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The Hartree Centre Neil Morgan Smarter Innovation at Scale 1 The Hartree Centre 2011 Tildesley Report Economic impact through software & modelling UK-wide investment from BIS 2012 Creation of the Hartree Centre Focus on industrial engagement Partnership with IBM 2014 Phase 2 investment Big data expansion & EEC R&D 2015 Major Investment in R&D with IBM Research Data Centric & Cognitive Computing Embedded IBM Research Centre Extended industrial & scientific reach 2 “To transform UK competitiveness by facilitating and accelerating the adoption of data-centric computing and cognitive technologies.” Lenovo / Arm development system Research & innovation Development & Better products & services Deployment through collaboration prototyping delivered faster & cheaper Our mission 3 • Collaborative Projects • Software & Algorithms • Training & Skills • Platform as a Service • R&D - Proof of Technology Our services 4 • Engineers • Chemists • Life Scientists • Mathematicians • Data Scientists • Software Engineers • Visualisation/VR/AR Engineers credit: istock Our people 5 Example Projects • Engineering and manufacturing – Vehicle design and testing – Consumer electronics design – Consumer goods products and packaging • Environment – Ocean and weather modelling • Life Sciences – Genomics for better crop yields – Disease mapping • Energy – Advanced battery cell design – Efficient well head oil extraction • Financial Services – Risk management – Service modelling • Transport – Network simulation 6 “Integrated Data Analytics and Modeling & Simulation Platforms” Ideas Proof of Proof of Roll Out • Creation of Technology Concept • Scale out as new ideas • Feasibility • Capability a robust • Minimal • Demonstrate service, viable to a user business or product base the licence value application Closing the loop between experiment andHartree comp Centreutati oPipelinen 7 Different Types of Workflows, Different Solutions Data Centric Conceptual View of Data Intensive-Integer Workflow Applications e.g. Quantized Neural Nets Conceptual View of Data Fusion/Uncertainty Quantification Workflow e.g. Low Spatial Weather Ensembles Locality Floating Point Integer OPS OPS High Spatial Conceptual View of Locality Data Intensive with Floating Point Workflow e.g. Linear Regression / Clustering Algorithms Region defined by Compute Centric LINPACK Applications 8 • Paragon Power8+NVLINK P100 • Emerging technologies • Panther Power8/NVIDIA K80 • NVIDIA GPU • FPGA • Blue Joule BG/Q (BGAS) 36,000 • Data Centric Systems Cores (0.5 Pflops) • Intel Xeon Phi • Blue Wonder NextScale 8,000 Cores • ARM systems • Data Analytics Cluster (288TB) Our machines 9 IBM Research and Hartree Centre R&D Programme Science, Engineering & Project Areas Life Sciences Manufacturing • Initial focus on three domains and enabling technologies • Genomics • Multi-physics component • Precision Agriculture • Machine Learning component • Eco-system identification and • Food Sustainability and • Full Jet Engine simulation development: collaborative partners in Security under consideration industry • Key measure is the impact on UK based commercial organizations Chemistry Enabling Technology • Computer Aided Formulation • Large Scale Machine Learning • Machine Learning component • Algorithms/Implementation to • Improvements for Dissipative exploit DCS Particle Dynamics • Visualization, Visual Analytics • HPC in the Cloud POC – & Computational Steering Motivating Problem for • HPC in the Cloud – tools etc. Enabling Technology • Ease of Access (IPad) • Uncertainty Quantification 10 Thank you for listening Contact: Email: [email protected] Twitter: @HartreeCentre 11.

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High Performance Computing and BD
High-Performance Computing and Big Data Challenge Dr Violeta Holmes Matthew Newall The University of Huddersfield Outline • High-Performance Computing – E-Infrastructure – Top500 -Tianhe-II – UoH experience: HPC resources and applications • Big Data – Hadoop – Case study: Close Call • Summary High-Performance Computing • The HPC and Big Data are new technologies vital for the advancement in science, business and industry. • ”High Performance Computing (HPC) and e- infrastructure in general are drivers of economic growth and societal well-being. They are also vital for maintaining international competitiveness in the generation of knowledge and its application.”(Kenway, Coveney, McGuire, Parchment, & Parsons, 2012) Where we were 50s-early 80s • Centralised computing facilities • Dummy Terminals • Punch cards • Batch Processing μ Processors • Allowed users to have “intelligent” machines under their table – Seen as an expensive typewriter – Few organisations and educational institutions had them • These machines were quickly overtaking Mainframes in price-performance factors, and later overtaking them in performance. 90s – early 2000 • Information on Demand - For the first time sharing and communicating became easy and quick. Where we are now • Efficient Multitasking Desktops • Capable Workstations • Mobile Devices – less capable Through the use of middlewares: • Clusters • Grids The 5th Utility Utility Computing/Cloud Computing/On- Demand Computing/Service Computing • The concept of just plugging in your problem into a computer system and getting results. • Defined as: “A large-scale distributed computing paradigm that is driven by economies of scale, in which a pool of abstracted virtualised, dynamically-scalable, managed computing power, storage, platforms and services are delivered on demand to external customers over the internet.” (Foster et al.
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Science, Technology and Innovation Exploiting the Excellence
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Scafell Pike - Public Hpcaas Offering to UK Academics and Industry Image Courtesy of STFC
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