which were actually faster than the old 4 and were also cooler and quieter. They then continued with the optimizations by beginning to take out FORTRAN code out of their software and made a list of what they wanted out of the CUDA technology which runs on the nVidia GPUs. They wanted to make sure that their code performed fast, but was also maintainable to a point where it was still fast enough for a 30 minute doctor visit. It took them about 3-4 man months to port over the functions of the FORTRAN code over to CUDA code. This was done between two people, Jim the CUDA developer, and his FORTRAN coding colleague. Here
is a video of Jim last year explaining this as well, covering many of the same points made here.
At the moment, the data acquisition is 12 minutes per breast and processing time is 24 minutes per breast.
This is not quite at the speed that they want it at, but with upcoming new generation of Tesla Fermi cGPUs, the speeds of computation done by the CPU could increase exponentially. In the end, they would like to be able to have accurate results given to the doctor within the standard 30 minute doctor visit so that the patient and doctor can immediately discuss the results without having to come to another visit or try to analyze the results on their own.
Using GPGPU technology is also very important for bringing the cost down, as the main intent for this device is to fit the price bracket so that even small doctors offices in outback/out-of-reach areas are able to afford it. We discussed the development of the device and the fact that it comes at a significantly lower price than radiation-based solutions means that in this case, saying that GPGPU saves lives is not a marketing gimmick, but rather the fact of the matter.
We are very excited for this information and hope that they are able to bring this product to market as soon as possible and begin to further the fight against breast cancer. Being someone who has had family members be impacted by breast cancer, I feel like we need to take every opportunity we can get to detect and treat this disease as soon as possible.
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