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GeForce 6600GT (NV43) GPU

A graphics processing unit or GPU (also occasionally called visual processing unit or VPU) is a specialized electronic circuit designed to rapidly manipulate and alter memory in such a way so as to accelerate the building of images in a frame buffer intended for output to a display. GPUs are used in embedded systems, mobile phones, personal computers, workstations, and game consoles. Modern GPUs are very efficient at manipulating computer graphics, and their highly parallel structure makes them more effective than general-purpose CPUs for algorithms where processing of large blocks of data is done in parallel. In a personal computer, a GPU can be present on a video card, or it can be on the motherboard or—in certain CPUs—on the CPU die. More than 90% of new desktop and notebook computers^{[date missing]} have integrated GPUs, which are usually far less powerful than those on a dedicated video card.^[1]

The term was popularized by Nvidia in 1999, who marketed the GeForce 256 as "the world's first 'GPU', or Graphics Processing Unit, a single-chip processor with integrated transform, lighting, triangle setup/clipping, and rendering engines that is capable of processing a minimum of 10 million polygons per second". Rival ATI Technologies coined the term visual processing unit or VPU with the release of the Radeon 9700 in 2002.

History[link]

1980s[link]

In 1983 Intel made the iSBX 275 Video Graphics Controller Multimodule Board for industrial systems based on the Multibus standard.^[2] The card was based on the 82720 Graphics Display Controller and accelerated the drawing of lines, arcs, rectangles, and character bitmaps. The framebuffer was also accelerated through loading via DMA. The board was intended for use with Intel's line of Multibus industrial single board computer plugin cards.

Released in 1985, the Commodore Amiga was one of the first personal computers to come standard with a GPU. The GPU supported line draw, area fill, and included a type of stream processor called a blitter which accelerated the movement, manipulation, and combination of multiple arbitrary bitmaps. Also included was a graphics coprocessor with its own (primitive) instruction set. Prior to this and for quite some time after, many other personal computer systems required a general purpose CPU to handle every aspect of drawing the display.

In 1986, Texas Instruments released the TMS34010, the first microprocessor with on-chip graphics capabilities. It could run general-purpose code, but it had a very graphics-oriented instruction set. In 1990-1991, this chip became the basis of the Texas Instruments Graphics Architecture ("TIGA") Windows accelerator cards.

In 1987, the IBM 8514 graphics system was released as one of the first video cards for IBM PC compatibles to implement fixed-function 2D primitives in electronic hardware.

1990s[link]

Voodoo3 2000 AGP card

In 1991, S3 Graphics introduced the S3 86C911, which its designers named after the Porsche 911 as an indication of the performance increase it promised. The 86C911 spawned a host of imitators: by 1995, all major PC graphics chip makers had added 2D acceleration support to their chips. By this time, fixed-function Windows accelerators had surpassed expensive general-purpose graphics coprocessors in Windows performance, and these coprocessors faded away from the PC market.

Throughout the 1990s, 2D GUI acceleration continued to evolve. As manufacturing capabilities improved, so did the level of integration of graphics chips. Additional application programming interfaces (APIs) arrived for a variety of tasks, such as Microsoft's WinG graphics library for Windows 3.x, and their later DirectDraw interface for hardware acceleration of 2D games within Windows 95 and later.

In the early and mid-1990s, CPU-assisted real-time 3D graphics were becoming increasingly common in computer and console games, which led to an increasing public demand for hardware-accelerated 3D graphics. Early examples of mass-marketed 3D graphics hardware can be found in fifth generation video game consoles such as PlayStation and Nintendo 64. In the PC world, notable failed first-tries for low-cost 3D graphics chips were the S3 ViRGE, ATI Rage, and Matrox Mystique. These chips were essentially previous-generation 2D accelerators with 3D features bolted on. Many were even pin-compatible with the earlier-generation chips for ease of implementation and minimal cost. Initially, performance 3D graphics were possible only with discrete boards dedicated to accelerating 3D functions (and lacking 2D GUI acceleration entirely) such as the 3dfx Voodoo. However, as manufacturing technology again progressed, video, 2D GUI acceleration, and 3D functionality were all integrated into one chip. Rendition's Verite chipsets were the first to do this well enough to be worthy of note.

