Lenovo unveils new partnerships to help boost academic and healthcare sectors

Lenovo DCG has unveiled some interesting developments into how High Performance Computing (HPC) and AI is helping both the academic and healthcare sectors.

To support the University of Southampton’s drive towards boosting its academic research projects, it is capitalising on HPC to increase its compute power and faster access data insights.

Because of the increase in requests for computational power, the University of Southampton is working with high performance compute, storage and data analytics integrator OCF to build the fifth iteration of its “Iridis” compute cluster supercomputer.

The new iteration contains over 20,000 Intel Xeon Scalable System cores on Lenovo ThinkSystem SD530 high-density servers.

Because the new cluster is capable of generating larger and more complex sets of data, it also requires more storage capacity. Lenovo DSS-G solutions will provide two petabytes of additional storage.

Dr. Oz Parchment, Director of IT at the University of Southampton, explained why they chose a Lenovo solution: “We were particularly impressed with the way the Lenovo solution brought together best-of-breed compute power, storage and networking into one coherent whole.

“It was very clear to us that Lenovo had invested a lot of knowledge and expertise in the design process.”

Over in the healthcare sector, Lenovo is partnering with the Barcelona Supercomputing Centre (BSC) in order to use AI to help millions of people suffering from visual impairments. This is achieved by using AI to help ophthalmologists detect diseases more effectively in screening processes.

AI technology further increases the likelihood of early detection by putting the power of screening in the hands of patients in underserved populations, allowing them to self-administer an initial screening in a matter of minutes, using a smartphone.

Dario Garcia-Gasulla, postdoctoral researcher at the Barcelona Supercomputing Center, explained: “Scaling the design, training and validation of machine learning models to study these vision issues may be challenging. But the impact of potential solutions for this is huge, as the same challenge is found within other medical domains and many other industrial applications.” 

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