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GPU Computing for Tech Enthusiasts

GPU-accelerated computing uses a graphics processing unit (GPU) along with a CPU to accelerate applications. Pioneered in 2007 by NVIDIA, GPUs now power datacenters around the world. GPUs accelerate applications by offloading compute-intensive portions to the GPU while the remainder runs on the CPU, significantly increasing application performance. A GPU performs parallel operations on data for rendering graphics and is increasingly used for non-graphics applications requiring repetitive computations. GPUs have thousands of cores optimized for handling multiple tasks simultaneously, compared to CPUs which have a few cores for sequential processing.

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116 views4 pages

GPU Computing for Tech Enthusiasts

GPU-accelerated computing uses a graphics processing unit (GPU) along with a CPU to accelerate applications. Pioneered in 2007 by NVIDIA, GPUs now power datacenters around the world. GPUs accelerate applications by offloading compute-intensive portions to the GPU while the remainder runs on the CPU, significantly increasing application performance. A GPU performs parallel operations on data for rendering graphics and is increasingly used for non-graphics applications requiring repetitive computations. GPUs have thousands of cores optimized for handling multiple tasks simultaneously, compared to CPUs which have a few cores for sequential processing.

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Sam Ronny
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© © All Rights Reserved
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WHAT IS GPU COMPUTING?

WHAT IS GPU ACCELERATED COMPUTING?


GPU-accelerated computing is the use of a graphics processing unit (GPU) together with a CPU to accelerate
scientific, analytics, engineering, consumer, and enterprise applications. Pioneered in 2007 by NVIDIA, GPU
accelerators now power energy-efficient datacenters in government labs, universities, enterprises, and small-andmedium businesses around the world. GPUs are accelerating applications in platforms ranging from cars, to mobile
phones and tablets, to drones and robots.

HOW GPUS ACCELERATE APPLICATIONS


GPU-accelerated computing offers unprecedented application performance by offloading compute-intensive
portions of the application to the GPU, while the remainder of the code still runs on the CPU. From a user's
perspective, applications simply run significantly faster.

GPU
(Graphics Processing Unit) A programmable logic chip that renders images, animations and video for the
computer's screen. GPUs are located on plug-in cards, in a chipset on the motherboard or in the same chip as
the CPU (see diagram below).
A GPU performs parallel operations on data to render images for the screen. Although it is used for 2D data as
well as for zooming and panning the screen, a GPU is essential for smooth decoding and rendering of 3D
animations and video. The more sophisticated the GPU, the higher the resolution and the faster and smoother
the motion in games and movies. GPUs on stand-alone cards include their own memory, while GPUs in the
chipset or CPU chip share main memory with the CPU.
Not Just Graphics Processing
Since GPUs perform parallel operations on multiple sets of data, they are increasingly used as vector
processors for non-graphics applications that require repetitive computations. For example, in 2010, a Chinese
supercomputer achieved the record for top speed using more than seven thousand GPUs in addition to its CPUs
(see GPGPU). See graphics pipeline and multi-GPU.

Graphics Hardware Locations


In a PC, graphics rendering originally took place in the CPU only. Over time, functions were offloaded to
separate circuits and then to GPUs either in separate cards, the chipset or the CPU chip itself. See display
adapter, integrated graphics and integrated GPU.

An Integrated GPU
This Trinity chip from AMD integrates a sophisticated GPU with four cores of x86 processing and a DDR3
memory controller. Each x86 section is a dual-core CPU with its own L2 cache. (Image courtesy of Advanced
Micro Devices, Inc., www.amd.com)

CPU VERSUS GPU


A simple way to understand the difference between a CPU and GPU is to compare how they process tasks. A CPU
consists of a few cores optimized for sequential serial processing while a GPU has a massively parallel architecture
consisting of thousands of smaller, more efficient cores designed for handling multiple tasks simultaneously.
GPUs have thousands of cores to process parallel workloads efficiently

REFERENCE

Article Title: WHAT IS GPU COMPUTING?

Website Title: What is GPU Computing?

URL: http://www.nvidia.com/object/what-is-gpu-computing.html

Article Title: // Encyclopedia

Website Title: GPU Definition from PC Magazine Encyclopedia

URL: http://www.pcmag.com/encyclopedia/term/43886/gpu

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