OpenGL appeared in the early 90s as a professional graphics API, but originally suffered from performance issues which allowed the Glide API to step in and become a dominant force on the PC in the late 90s.^[3] However these issues were quickly overcome and the Glide API fell by the wayside. Software implementations of OpenGL were common during this time although the influence of OpenGL eventually led to widespread hardware support. Over time a parity emerged between features offered in hardware and those offered in OpenGL. DirectX became popular among Windows game developers during the late 90s. Unlike OpenGL, Microsoft insisted on providing strict one-to-one support of hardware. The approach made DirectX less popular as a stand alone graphics API initially since many GPUs provided their own specific features, which existing OpenGL applications were already able to benefit from, leaving DirectX often one generation behind. (See: Comparison of OpenGL and Direct3D).

Over time Microsoft began to work more closely with hardware developers, and started to target the releases of DirectX with those of the supporting graphics hardware. Direct3D 5.0 was the first version of the burgeoning API to gain widespread adoption in the gaming market, and it competed directly with many more hardware specific, often proprietary graphics libraries, while OpenGL maintained a strong following. Direct3D 7.0 introduced support for hardware-accelerated transform and lighting (T&L) for Direct3D, while OpenGL already had this capability already exposed from its inception. 3D accelerators moved beyond being just simple rasterizers to add another significant hardware stage to the 3D rendering pipeline. The Nvidia GeForce 256 (also known as NV10) was the first consumer-level card on the market with hardware-accelerated T&L, while professional 3D cards already had this capability. Hardware transform and lighting, both already existing features of OpenGL, came to consumer-level hardware in the 90s and set the precedent for later pixel shader and vertex shader units which were far more flexible and programmable.

2000 to present[link]

With the advent of the OpenGL API and similar functionality in DirectX, GPUs added programmable shading to their capabilities. Each pixel could now be processed by a short program that could include additional image textures as inputs, and each geometric vertex could likewise be processed by a short program before it was projected onto the screen. Nvidia was first to produce a chip capable of programmable shading, the GeForce 3 (code named NV20). By October 2002, with the introduction of the ATI Radeon 9700 (also known as R300), the world's first Direct3D 9.0 accelerator, pixel and vertex shaders could implement looping and lengthy floating point math, and in general were quickly becoming as flexible as CPUs, and orders of magnitude faster for image-array operations. Pixel shading is often used for things like bump mapping, which adds texture, to make an object look shiny, dull, rough, or even round or extruded.^[4]

As the processing power of GPUs has increased, so has their demand for electrical power. High performance GPUs often consume more energy than current CPUs.^[5] See also performance per watt and quiet PC.

Today, parallel GPUs have begun making computational inroads against the CPU, and a subfield of research, dubbed GPU Computing or GPGPU for General Purpose Computing on GPU, has found its way into fields as diverse as machine learning,^[6] oil exploration, scientific image processing, linear algebra,^[7] statistics,^[8] 3D reconstruction and even stock options pricing determination. Nvidia's CUDA platform was the earliest widely adopted programming model for GPU computing. More recently OpenCL has become broadly supported. OpenCL is an open standard defined by the Khronos Group.^[9] OpenCL solutions are supported by Intel, AMD, Nvidia, and ARM, and according to a recent report by Evan's data Open CL is the GPGPU development platform most widely used by developers in both the US and Asia Pacific.

GPU companies[link]

Many companies have produced GPUs under a number of brand names. In 2008, Intel, Nvidia and AMD/ATI were the market share leaders, with 49.4%, 27.8% and 20.6% market share respectively. However, those numbers include Intel's integrated graphics solutions as GPUs. Not counting those numbers, Nvidia and ATI control nearly 100% of the market.^[10] In addition, S3 Graphics,^[11] VIA Technologies ^[12] and Matrox ^[13] produce GPUs.

