Dedicated GPU
The most powerful GPUs connect to the motherboard via an expansion slot such as PCI Express (PCIe) or Accelerated Graphics Port (AGP), and can normally be changed or updated with reasonable ease, given that the motherboard can support the upgrade.A few graphics cards still use PCI slots, but their bandwidth is so low that they’re usually only used when a PCIe or AGP slot isn’t available.
A dedicated GPU isn’t always removable, and it doesn’t always connect to the motherboard in the same way.The term “dedicated” refers to dedicated graphics cards’ RAM being dedicated to the card’s use, not to the fact that most dedicated GPUs are removable.
Furthermore, this RAM is frequently chosen specifically for the graphics card’s predicted serial workload (see GDDR).In contrast to “UMA” systems, systems with specialized, discrete GPUs were sometimes referred to as “DIS” systems.
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Due to size and weight constraints, dedicated GPUs for portable computers are most typically interfaced through a non-standard and often proprietary slot.Even though they are not physically interchangeable, such ports may nevertheless be classified as PCIe or AGP in terms of their logical host interface.
Nvidia’s SLI and NVLink, as well as AMD’s CrossFire, allow several GPUs to render images simultaneously for a single screen, increasing the graphics processing power available.However, because most games do not fully utilize several GPUs, and most users cannot purchase them, these technologies are becoming increasingly rare.
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 VFX and simulations, and in AI to speed up training, as with Nvidia’s DGX workstations and servers and Tesla GPUs, as well as Intel’s upcoming Ponte Vecchio GPUs.
Integrated graphics processing unit
The position of an integrated GPU in a northbridge/southbridge system layout.Integrated graphics processing unit (IGPU), Integrated graphics, shared graphics solutions, integrated graphics processors (IGP) or unified memory architecture (UMA) employ a portion of a computer’s system RAM rather than dedicated graphics memory.
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IGPs can be built into the motherboard as part of the (northbridge) chipset, or on the same die as the CPU (integrated circuit) (like AMD APU or Intel HD Graphics).AMD’s IGPs can utilise dedicated sideport[clarification needed] memory on some motherboards.This is a set block of high-performance memory reserved only for the GPU.
NOTE:An ASRock motherboard with integrated graphics, which has HDMI, VGA and DVI outs.
In early 2007, integrated graphics systems accounted for almost 90% of all PC shipments.They’re less expensive to set up than dedicated graphics processors, but they’re also less powerful.Integrated processing was once thought to be unsuitable for playing 3D games or running graphically intense programs, but it could run less intensive apps like Adobe Flash. Offerings from SiS and VIA from 2004 are examples of such IGPs.
Modern integrated graphics processors, such as AMD’s Accelerated Processing Unit and Intel’s HD Graphics, can handle 2D and low-stress 3D graphics with ease.Because GPU computations are memory-intensive, integrated processing may have to compete with the CPU for the system’s relatively slow RAM, as it has little or no dedicated video memory.
System RAM can provide up to 29.856 GB/s of memory bandwidth to IGPs, whereas the RAM plus GPU core on a graphics card can provide up to 264 GB/s of bandwidth.The GPU’s performance may be limited by the memory bus bandwidth, however multi-channel memory can compensate for this shortcoming.
Hardware transform and lighting were not available on older integrated graphics chipsets, but they are now available on current chipsets.
Hybrid graphics processing
In the low-end desktop and notebook sectors, this newer class of GPUs competes with integrated graphics.ATI’s HyperMemory and Nvidia’s TurboCache are the most frequent implementations of this.
Hybrid graphics cards are a little more expensive than integrated graphics, but they’re still cheaper than dedicated graphics cards.To compensate for the system RAM’s high latency, these share memory with the system and have a small specialized memory cache.
This is achievable thanks to PCI Express technologies.While these solutions may promise as much as 768MB of RAM, this refers to the amount that can be shared with the system memory.
