Smarter Innovation at Scale
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A presentation by Michael Gleaves from the Hartree Centre at HPC and Big Data 2016, Central London
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Smarter Innovation at Scale
- 1. The Hartree Centre Smarter Innovation at Scale
- 2. E-Infrastructure • 2011 Tildesley Report • Economic Impact through Software & Modelling
- 3. Better products, developed faster and cheaper
- 4. • Blue Joule 120,000 Cores (1.4 Pflops) • Blue Wonder 19,000 Cores • 9PB of Disk Storage • 15 PB of Tape Storage • Big Data Analytics Cluster (288TB) • At Rest • Streaming • Predictive • Novel server technologies • Open Power • FPGAs • Data Centric Architecture • Intel Phi Our Machines
- 5. Our People • Engineers • Chemists • Life Scientists • Mathematicians • Software Developers • Data Scientists
- 6. Our Services • Collaborative R&D • Software & Algorithms • Training & Skills • Platform as a Service
- 7. Project Examples • Engineering & Manufacturing – Vehicle Design & Testing – Consumer Electronics Design – Consumer Packaged Goods Products and Packaging • Environment – Weather modeling • 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
- 8. 4 Forces of Change Industrial Engagement • Funding models demanding economic impact • Using existing computational modelling in industrial workflows • Better outcomes, developed faster and cheaper • New engagement models, delivery disciplines, cadence & language DemocratisationPower • Unprecedented scaling of systems needed • Availability and Affordability • Existing architectures are not sustainable • Moore’s Law ending • Emerging platforms and new Architectures • New languages, compilers, middleware and applications • Cloud Big Data • Everything will change • 3 V’s • Internet of Things • Security standards • Todays architectures will be obsolete in 5 years • Emerging platforms and new Architectures • New algorithms, languages, compilers, middleware and applications • Open data is great – but useless on its own • Analytics is the key to • Availability of skills will remain a bottleneck • Science and Knowledge workers • Build the Data Scientist in to the Machine / Software • New ways of interaction • Speech • Visualisation • Mobile • Emerging platforms and new Architectures • New algorithms, languages, compilers, middleware and applications
- 9. Chemistry High Accuracy Chemistry Simulation Formulation in the Cloud Engineering Data Driven Multi Physics and Multi Scale modelling Acoustic modelling of Large Scale CFD Intelligent manufacturing Life Sciences Agri-tech Food Provenance and security Enabling Technologies Machine Learning, Algorithms & Implementation for Data Centric Systems, HPC in the Cloud, Visual Interactive Supercomputing, Ease of Use, Access , Uncertainty Quantification and Modelling Research Areas:
- 11. What is the ternary phase diagram for the sds/c12e6/water mixture at 12% surfactant? Determining… High Throughput Chemistry & Materials Simulation • Automation • Force-field Development • Robust Simulation Analytics • Customizable Simulation Code • Uncertainty Quantification Big Data for Chemistry & Materials • Strong Links to Experimental Facilities • Data analytics • Machine Learning • Natural Language Processing • Cognitive Computing • Agreed Data Standards? Chemistry & Materials Advisor Data Centric Computing Infrastructures
- 14. A call to action! Hartree is looking for strategic engagement with industrial applications of data and compute. Merging of simulation and advanced analytics are merging on to a single compute platform. How do these technologies affect your business or sector. When do you engage with these and how?
Editor's Notes
- The Hartree Centre is developing capabilities in Big Data (See slide for subset of areas) and high throughput simulation (see slide for subset of areas) for the chemistry and materials sector. Combination of these two fields will give an integrated data analytics and simulation capability capable of addressing a number of real world applications. Our initial efforts are focused around computer aided formulation (fuels, lubricants & home and personal care products). The deliverable we envisage will be a chemistry and materials advisor that enables non-expert users to ask questions relating to their specific area. The results will be determined from data where it exists or from simulation where data is not available. These ‘in-silico’ experiments are required to be robust, automated and reliable. This includes ensuring robust and accurate force fields that are parameterised to appropriately predict experimental behaviour. We are investigating a couple of candidate methods for this area. Simulation results will be fed into the corpus of data for future advisor searches. The results given back to the scientist will be a series of results each accompanied with a probability score on the reliability of the data We are attempting to aid the scientist not replace them.
- Addedsd IBM Research. Is another get traiing and skills in the emerging areas of data centric cognitive computing?