Computational functions[link]

Modern GPUs use most of their transistors to do calculations related to 3D computer graphics. They were initially used to accelerate the memory-intensive work of texture mapping and rendering polygons, later adding units to accelerate geometric calculations such as the rotation and translation of vertices into different coordinate systems. Recent developments in GPUs include support for programmable shaders which can manipulate vertices and textures with many of the same operations supported by CPUs, oversampling and interpolation techniques to reduce aliasing, and very high-precision color spaces. Because most of these computations involve matrix and vector operations, engineers and scientists have increasingly studied the use of GPUs for non-graphical calculations. An example of GPUs being used non-graphically is the generation of Bitcoins, where the graphical processing unit is used to solve puzzles.

In addition to the 3D hardware, today's GPUs include basic 2D acceleration and framebuffer capabilities (usually with a VGA compatibility mode). Newer cards like AMD/ATI HD5000-HD7000 even lack 2D acceleration, it has to be emulated by 3D hardware.

GPU accelerated video decoding[link]

Most GPUs made since 1995 support the YUV color space and hardware overlays, important for digital video playback, and many GPUs made since 2000 also support MPEG primitives such as motion compensation and iDCT. This process of hardware accelerated video decoding, where portions of the video decoding process and video post-processing are offloaded to the GPU hardware, is commonly referred to as "GPU accelerated video decoding", "GPU assisted video decoding", "GPU hardware accelerated video decoding" or "GPU hardware assisted video decoding".

More recent graphics cards even decode high-definition video on the card, offloading the central processing unit. The most common APIs for GPU accelerated video decoding are DxVA for Microsoft Windows operating system, VDPAU, VAAPI, XvMC, and XvBA for Linux and UNIX based operating-system. All except XvMC are capable of decoding videos encoded with MPEG-1, MPEG-2, MPEG-4 ASP (MPEG-4 Part 2), MPEG-4 AVC (H.264 / DivX 6), VC-1, WMV3/WMV9, Xvid / OpenDivX (DivX 4), and DivX 5 codecs, while XvMC is only capable of decoding MPEG-1 and MPEG-2.

Video decoding processes that can be accelerated[link]

The video decoding processes that can be accelerated by today's modern GPU hardware are:

• Motion compensation (mocomp)
• Inverse discrete cosine transform (iDCT)
  • Inverse telecine 3:2 and 2:2 pull-down correction
• Inverse modified discrete cosine transform (iMDCT)
• In-loop deblocking filter
• Intra-frame prediction
• Inverse quantization (IQ)
• Variable-length decoding (VLD), more commonly known as slice-level acceleration
• Spatial-temporal deinterlacing and automatic interlace/progressive source detection
• Bitstream processing (Context-adaptive variable-length coding/Context-adaptive binary arithmetic coding) and perfect pixel positioning.

GPU forms[link]

Dedicated graphics cards[link]

The GPUs of the most powerful class typically interface with the motherboard by means of an expansion slot such as PCI Express (PCIe) or Accelerated Graphics Port (AGP) and can usually be replaced or upgraded with relative ease, assuming the motherboard is capable of supporting the upgrade. A few graphics cards still use Peripheral Component Interconnect (PCI) slots, but their bandwidth is so limited that they are generally used only when a PCIe or AGP slot is not available.

A dedicated GPU is not necessarily removable, nor does it necessarily interface with the motherboard in a standard fashion. The term "dedicated" refers to the fact that dedicated graphics cards have RAM that is dedicated to the card's use, not to the fact that most dedicated GPUs are removable. Dedicated GPUs for portable computers are most commonly interfaced through a non-standard and often proprietary slot due to size and weight constraints. Such ports may still be considered PCIe or AGP in terms of their logical host interface, even if they are not physically interchangeable with their counterparts.

Technologies such as SLI by Nvidia and CrossFire by ATI allow multiple GPUs to be used to draw a single image, increasing the processing power available for graphics.

