What is GPU? how does it work? Graphical processing units, or GPUs, are what give you the video and graphics that appear on your screen. While computers can function without some sort of a GPU, you won’t be able to connect a monitor to them. Such machines (most commonly servers) are generally accessed remotely via command terminal anyways. GPUs come in all different shapes and forms, such as dedicated cards which you can plug into your desktop’s PCI-Express slot, to graphical chips called integrated graphics chips, which are built directly into the motherboard - the backbone component of your system.

A Graphics Processing Unit or GPU is meant to alleviate the load of the CPU by handling all the advanced computations necessary to project the final display on the monitor. Originally, CPUs handle all of the computations and instructions in the whole computer, thus the use of the word ‘central'. But as technology progressed, it became more advantageous to take out some of the responsibilities from the CPU and have it performed by other microprocessors. In the days before GUIs, the screen was simply a small grid with each box having an 8bit value that corresponds to a character. This was relatively very easy to do for the CPU, but GUIs have greater resolutions with each pixel having a 16bit or 32bit color value. GPUs where originally developed to render 2D graphics; specifically, to accelerate the drawing of windows in a GUI. But as the need for 3D and faster graphics acceleration grew, the GPU became faster and more specialized in its task.

GPUs are now generally floating point processors that can easily crunch geometric computations along with texture mapping tasks. Most GPUs have implemented MPEG primitives to make enhance the playback of videos; some even have the capability to directly decode HD video data, taking another task away from the CPU. Hardware wise, GPUs and CPUs are similar but not identical. If we looked at the very building block of each, the transistors, we can see that most GPUs already rival CPUs in transistor count. The specialized nature of GPUs means that it can do its task much faster than a CPU ever can, but it is not able to cover all of the capabilities of the CPU. Multiple GPUs can also be employed to achieve a single goal much like the dual core CPUs currently available. ATI’s Crossfire and NVidia’s SLI allow users to connect two identical GPU’s and make them work as one.

CPU is the brain of the computer the GPU is only meant to complement it. GPUs are specialized and cannot replace the function of a CPU. CPUs can perform the functions of a GPU but at a much slower speed. GPUs can rival CPUs in transistor count. GPUs can also work in tandem just like the CPU’s multi core capability.

A Graphics Processing Unit (GPU) is a special purpose processor, optimized for calculations commonly (and repeatedly) required for Computer Graphics, particularly SIMD operations. A Central Processing Unit (CPU) is a general purpose processor - it can in principle do any computation, but not necessarily in an optimal fashion for any given computation. One can do graphics processing on a CPU - but it likely will not produce the result anywhere nearly as fast as a properly programmed GPU.

All PCs have chips that render the display images to monitors All PCs have chips that render the display images to monitors. But not all these chips are created equal. Intel’s integrated graphics controller provides basic graphics that can display only productivity applications like Microsoft PowerPoint, low-resolution video and basic games. The GPU is in a class by itself – it goes far beyond basic graphics controller functions, and is a programmable and powerful computational device in its own right.

The GPU’s advanced capabilities were originally used primarily for 3D game rendering. But now those capabilities are being harnessed more broadly to accelerate computational workloads in areas such as financial modeling, cutting-edge scientific research and oil and gas exploration. In a recent Business Week article, Insight64 principal analyst Nathan Brook wood described the unique capabilities of the GPU this way: GPUs are optimized for taking huge batches of data and performing the same operation over and over very quickly, unlike PC microprocessors, which tend to skip all over the place.

Architecturally, the CPU is composed of just few cores with lots of cache memory that can handle a few software threads at a time. In contrast, a GPU is composed of hundreds of cores that can handle thousands of threads simultaneously. The ability of a GPU with 100+ cores to process thousands of threads can accelerate some software by 100x over a CPU alone. What is more, the GPU achieves this acceleration while being more power- and cost-efficient than a CPU. GPU-Accelerated Computing Goes Mainstream GPU-accelerated computing has now grown into a mainstream movement supported by the latest operating systems from Apple (with OpenCL) and Microsoft (using DirectCompute). The reason for the wide and mainstream acceptance is that the GPU is a computational powerhouse, and its capabilities are growing faster than those of the x86 CPU.

In today’s PC, the GPU can now take on many multimedia tasks, such as accelerating Adobe Flash video, transcoding (translating) video between different formats, image recognition, virus pattern matching and others. More and more, the really hard problems to solve are those that have an inherent parallel nature – video processing, image analysis, signal processing. The combination of a CPU with a GPU can deliver the best value of system performance, price, and power.

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