General-purpose computing on graphics processing units

General-purpose computing on graphics processing units (GPGPU, rarely GPGP) 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 process far more pictures and graphical data per second than a traditional CPU. Migrating data into graphical form and then using the GPU to scan and analyze it can create a large speedup. GPGPU pipelines were developed at the beginning of the 21st century for 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. In principle, any arbitrary boolean function, including those of addition, multiplication and other mathematical functions can be built-up from a functionally complete set of logic operators. In 1987, Conway's Game of Life became one of the first examples of general purpose computing using an early stream processor called a blitter to invoke a special sequence of logical operations on bit vectors. General-purpose computing on GPUs became more 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). These early efforts to use GPUs as general-purpose processors required reformulating computational problems in terms of graphics primitives, as supported by the two major APIs for graphics processors, OpenGL and DirectX. This cumbersome translation was obviated by the advent of general-purpose programming languages and APIs such as Sh/RapidMind, Brook and Accelerator. These were followed by Nvidia's CUDA, which allowed programmers to ignore the underlying graphical concepts in favor of more common high-performance computing concepts. Newer, hardware vendor-independent offerings include Microsoft's DirectCompute and Apple/Khronos Group's OpenCL. This means that modern GPGPU pipelines can leverage the speed of a GPU without requiring full and explicit conversion of the data to a graphical form. Any language that allows the code running on the CPU to poll a GPU shader for return values, can create a GPGPU framework.