Integrated graphics solutions[link]

Integrated graphics solutions, shared graphics solutions, or Integrated graphics processors (IGP) utilize a portion of a computer's system RAM rather than dedicated graphics memory. They are integrated into the motherboard. Exceptions are AMD's IGPs that use dedicated sideport memory on certain motherboards, and APUs, where they are integrated with the CPU die. Computers with integrated graphics account for 90% of all PC shipments.^[14] These solutions are less costly to implement than dedicated graphics solutions, but tend to be less capable. Historically, integrated solutions were often considered unfit to play 3D games or run graphically intensive programs but could run less intensive programs such as Adobe Flash. Examples of such IGPs would be offerings from SiS and VIA circa 2004.^[15] However, modern integrated graphics processors such as AMD's Fusion IGPs and Intel's HD Graphics are more than capable of handling 2D graphics from Adobe Flash or low stress 3D graphics, but struggle with the latest games like Battlefield 3. IGPs like the Intel's HD Graphics 3000 and AMD's Fusion IGPs have improved performance that may match cheap dedicated graphic cards, but still lag behind the more expensive dedicated graphics cards. While older platforms had the IGP integrated onto the motherboard, newer platforms (Intel Core i series and AMD Fusion) integrate the GPU right onto the CPU die.

As a GPU is extremely memory intensive, an integrated solution may find itself competing for the already relatively slow system RAM with the CPU, as it has minimal or no dedicated video memory. IGPs can have up to 29.856 GB/s of memory bandwidth from system RAM, however graphics cards can enjoy up to 264GB/sec of bandwidth over its memory-bus. Older integrated graphics chipsets lacked hardware transform and lighting, but newer ones include it.^[16][17]

Hybrid solutions[link]

This newer class of GPUs competes with integrated graphics in the low-end desktop and notebook markets. The most common implementations of this are ATI's HyperMemory and Nvidia's TurboCache. Hybrid graphics cards are somewhat more expensive than integrated graphics, but much less expensive than dedicated graphics cards. These share memory with the system and have a small dedicated memory cache, to make up for the high latency of the system RAM. Technologies within PCI Express can make this possible. While these solutions are sometimes advertised as having as much as 768MB of RAM, this refers to how much can be shared with the system memory.

Stream Processing and General Purpose GPUs (GPGPU)[link]

It is becoming increasing common to use a general purpose graphics processing unit as a modified form of stream processor. This concept turns the massive computational power of a modern graphics accelerator's shader pipeline into general-purpose computing power, as opposed to being hard wired solely to do graphical operations. In certain applications requiring massive vector operations, this can yield several orders of magnitude higher performance than a conventional CPU. The two largest discrete (see "Dedicated graphics cards" above) GPU designers, ATI and Nvidia, are beginning to pursue this approach with an array of applications. Both Nvidia and ATI have teamed with Stanford University to create a GPU-based client for the Folding@home distributed computing project, for protein folding calculations. In certain circumstances the GPU calculates forty times faster than the conventional CPUs traditionally used by such applications.^[18][19]

GPGPU can be used for many types of embarrassingly parallel task including ray tracing, computational fluid dynamics and weather modelling. They are generally suited to high-throughput type computations that exhibit data-parallelism to exploit the wide vector width SIMD architecture of the GPU.

Furthermore, GPU-based high performance computers are starting to play a significant role in large-scale modelling. Three of the 5 most powerful supercomputers in the world take advantage of GPU acceleration. This includes the current leader as of October 2010, Tianhe-1A, which uses the Nvidia Tesla platform.^[20]

NVIDIA cards support API extensions to the C programming language such as CUDA ("Compute Unified Device Architecture") and OpenCL. CUDA is specifically for NVIDIA GPUs whilst OpenCL is designed to work across a multitude of architectures including GPU, CPU and DSP (using vendor specific SDKs). These technologies allow specified functions (kernels) from a normal C program to run on the GPU's stream processors. This makes C programs capable of taking advantage of a GPU's ability to operate on large matrices in parallel, while still making use of the CPU when appropriate. CUDA is also the first API to allow CPU-based applications to access directly the resources of a GPU for more general purpose computing without the limitations of using a graphics API.

