General-purpose computing on graphics processing units

General-purpose computing on graphics processing units (GPGPU, rarely GPGP or GP²U) is the use of a graphics processing unit (GPU), which typically handles computation only for computer graphics, to perform computation in applications traditionally handled by the central processing unit (CPU). The use of multiple video cards in one computer, or large numbers of graphics chips, further parallelizes the already parallel nature of graphics processing. In addition, even a single GPU-CPU framework provides advantages that multiple CPUs on their own do not offer due to the specialization in each chip.

Essentially, a GPGPU pipeline is a kind of parallel processing between one or more GPUs and CPUs that analyzes data as if it were in image or other graphic form. While GPUs operate at lower frequencies, they typically have many times the number of cores. Thus, GPUs can operate on pictures and graphical data effectively far faster than a traditional CPU. Migrating data into graphical form and then using the GPU to scan and analyze it can result in profound speedup.

GPGPU pipelines were first developed for better, more general graphics processing (e.g., for better shaders). These pipelines were found to fit scientific computing needs well, and have since been developed in this direction.

General-purpose computing on GPUs only became practical and popular after about 2001, with the advent of both programmable shaders and floating point support on graphics processors. Notably, problems involving matrices and/or vectors – especially two-, three-, or four-dimensional vectors – were easy to translate to a GPU, which acts with native speed and support on those types. The scientific computing community's experiments with the new hardware began with a matrix multiplication routine (2001); one of the first common scientific programs to run faster on GPUs than CPUs was an implementation of LU factorization (2005).

...
Wikipedia