GPU-accelerated computing is the use of a graphics processing unit (GPU) together with a CPU to accelerate deep learning, analytics, and engineering applications. GPU accelerators play a huge role in accelerating applications in platforms ranging from artificial intelligence to cars, drones, and robots.
GPU-accelerated computing offloads 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 much faster.
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.
With over 450 HPC applications accelerated—including 10 out of top 10—all GPU users can experience dramatic throughput boost for their workloads. Find out if the applications you use are GPU-accelerated in NVidia's application catalog.
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