A graphics processing unit (GPU) is a specialized electronic circuit designed for digital image processing and to accelerate computer graphics, being present either as a component on a discrete graphics card or embedded on motherboards, mobile phones, personal computers, workstations, and game consoles. GPUs are also increasingly being used for artificial intelligence (AI) processing and model training due to linear algebra acceleration, which is also used extensively in graphics processing. Although there is no single definition of the term, and it may be used to describe any video display system, in modern use a GPU includes the ability to internally perform the calculations needed for various graphics tasks, like rotating and scaling 3D images, and often the additional ability to run custom programs known as shaders. This contrasts with earlier graphics controllers known as video display controllers which had no internal calculation capabilities, or blitters, which performed only basic memory movement operations. The modern GPU emerged during the 1990s, adding the ability to perform operations like drawing lines and text without CPU help, and later adding 3D functionality. Graphics functions are generally independent and this lends these tasks to being implemented on separate calculation engines. Modern GPUs include hundreds, or thousands, of calculation units. This made them useful for non-graphic calculations involving embarrassingly parallel problems due to their parallel structure. The ability of GPUs to rapidly perform vast numbers of calculations has led to their adoption in diverse fields including artificial intelligence (AI) where they excel at handling data-intensive and computationally demanding tasks. Other non-graphical uses include the training of neural networks and cryptocurrency mining.
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. In addition, Matrox while originally producing custom solutions, now customizes GPUs from Intel and AMD for workstation usage. Chinese companies such as Jingjia Micro have also produced GPUs for the domestic market although in terms of worldwide sales, they lag behind market leaders.
Computational functions
Several factors of GPU construction affect the performance of the card for real-time rendering, such as the size of the connector pathways in the semiconductor device fabrication, the clock signal frequency, and the number and size of various on-chip memory caches. Performance is also affected by the number of streaming multiprocessors (SM) for NVidia GPUs, or compute units (CU) for AMD GPUs, or Xe cores for Intel Xe-based GPUs, which describe the number of on-silicon processor core units within the GPU chip that perform the core calculations, typically working in parallel with other SM/CUs on the GPU. GPU performance is typically measured in floating point operations per second (FLOPS); Modern GPUs typically deliver performance measured in teraflops (TFLOPS). This is an estimated performance measure, and should not be treated as fact, as other factors can affect actual performance. Modern GPUs also include dedicated hardware blocks for ray tracing, video encoding, and AI acceleration.
GPU forms In personal computers, there are two main forms of GPUs: dedicated graphics (also called discrete graphics) and integrated graphics (also called shared graphics solutions, integrated graphics processors (IGP), or unified memory architecture (UMA).
Dedicated graphics processing unit
Dedicated graphics processing units use on board RAM that is dedicated to the GPU rather than relying on the computer's main system memory. This RAM is usually specially selected for the expected serial workload of the graphics card, such as GDDR SDRAM. This has massive performance benefits, but the caveat of "choking" when running out of dedicated memory, worsening performance. Technologies such as Scalable Link Interface (SLI), NVLink, and CrossFire allow multiple GPUs to draw images simultaneously for a single screen, increasing the processing power available for graphics. These technologies, however, are increasingly uncommon; most games do not fully use multiple GPUs, as most users cannot afford them. Multiple GPUs are still used on supercomputers (such as in Summit); on workstations to accelerate video (processing multiple videos at once) and 3D rendering; for visual effects (VFX); general purpose graphics processing unit (GPGPU) workloads and for simulations, and in AI to expedite training, as is the case with Nvidia's lineup of DGX workstations and servers.
Integrated graphics processing unit
… excerpt ends here. Continue reading the full article.
