JADE: Joint Academic Data science Endeavour - 2

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This proposal brings together 19 universities, including 12 out of 16 newly established UKRI CDTs in Artificial Intelligence. Led by the University of Oxford, with support from the Alan Turing Institute (Turing), Bath, Bristol, Cambridge, Exeter, Imperial, KCL, Leeds, Loughborough, Newcastle, QMUL, Sheffield, Southampton, Surrey, Sussex, UCL, Warwick and York, our proposal aims to build on the success of the JADE Tier 2 facility. The current JADE facility represents a unique national resource providing state of the art GPU computing facilities to world leading experts in the areas of Artificial Intelligence/Machine Learning (AI/ML) and molecular dynamics (MD) research. In addition to providing a leading compute resource, the JADE facility has also provided a nucleus around which a national consortium of AI researchers has formed, making it the de facto national compute facility for AI research. By providing a much-needed shared resource to these communities, JADE has also delivered an outstanding level of world leading science, evidenced in the twenty two pages of preliminary case studies submitted to EPSRC on 11/09/18. JADE2 will build upon these successes by providing increased computational capabilities to these communities and delivering a stronger, more robust service to address the lessons learned from the initial service. The architecture for JADE2 will be a similar to that of JADE, based on NVIDIA's DGX platform. JADE is formed from 22x DGX1V nodes. JADE2 will be over twice the size of JADE and employ the more cost effective DGX1 Max Q platform. Differences between Max Q and the premium DGX1V are centred on on a slightly reduced bandwidth to GPU memory and lower peak compute performance.Tests of relevant codes on these platforms show that, for AI/ML and Molecular Dynamics, Max Q achieves at least 3/4 performance, using 2/3 the power for 1/2 of the price. The system will be run as a national facility, providing free access to all academic users through a lightweight Resource Allocation Panel (RAP). HECBioSim will run the RAP for MD users, ATI will run the corresponding RAP for AI/ML users.