For a century, researchers have tracked genetic traits to find out which cattle produce more and better milk and meat. Now, two University of Florida scientists will use artificial intelligence to analyze millions of bits of genetic data to try to keep cattle cooler and thus, more productive. Raluca Mateescu, a UF/IFAS professor, and Fernanda Rezende, a UF/IFAS assistant professor – both in animal sciences -- gather hundreds of thousands of pieces of information about cattle genetic traits. They plan to use UF's supercomputer, the HiPerGator, to analyze that data. With the information Mateescu and her team get from the HiPerGator, they can give ranchers better recommendations on which animals to keep and breed for improved quantity of beef and dairy.

UFIT Research Computing has a team of 18 very dedicated people supporting research and education on UF's supercomputer HiPerGator. Now it has 7 open positions to help build the infrastructure and support for the University's AI Initiative in partnership with NVIDIA announced on July 21, see the press release. The arrival of the 3.0 upgrade to HiPerGator and the addition of HiPerGator AI powered by a 140 node NVIDIA DGX A100 SuperPOD puts UF at the top of the academic infrastructure for AI. Here are the technical details. The HiPerGator AI cluster powered by NVIDIA has significant computing power, but also adds complexity to the systems operated by UFIT RC.

The University of Florida (UF) has unveiled a $70 million partnership with Nvidia to boost the institution's AI capabilities thanks to what it claims will be the fastest supercomputer in academia. The deal will see UF upgrade its existing supercomputer, the HiPerGator 3.0, from maximum speeds of just over one petaflop to 700 petaflops of AI performance, which the institution hopes will advance academic research at an unprecedented pace. To boost the capabilities of the HiPerGator, UF will deploy Nvidia's DGX A100 systems, which are designed to build and run AI projects at a large scale. It will be the first time an institution of higher learning in the US accesses the A100 systems. The existing supercomputer will integrate 140 A100 systems powered by 1,120 A100 Tensor Core GPUs, and will include four petabytes of high-performance storage.

Nvidia and the University of Florida (UF) today announced plans to build the fastest AI supercomputer in academia. By enhancing the capabilities of UF's existing HiPerGator supercomputer with the DGX SuperPod architecture, Nvidia claims the system -- which it expects will be up and running by early 2021 -- will deliver 700 petaflops (one quadrillion floating point operations per second) of performance. Some researchers within the AI community believe that capable computers, in conjunction with reinforcement learning and other techniques, can achieve paradigm-shifting AI advances. A paper recently published by researchers at the Massachusetts Institute of Technology, MIT-IBM Watson AI Lab, Underwood International College, and the University of Brasilia found that deep learning improvements have been "strongly reliant" on increases in compute. And in 2018, OpenAI researchers released an analysis showing that from 2012 to 2018, the amount of compute used in the largest AI training runs grew more than 300,000 times with a 3.5-month doubling time, far exceeding the pace of Moore's law.