What is GPU-Accelerated Computing?
GPU-accelerated computing is the use of a graphics processing unit (GPU) together with a CPU to accelerate deep learning, analytics, and engineering applications. Pioneered in 2007 by NVIDIA, GPU accelerators now power energy-efficient data centers in government labs, universities, enterprises, and small-and-medium businesses around the world. They play a huge role in accelerating applications in platforms ranging from artificial intelligence to cars, drones, and robots. Source - Navidia
History of GPUs
Originally, the primary purpose of GPU applications were for rendering graphics only. The history of graphics chips can be traced back to the 1980s, but the first consumer-level and “modern” equivalent to what we think of as a GPU was the NVIDIA® GeForce 256 (also called NV10) which was released in 1999. NVIDIA® marketed it as “the world’s first ‘GPU’” and is generally credited with popularizing the term. Source - icc-usa
