While it seems that central processing units (CPUs) get all the glory for computing horsepower, graphical processing units (GPUs) have become the processor of choice for many types of intensively parallel computations.
As the boundaries of computing are pushed in areas such as speech recognition and natural language processing, image and pattern recognition, text and data analytics, and other complex areas, researchers continue to look for new and better ways to extend and expand computing capabilities. For decades this has been accomplished via high-performance computing (HPC) clusters, which use huge amounts of expensive processing power to solve problems.
Researchers at the University of Illinois had studied the possibility of using graphics processing units (GPUs) in desktop supercomputers to speed processing of tasks such as image reconstruction, but it was a computing group at the University of Toronto that demonstrated a way to significantly advance computer vision using GPUs. By plugging in GPUs, previously used primarily for graphics, it became possible to achieve huge performance gains on computing neural networks, and these gains were reflected in superior results in computer vision.