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Throughout the s, 2D GUI acceleration continued to evolve. As manufacturing capabilities improved, so did the level of integration of graphics chips. In the early- and mids, real-time 3D graphics were becoming increasingly common in arcade, computer and console games, which led to an increasing public demand for hardware-accelerated 3D graphics.
Early examples of mass-market 3D graphics hardware can be found in arcade system boards such as the Sega Model 1Namco System 22and Sega Model 2and the fifth-generation video game consoles such as the SaturnPlayStation and Nintendo 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 Unit 2 home labs key entirely such as the PowerVR and the 3dfx Voodoo.
However, as manufacturing technology continued to progress, video, 2D GUI acceleration and 3D functionality were all integrated into one chip. 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.
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.
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. The earliest incarnations of shader execution engines used in Xbox were not general purpose and could not execute arbitrary pixel code.
Vertices and pixels were processed by different units which had their own resources with pixel shaders having much tighter constraints being as they are executed at much higher frequencies than with vertices.
Pixel shading is often used for bump mappingwhich adds texture, to make an object look shiny, dull, rough, or even round or extruded. 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 oil explorationscientific image processinglinear algebra statistics 3D reconstruction and even stock options pricing determination.
GPGPU at the time was the precursor to what we now call compute shaders e. CUDA, OpenCL, DirectCompute and actually abused the hardware to a degree by treating the data passed to algorithms as texture maps and executing algorithms by drawing a triangle or quad with an appropriate pixel shader.
Over the years, the energy consumption of GPUs has increased and to manage it, several techniques have been proposed. More recently OpenCL has become broadly supported. A new feature in this new GPU microarchitecture included GPU boost, a technology adjusts the clock-speed of a video card to increase or decrease it according to its power draw.
The GeForce 10 series of cards are under this generation of graphics cards. Their release results in a substantial increase in the performance per watt of AMD video cards. Computational functions[ edit ] Modern GPUs use most of their transistors to do calculations related to 3D computer graphics.
GPUs 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 CPUsoversampling and interpolation techniques to reduce aliasingand 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; they are especially suited to other embarrassingly parallel problems. With the emergence of deep learning, the importance of GPUs has increased.
The explosive growth of Deep Learning in recent years has been attributed to the emergence of general purpose GPUs. However, these can require changes to existing code and GPUs are still very popular.
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".
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