OCF has deployed new systems supporting research into superfluid flows and deep learning at Queen Mary University of London (QMUL) and Newcastle University.
The universities are the first UK academic organisations to deploy IBM's POWER9 system which supports high-performance computing (HPC), analytics and artificial Intelligence (AI) workloads.
QMUL needs to have the latest technology to enable its researchers to work in areas where they are using deep learning to solve difficult scientific challenges.
The university became one of the first organisations to purchase two IBM Accelerated Compute Servers (AC922) powered by POWER9 CPUs, Volta GPUs and NVLink 2.0 interconnects.
"Modern AI, HPC and analytics workloads are driving an ever-growing set of data intensive challenges," said Julian Fielden, managing director of OCF.
"These challenges can only be met with accelerated infrastructure, such as IBM's POWER9. In such a highly competitive field as academic research, providing superior HPC services to compute large quantities of data quickly, can help to attract world-class researchers, as well as grants and funding," said Fielden.
Professor Sean Gong, head of the Computer Vision Research Group at QMUL, said deep learning by large-scale convolutional neural networks - a category of neural networks that has been proven effective in areas such as image recognition and classification - has radically changed research into computer vision in recent years.
"Given our previous test trials on IBM Minsky POWER8 servers, we expect to see significant benefit from the new POWER9 servers for deep learning on big video data," said Gong.
Carlo Barenghi, co-director of the Joint Quantum Centre at Newcastle University said the major difficulty it faces is that its calculations are non-linear, time-dependent and three-dimensional, so solving them is not possible with pencil and paper.
"The numerical computation requires large memory and fast speed - we are humbled from the start.
"We did some investigations and the IBM POWER9 system was the best technology for our work - in trial runs we got a ‘speed up factor' in the order of 10x magnitude, so the decision was easily made," added Barenghi.
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