Top 10 Most Popular Online Games 2019

For an expansion card that contains a graphics processing, see Video cards. "GPU" redirects here. For other uses, see GPU (disambiguation). Components of a GPU A graphics processing unit (GPU) is a specialized electronic circuit designed to rapidly manipulate and alter memory to accelerate the creation of images in a frame buffer intended for output to a display device. GPUs are used in embedded systems, mobile phones, personal computers, workstations, and game consoles. Modern GPUs are very efficient at manipulating computer graphics and image processing, and their highly parallel structure makes them more efficient than general-purpose CPUs for algorithms where the 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 embedded on the motherboard or—in certain CPUs—on the CPU die.[1] The term GPU was popularized by Nvidia in 1999, who marketed the GeForce 256 as "the world's first GPU", or Graphics Processing Unit,[2] although the term had been in use since at least the 1980s.[3] It was presented as a "single-chip processor with integrated transform, lighting, triangle setup/clipping, and rendering engines".[4] Rival ATI Technologies coined the term "visual processing unit" or VPU with the release of the Radeon 9700 in 2002. GPU companies GPU manufacturers market share 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 AMD control nearly 100% of the market as of 2008.[55] In addition, S3 Graphics[56] (owned by VIA Technologies) and Matrox[57] produce GPUs. Modern smartphones are also using mostly Adreno GPUs from Qualcomm, PowerVR GPUs from Imagination Technologies and Mali GPUs from ARM. Computational functions Modern GPUs use most of their transistors to do calculations related to 3D computer graphics. 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. 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 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; they are especially suited to other embarrassingly parallel problems. With the emergence of deep learning, the importance of GPUs has increased. In a research done by Indigo, it was found that while training a deep learning neural networks, GPUs can be 250 times faster than CPUs. That's a difference between one day of training and almost 8 months and 10 days of training. The explosive growth of Deep Learning in recent years has been attributed to the emergence of general purpose GPUs.[citation needed]However, beginning in early 2017, GPUs have begun to face some competition from Field Programmable Gate Arrays (FPGAs). FPGAs can also accelerate Machine Learning and Artificial Intelligence workloads. As AI applications mature, data for training models is not necessarily ordered in the neat, array-based, data that GPUs are best at handling, so FPGAs are also used for rapidly processing repetitive functions. Introduced at Hot Chips 2017, Microsoft's Project BrainWave is one example of real-time AI that uses FPGAs in an acceleration platform rather than GPUs. GPU accelerated video decoding The ATI HD5470 GPU (above) features UVD 2.1 which enables it to decode AVC and VC-1 video formats 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 and VDPAU, VAAPI, XvMC, and XvBA for Linux-based and UNIX-like operating systems. 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. GPU accelerated video decoding The ATI HD5470 GPU (above) features UVD 2.1 which enables it to decode AVC and VC-1 video formats 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 and VDPAU, VAAPI, XvMC, and XvBA for Linux-based and UNIX-like operating systems. 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.

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