Since 2005 there has been interest in using the performance offered by GPUs for evolutionary computation in general, and for accelerating the fitness evaluation in genetic programming in particular. Most approaches compile linear or tree programs on the host PC and transfer the executable to the GPU to be run. Typically the performance advantage is only obtained by running the single active program simultaneously on many example problems in parallel, using the GPU's SIMD architecture.^[21][22] However, substantial acceleration can also be obtained by not compiling the programs, and instead transferring them to the GPU, to be interpreted there.^[23][24] Acceleration can then be obtained by either interpreting multiple programs simultaneously, simultaneously running multiple example problems, or combinations of both. A modern GPU (e.g. 8800 GTX or later) can readily simultaneously interpret hundreds of thousands of very small programs.

See also[link]

Reference[link]

• Brute force attack
• Computer graphics
• Computer hardware
• Computer monitor
• Central processing unit
• Physics processing unit (PPU)
• Ray tracing hardware
• Video card
• Video Display Controller
• Video game console

Hardware[link]

• Comparison of AMD graphics processing units
• Comparison of Nvidia graphics processing units
• Intel GMA
• Larrabee
• Nvidia PureVideo - the bit-stream technology from Nvidia used in their graphics chips to accelerate video decoding on hardware GPU with DXVA.
• UVD (Unified Video Decoder) - is the video decoding bit-stream technology from ATI Technologies to support hardware (GPU) decode with DXVA.

APIs[link]

• DirectX Video Acceleration (DxVA) API for Microsoft Windows operating-system.
• OpenVideo Decode (OVD) – an new open cross-platform video acceleration API from AMD.^[25]
• Video Acceleration API (VA API)
• Video Decode Acceleration Framework is Apple Inc.s API for hardware-accelerated decoding of H.264 on Mac OS X
• VDPAU (Video Decode and Presentation API for Unix)
• VideoToolBox is an undocumented API from Apple Inc. for hardware-accelerated decoding on Apple TV and Mac OS X 10.5 or later.^[26]
• X-Video Bitstream Acceleration (XvBA), the X11 equivalent of DXVA for MPEG-2, H.264, and VC-1
• X-Video Motion Compensation, the X11 equivalent for MPEG-2 video codec only

Applications[link]

• GPU cluster
• NCLab provides free GPU programming in the web browser.
• Mathematica includes built-in support for CUDA and OpenCL GPU execution
• MATLAB acceleration using the Parallel Computing Toolbox and MATLAB Distributed Computing Server,^[27] as well as 3rd party packages like Jacket.
• Molecular modeling on GPU
• Bitcoin Mining

References[link]

External links[link]

Wikimedia Commons has media related to: Graphics processing unit

• GPU Specifications, Comparison, and Performance Charts
• NVIDIA - What is a GPU?^{[dead link]}
• The GPU Gems book series
• - a Graphics Hardware History
• Toms Hardware GPU beginners' Guide
• General-Purpose Computation Using Graphics Hardware
• How GPUs work
• GPU Caps Viewer - Video card information utility
• OpenGPU-GPU Architecture(In Chinese)
• ARM Mali GPUs Overview

Look up processor in Wiktionary, the free dictionary.

Processor or Processing unit may refer to:

Computing[link]

• Central processing unit (CPU), an electronic circuit which executes computer programs, containing a processing unit and a control unit
  • Processing unit, in Von Neumann computer architecture, contains an arithmetic logic unit (ALU) and processor registers
• Microprocessor, a CPU on one silicon chip as part of a microcomputer
• Graphics processing unit (GPU / VPU), a dedicated graphics rendering device for a personal computer or game console
• Physics processing unit (PPU), a dedicated microprocessor designed to handle the calculations of physics
• Digital signal processor, a specialized microprocessor designed specifically for digital signal processing
• Network processor, a microprocessor specifically targeted at the networking application domain
• Front end processor, a helper processor for communication between a host computer and other devices
• Coprocessor
  • Floating point unit
• Data processor, a system that translates or converts between different data formats
• Word processor, a computer application used for the production of printable material
• Audio processor, used in studios and radio stations

Other[link]

• Food processor, a machine for preparing food

See also[link]

• Process (disambiguation)

This disambiguation page lists articles associated with the same title. If an internal link led you here, you may wish to change the link to point directly to the intended article.

A modern render of the Utah teapot, an iconic model in 3D computer graphics created by Martin Newell in 1975.

