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GPU
,→History: minor date correction
=History=
In the 1970s and 1980s RAM was expensive and Home Computers used custom graphics chips to operate directly on registers/memory without a dedicated frame buffer, like [https://en.wikipedia.org/wiki/Television_Interface_Adaptor TIA]in the [[Atari 8-bit|Atari VCS]] gaming system, [https://en.wikipedia.org/wiki/CTIA_and_GTIA GTIA]+[https://en.wikipedia.org/wiki/ANTIC ANTIC] in the [[Atari 8-bit|Atari 400/800]] series, or [https://en.wikipedia.org/wiki/Original_Chip_Set#Denise Denise]+[https://en.wikipedia.org/wiki/Original_Chip_Set#Agnus Agnus] in the [[Amiga|Commodore Amiga]] series. The 1990s would make 3D graphics and 3D modeling more popular, especially for video games. Cards specifically designed to accelerate 3D math, such as the [https://en.wikipedia.org/wiki/Voodoo2 3dfx Voodoo2], were used by the video game community to play 3D graphics. Some game engines, such as [https://en.wikipedia.org/wiki/Quake_(video_game) Quake], could use instead the [[SIMD and SWAR Techniques|SIMD-capabilities]] of CPUs such as the [[Intel]] [[MMX]] instruction set or [[AMD|AMD's]] [[X86#3DNow!|3DNow!]]. Sony's 3D capable chip used in the PlayStation (1994) and Nvidia's 2D/3D combi chips like NV1 (1995) coined the term GPU for 3D graphics hardware acceleration. With the advent of the [https://en.wikipedia.org/wiki/Unified_shader_model unified shader architecture], like in Nvidia [https://en.wikipedia.org/wiki/Tesla_(microarchitecture) Tesla] (2006), ATI/AMD [https://en.wikipedia.org/wiki/TeraScale_(microarchitecture) TeraScale] (20082007) or Intel [https://en.wikipedia.org/wiki/Intel_GMA#GMA_X3000 GMA X3000] (2006) , GPGPU frameworks like CUDA and OpenCL emerged and gained in popularity.
The large number of regular [https://en.wikipedia.org/wiki/Matrix_multiplication matrix multiplications] led to natural SIMD-style algorithms. The 3D graphics community drew upon the rich history of vector-compute and SIMD-compute from 1980s and 1970s supercomputers. As such, many publications relating to [[Cray X-MP|Cray-vector supercomputers]] or the [[Connection Machine]] supercomputer easily apply to modern GPUs. For example, all the algorithms described in the 1986 publication ''Data Parallel Algorithms'' <ref>[[Mathematician#Hillis|W. Daniel Hillis]], [[Mathematician#GSteele|Guy L. Steele, Jr.]] ('''1986'''). ''[https://dl.acm.org/citation.cfm?id=7903 Data parallel algorithms]''. [[ACM#Communications|Communications of the ACM]], Vol. 29, No. 12, Special Issue on Parallelism</ref> can be efficiently executed on a modern GPU workgroup (roughly ~256x GPU threads). The ''Data Parallel Algorithms'' paper is a beginner-level algorithms paper, demonstrating simple and efficient [[Parallel Prefix Algorithms|parallel-prefix sum]], parallel-linked list traversal, parallel RegEx matching on the 4096x parallel Connection Machine-2 supercomputer.