Stream processing and general purpose GPUs
A general purpose graphics processing unit (GPGPU) is increasingly being used to run compute kernels as a modified form of stream processor (or vector processor).Instead of being hardwired primarily for graphical operations, this notion converts the tremendous computational capability of a modern graphics accelerator’s shader pipeline into general-purpose processing capacity.
This can provide several orders of magnitude more performance than a traditional CPU in certain applications demanding huge vector calculations.AMD and Nvidia, the two largest discrete (see “Dedicated graphics cards” above) GPU manufacturers, are experimenting with this technology in a variety of applications.
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Nvidia and AMD have collaborated with Stanford University to develop a GPU-based client for the Folding@home distributed computing project, which is used to calculate protein folding.In some cases, the GPU can perform calculations forty times quicker than the CPUs typically utilized in such applications.
GPGPU can be utilized for a variety of activities that are embarrassingly parallel, such as ray tracing.They’re best for high-throughput calculations with data parallelism to take use of the GPU’s broad vector width SIMD architecture.In addition, GPU-based high-performance computers are increasingly being used in large-scale modeling.
GPU acceleration is used by three of the world’s ten most powerful supercomputers. OpenCL and OpenMP are API enhancements to the C programming language that GPUs support.Furthermore, each GPU vendor created its own API that is solely compatible with their GPUs, such as AMD APP SDK and Nvidia CUDA.These methods allow compute kernels from a standard C application to execute on the GPU’s stream processors.
This allows C programs to take advantage of a GPU’s capacity to work in parallel on huge buffers while still using the CPU when necessary.CUDA is also the first API that allows CPU-based applications to directly utilize the resources of a GPU for more general-purpose computation without having to use a graphics API.
Since 2005, there has been a growing interest in exploiting GPU performance for evolutionary computation in general, and specifically for accelerating fitness evaluation in genetic programming.
The majority of methods compile linear or tree programs on the host PC before transferring the executable to the GPU for execution.Using the GPU’s SIMD architecture, the speed benefit is typically only realized by executing the single active program in parallel on multiple sample problems.
However, by not compiling the programs and instead moving them to the GPU to be interpreted there, significant acceleration can be achieved.Acceleration can therefore be achieved by understanding numerous programs at the same time, executing multiple example problems at the same time, or a mix of the two.
A modern GPU can easily interpret hundreds of thousands of extremely little programs at the same time. Dedicated processing cores for tensor-based deep learning applications are available on some current workstation GPUs, such as Nvidia Quadro workstation cards based on the Volta and Turing architectures.
Tensor Cores are the name given to these cores in Nvidia’s current GPU series.Using 4×4 matrix multiplication and division, these GPUs typically have considerable FLOPS performance gains, resulting in hardware performance up to 128 TFLOPS in some workloads.
These tensor cores are also expected to come in consumer graphics cards that run the Turing architecture.
External GPU (eGPU)
An external GPU, like a huge external hard drive, is a graphics processor that is located outside of the computer’s enclosure.Laptop computers occasionally employ external graphics processors.
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Laptops may have plenty of RAM and a powerful central processing unit (CPU), but they frequently lack a powerful graphics processor in favor of a less powerful but more energy-efficient on-board graphics chip.
On-board graphics chips are frequently insufficient for playing video games or performing other graphically demanding tasks such as video editing or 3D animation and rendering. As a result, the ability to connect a GPU to a notebook’s external bus is desirable.PCI Express is the only bus that can be utilized for this.
A Thunderbolt 1, 2, or 3 port (PCIe 4, up to 10, 20, or 40 Gbit/s respectively) or an ExpressCard or mPCIe port (PCIe 1, up to 5 or 2.5 Gbit/s respectively) could be used.Those ports are only available on a limited number of notebooks.
Because powerful GPUs can quickly consume hundreds of watts, eGPU containers include their own power supply (PSU).Vendor support for external GPUs has lately gained traction.
Apple’s decision to officially allow external GPUs with MacOS High Sierra 10.13.4 was a significant milestone.Several large hardware companies (HP, Alienware, and Razer) are also developing Thunderbolt 3 eGPU enclosures.eGPU implementations by enthusiasts have continued to be fueled by this support.