Medical applications like CT (computed tomography) scanning and MRI (magnetic resonance imaging) require quick, accurate results from processing complex algorithms. A major challenge for manufacturers of medical imaging equipment is reducing the time required to produce the images while reducing equipment cost and valuable space.
The GPU has been on the rise as a compute tool. They are incredibly powerful, especially for computing parallel processes. So why isn’t everyone using a ton of them? Aside from any cost factors, GPUs are large and they require a lot of power and cooling, which most systems are not equipped to handle. GPUs are typically added to a server but the amount of data that can be manipulated is dependent on the number of GPUs the computer can support.