Graphics processing unit
From Wikipedia, the free encyclopedia
Not to be confused with Graphics card.
"GPU" redirects here. For other uses, see GPU (disambiguation).
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This article is outdated. (February 2013) |
The term GPU 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 are 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
1980s
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 coprocessor with its own (primitive) instruction set capable of directly invoking a sequence of graphics operations without CPU intervention. Prior to this and for quite some time after, many other personal computer systems instead used their main, general-purpose CPU to handle almost every aspect of drawing the display, short of generating the final video signal.
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 would become 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
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 continued to progress, 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 standalone 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 to coincide 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 had this capability already exposed from its inception. 3D accelerator cards 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 2005
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]2006 to present
With the introduction of the GeForce 8 series, which was produced by Nvidia, and then new generic stream processing unit GPUs became a more generalized computing device. 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,[5] oil exploration, scientific image processing, linear algebra,[6] statistics,[7] 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 which allows for the development of code for both GPUs and CPUs with an emphasis on portability.[8] OpenCL solutions are supported by Intel, AMD, Nvidia, and ARM, and according to a recent report by Evan's data OpenCL is the GPGPU development platform most widely used by developers in both the US and Asia Pacific.GPU companies
Many companies have produced GPUs under a number of brand names. In 2009, 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 as of 2008.[9] In addition, S3 Graphics [10] (owned by VIA Technologies) and Matrox [11] produce GPUs.Computational functions
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 hash functions.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
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
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
Dedicated graphics cards
Main article: Video card
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
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 264 GB/s of bandwidth over its memory-bus. [clarification needed] Older integrated graphics chipsets lacked hardware transform and lighting, but newer ones include it.[14][15] [clarification needed]
Hybrid solutions
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)
Main articles: GPGPU and Stream processing
It is becoming increasingly 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.[16][17]GPGPU can be used for many types of embarrassingly parallel tasks including ray tracing. 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 10 most powerful supercomputers in the world take advantage of GPU acceleration.[18]
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 directly access 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.[19][20] However, substantial acceleration can also be obtained by not compiling the programs, and instead transferring them to the GPU, to be interpreted there.[21][22] 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
- 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
- Comparison of AMD graphics processing units
- Comparison of Nvidia graphics processing units
- Comparison of Intel 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
- DirectX Video Acceleration (DxVA) API for Microsoft Windows operating-system.
- Video Acceleration API (VA API)
- VDPAU (Video Decode and Presentation API for Unix)
- 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
- GPU cluster
- Mathematica includes built-in support for CUDA and OpenCL GPU execution
- MATLAB acceleration using the Parallel Computing Toolbox and MATLAB Distributed Computing Server,[23] as well as 3rd party packages like Jacket.
- Molecular modeling on GPU
- Bitcoin mining
References
- Jump up ^ Denny Atkin. "Computer Shopper: The Right GPU for You". Retrieved 2007-05-15.
- Jump up ^ Michael Swaine, "New Chip from Intel Gives High-Quality Displays", March 14, 1983, p.16
- Jump up ^ 3dfx Glide API
- Jump up ^ Søren Dreijer. "Bump Mapping Using CG (3rd Edition)". Retrieved 2007-05-30.
- Jump up ^ http://dl.acm.org/citation.cfm?id=1553486
- Jump up ^ "Linear algebra operators for GPU implementation of numerical algorithms", Kruger and Westermann, International Conf. on Computer Graphics and Interactive Techniques, 2005
- Jump up ^ "ABC-SysBio—approximate Bayesian computation in Python with GPU support", Liepe et al., Bioinformatics, (2010), 26:1797-1799 [1]
- Jump up ^ Khronos Group
- Jump up ^ Q3 Sales Report from Jon Peddie Research via TechReport.com
- Jump up ^ http://www.s3graphics.com/en/products/index.aspx
- Jump up ^ http://www.matrox.com/graphics/en/products/graphics_cards
- Jump up ^ AnandTech: µATX Part 2: Intel G33 Performance Review
- Jump up ^ Tim Tscheblockov. "Xbit Labs: Roundup of 7 Contemporary Integrated Graphics Chipsets for Socket 478 and Socket A Platforms". Retrieved 2007-06-03.
- Jump up ^ Bradley Sanford. "Integrated Graphics Solutions for Graphics-Intensive Applications". Retrieved 2007-09-02.
- Jump up ^ Bradley Sanford. "Integrated Graphics Solutions for Graphics-Intensive Applications". Retrieved 2007-09-02.
- Jump up ^ Darren Murph. "Stanford University tailors Folding@home to GPUs". Retrieved 2007-10-04.
- Jump up ^ Mike Houston. "Folding@Home - GPGPU". Retrieved 2007-10-04.
- Jump up ^ http://www.top500.org/list/2012/06/100
- Jump up ^ John Nickolls. "Stanford Lecture: Scalable Parallel Programming with CUDA on Manycore GPUs".
- Jump up ^ S Harding and W Banzhaf. "Fast genetic programming on GPUs". Retrieved 2008-05-01.
- Jump up ^ W Langdon and W Banzhaf. "A SIMD interpreter for Genetic Programming on GPU Graphics Cards". Retrieved 2008-05-01.
- Jump up ^ V. Garcia and E. Debreuve and M. Barlaud. Fast k nearest neighbor search using GPU. In Proceedings of the CVPR Workshop on Computer Vision on GPU, Anchorage, Alaska, USA, June 2008.
- Jump up ^ "MATLAB Adds GPGPU Support". 2010-09-20.
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