GPU-accelerated computing is the use of GPUs in conjunction with CPUs to significantly accelerate deep learning, analysis and engineering applications. Compute-intensive application parts are run on the GPUs and the remaining tasks are processed by the CPUs.
GPU accelerators provide extreme computing power to energy-efficient data centers in research facilities, universities and large, small and medium-sized enterprises around the world. They play a major role in accelerating applications in platforms ranging from artificial intelligence to cars, drones and robots.
A simple way to understand the difference between a GPU and a CPU is to compare how they handle tasks. A CPU consists of a few cores optimized for sequential serial processing. A GPU has a massively parallel architecture consisting of thousands of smaller, more efficient cores designed to handle multiple tasks simultaneously.
There are three basic approaches to adding GPU acceleration to your applications:
sysGen has more than 20 years of IT experience in research and development:
sysGen offers a variety of high-performance, application-optimized GPGPU servers with optimized thermal designs to minimize system power consumption and extremely efficient Platinum power supplies. GPGPUs extend the performance of today's processors by up to 10 times, but differ considerably:
With CUDA and OpenCL, two GPU programming environments are available which enable the use of GPUs for applications that can be parallelized:
Reliability, performance and efficiency:
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