Computer graphics is a sub-field of computer science which studies methods for digitally synthesizing and manipulating visual content. Although the term often refers to the study of three-dimensional computer graphics, it also encompasses two-dimensional graphics and image processing.

Overview[link]

Computer graphics studies the manipulation of visual and geometric information using computational techniques. It focuses on the mathematical and computational foundations of image generation and processing rather than purely aesthetic issues. Computer graphics is often differentiated from the field of visualization, although the two fields have many similarities.

Connected studies include:

• Scientific visualization
• Information visualization
• Computer vision
• Image processing
• Computational geometry
• Computational topology
• Applied mathematics

Applications of computer graphics include:

• Special effects
• Visual effects
• Video games
• Digital art

History[link]

One of the first displays of computer animation was Futureworld (1976), which included an animation of a human face and hand — produced by Ed Catmull and Fred Parke at the University of Utah.

There are several international conferences and journals where the most significant results in computer graphics are published. Among them are the SIGGRAPH and Eurographics conferences and the Association for Computing Machinery (ACM) Transactions on Graphics journal. The joint Eurographics and ACM SIGGRAPH symposium series features the major venues for the more specialized sub-fields: Symposium on Geometry Processing,Symposium on Rendering, and Symposium on Computer Animation. As in the rest of computer science, conference publications in computer graphics are generally more significant than journal publications (and subsequently have lower acceptance rates)^[1][2][3].^[4]

Subfields in computer graphics[link]

A broad classification of major subfields in computer graphics might be:

1. Geometry: studies ways to represent and process surfaces
2. Animation: studies with ways to represent and manipulate motion
3. Rendering: studies algorithms to reproduce light transport
4. Imaging: studies image acquisition or image editing

Geometry[link]

The subfield of geometry studies the representation of three-dimensional objects in a discrete digital setting. Because the appearance of an object depends largely on its exterior, boundary representations are most commonly used. Two dimensional surfaces are a good representation for most objects, though they may be non-manifold. Since surfaces are not finite, discrete digital approximations are used. Polygonal meshes (and to a lesser extent subdivision surfaces) are by far the most common representation, although point-based representations have become more popular recently (see for instance the Symposium on Point-Based Graphics). These representations are Lagrangian, meaning the spatial locations of the samples are independent. Recently, Eulerian surface descriptions (i.e., where spatial samples are fixed) such as level sets have been developed into a useful representation for deforming surfaces which undergo many topological changes (with fluids being the most notable example^[5]).

Geometry Subfields

• Implicit surface modeling - an older subfield which examines the use of algebraic surfaces, constructive solid geometry, etc., for surface representation.
• Digital geometry processing - surface reconstruction, simplification, fairing, mesh repair, parameterization, remeshing, mesh generation, surface compression, and surface editing all fall under this heading.^[6][7][8]
• Discrete differential geometry - a nascent field which defines geometric quantities for the discrete surfaces used in computer graphics.^[9]
• Point-based graphics - a recent field which focuses on points as the fundamental representation of surfaces.
• Subdivision surfaces
• Out-of-core mesh processing - another recent field which focuses on mesh datasets that do not fit in main memory.

Animation[link]

The subfield of animation studies descriptions for surfaces (and other phenomena) that move or deform over time. Historically, most work in this field has focused on parametric and data-driven models, but recently physical simulation has become more popular as computers have become more powerful computationally.

Subfields

• Performance capture
• Character animation
• Physical simulation (e.g. cloth modeling, animation of fluid dynamics, etc.)

Rendering[link]

Rendering generates images from a model. Rendering may simulate light transport to create realistic images or it may create images that have a particular artistic style in non-photorealistic rendering. The two basic operations in realistic rendering are transport (how much light passes from one place to another) and scattering (how surfaces interact with light). See Rendering (computer graphics) for more information.

Transport

Transport describes how illumination in a scene gets from one place to another. Visibility is a major component of light transport.

