GPU Computing - NVIDIA

GPU-accelerated computing is the use of a graphics processing unit (GPU) together with a CPU to accelerate scientific, engineering, and enterprise applications. Pioneered in 2007 by NVIDIA®, GPUs now power energy-efficient data centres in government labs, universities, enterprises, and small-and-medium businesses around the world. These GPU Computing systems are ideal for data analytics, artificial intelligence, and other visualisation workloads. GPUs for computational workloads are specially designed, and include features such as matrix multiplication, multi-core computations, and internal circuitry to ensuring the computers can take advantage of all the capabilities of the GPU. NVIDIA®maintains a catalogue of GPU accelerated applications – download the catalogue.

How Applications Accelerate with GPUs?

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. XENON designs GPU Computing systems for optimal performance across the whole system – power supply, cooling, memory and internal CPU performance.

Browse this section to review the NVIDIA range of GPUs.

XENON also builds specific systems with NVIDIA GPUs – view these systems.

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