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Technology / Data

Nimbix taps into IBM and NVIDIA for HPC in the cloud

Analytics looks to be increasingly moving to the cloud and Nimbix is looking to take advantage of it with an HPC solution.

The high performance computing cloud platform provider is adding IBM’s Power Systems S822LC featuring NVIDIA Pascal GPUs so that developers and data scientists have even more power to play with.

The IBM Power Systems offering is designed for HPC and was developed from the OpenPOWER Foundation to focus on deep learning, artificial intelligence, and cognitive computing.

Combined at the silicon level with the NVIDIA NVLink processor the POWER8 CPU is directly connected with NVIDIA Tesla to increase incorporation into the overall system design, something which the company says will allow data to flow 5x faster than on a comparable offering.

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Nimbix says that they are offering this so that researchers, data scientists, and engineers can leverage optimise workloads.

Sumit Gupta, Vice President, High Performance Computing and Data Analytics, IBM, said: “Today’s emerging workloads like machine and deep learning, artificial intelligence, accelerated databases, and high performance data analytics require incredible speed through accelerated computing.”

Because of the high demand for compute power from scientists and AI researchers there is a significant growth from tech companies seeking to provide the capabilities that will fulfill those needs.

The IBM Power Systems S822LC is designed for HPC .
The IBM Power Systems S822LC is designed for HPC .

 

Scientists at the likes of the Scripps Research Institute have been using HPC power to look into finding cures for viruses such as HIV and Zika, while also looking at cures for cancer.

The benefit of this kind of compute power being in the cloud is that it is on-demand, so they essentially only pay for it when it is required, this can help to reduce costs and speed up the time to discovery.

Roy Kim, Tesla Product Lead at NVIDIA, said: “The Nimbix cloud platform offers scientists and researchers on-demand, scalable compute resources to deliver significantly faster results than was previously thought possible.”
This article is from the CBROnline archive: some formatting and images may not be present.