Scattering

Models of scattering and shading are used to describe the appearance of a surface. In graphics these problems are often studied within the context of rendering since they can substantially affect the design of rendering algorithms. Shading can be broken down into two orthogonal issues, which are often studied independently:

1. scattering - how light interacts with the surface at a given point
2. shading - how material properties vary across the surface

The former problem refers to scattering, i.e., the relationship between incoming and outgoing illumination at a given point. Descriptions of scattering are usually given in terms of a bidirectional scattering distribution function or BSDF. The latter issue addresses how different types of scattering are distributed across the surface (i.e., which scattering function applies where). Descriptions of this kind are typically expressed with a program called a shader. (Note that there is some confusion since the word "shader" is sometimes used for programs that describe local geometric variation.)

Other subfields

• physically based rendering - concerned with generating images according to the laws of geometric optics
• real time rendering - focuses on rendering for interactive applications, typically using specialized hardware like GPUs
• non-photorealistic rendering
• relighting - recent area concerned with quickly re-rendering scenes

Notable researchers in computer graphics[link]

See also[link]

References[link]

Further reading[link]

• Foley et al. Computer Graphics: Principles and Practice.
• Shirley. Fundamentals of Computer Graphics.
• Watt. 3D Computer Graphics.

External links[link]

Look up computer graphics in Wiktionary, the free dictionary.

• A Critical History of Computer Graphics and Animation
• History of Computer Graphics series of articles

University groups[link]

• Computer Graphics Usability and Visualization Group at Simon Fraser University
• Computer Graphics Group at The University of Hong Kong
• Bath University Media Technology Research Centre
• Berkeley Computer Animation and Modeling Group
• Berkeley Computer Graphics
• Bristol University Computer Graphics Group
• C²G² at Columbia University
• Center for Visual Information Technology,IIIT Hyderabad
• Caltech Multi-Res Modeling Group
• Carnegie Mellon Graphics Lab
• Center for Graphics and Geometric Computing at Technion Israel Institute of Technology, Haifa, Israel
• Computer Graphics Department at Max-Planck-Institut fur Informatik
• Computer Graphics Department at Haute Ecole Albert Jacquard
• Computer Graphics Group at Brown
• Computer Graphics Group at RWTH Aachen University
• Computer Graphics at Harvard
• Computer Graphics and Immersive Technologies Laboratory at USC
• Graphics Lab of Institute for Creative Technologies at USC
• Computer Graphics Laboratory at Korea Advanced Institute of Science and Technology (KAIST)
• Computer Graphics Group at PUC-Rio
• Computer Graphics Group at University of Bonn
• Computer Graphics Group at University of Virginia
• Computer Graphics Laboratory at University of Tokyo
• Computer Graphics Laboratory at UT Austin
• Computer Graphics Laboratory at ETH Zurich
• Computer Graphics / Geometric Design Group at Rice
• Computer Graphics and User Interfaces Lab at Columbia University
• Computer Graphics and Visualization Lab at Purdue University
• Computer Graphics and Visualization Lab at University of Utah
• Computer Graphics and Visualization Lab at University of Wisconsin
• Cornell University Program of Computer Graphics
• Dynamic Graphics Project at University of Toronto
• Geometric Modeling and Industrial Geometry Group at Technische Universitat Wien
• The Institute of Computer Graphics and Algorithms at Technische Universitat Wien
• Graphics and Image Analysis at UNC
• Graphics and Geometric Computing Group at Tsinghua University
• Graphics@Illinois
• GRAIL at University of Washington
• GRAVIR at iMAGIS
• GVIL at University of Maryland, College Park
• GVU Center at Georgia Tech
• IDAV Visualization and Graphics Research Group at UC Davis
• IMAGINE Research Group at Universidad de los Andes, Bogotá, Colombia
• Imager Laboratory at University of British Columbia
• MIT Computer Graphics Group
• MRL at NYU
• Princeton Graphics and Geometry Group
• Stanford Computer Graphics Laboratory
• UCSD Computer Graphics Laboratory
• Vision Research Center at Vanderbilt
• INI-GraphicsNet international network

Industry[link]

Industrial labs doing "blue sky" graphics research include:

• Adobe Advanced Technology Labs
• MERL
• Microsoft Research - Graphics
• NVIDIA Research

Major film studios notable for graphics research include:

• ILM
• PDI/Dreamworks Animation
• Pixar