Representative Big Data, Supercomputing and E-Infrastructure
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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. -
Annual Report 2018–19
Annual report 2018–19 2018 –19 Annual Report 2018–19 Part 1 Our year 3 Part 2 Trustees’ and strategic report 74 Part 3 Financial statements 84 Our year 1.1 Chair’s foreword 1.2 Institute Director and Chief Executive’s foreword 1 1.3 Outputs, impact and equalities 1.4 Turing trends and statistics 1.5 Research mapping: Linking research, people and activities 3 Creating real world impact We have had yet another year of rapid growth Since 2016, researchers at the Institute have produced and remarkable progress. over 200 publications in leading journals and demonstrated early impacts with great potential. Some of the impacts The Alan Turing Institute is a multidisciplinary institute are captured in this annual report and we are excited to formed through partnerships with some of the UK’s see the translation of our data science and AI research leading universities. This innovative approach gives us into a real-world context. access to expertise and skills in data science and AI that is unparalleled outside of a handful of large tech platforms. We are witnessing a massive growth of data science Our substantial convening power enables us to work and AI. We are also seeing important challenges and across the economy: with large corporations, the public breakthroughs in many areas including safe and ethical sector including government departments, charitable AI, quantum computing, urban transport, defence, foundations and small businesses. These collaborations manufacturing, health and financial services. Across cut across disciplines and break through institutional the Institute we are leading the public conversation in boundaries. -
STEM Education Centre E-Bulletin: March 2014
STEM Education Centre E-bulletin: March 2014 Welcome to the March e-bulletin from the University of Birmingham’s STEM Education Centre. It is designed to provide you with information about STEM-related events, resources, news and updates that may be of interest to you and your colleagues. STEM Policy and Practice News Budget 2014: Alan Turing Institute to lead big data research A £42m Alan Turing Institute is to be founded to ensure that Britain leads the way in big data and algorithm research, George Osborne has announced. Drawing on the name of the British mathematician who led code-breaking work at Bletchley Park during the World War II, the institute is intended to help British companies by bringing together expertise and experience in tackling problems requiring huge computational power. Turing’s work led to the cracking of the German "Enigma" codes, which used highly compl ex encryption, is believed to have saved hundreds or even thousands of lives. He later formed a number of theories that underpin modern computing, and formalised the idea of algorithms – sequences of instructions – for a computer. This announcement marks further official rehabilitation of a scientist who many see as having been treated badly by the British government after his work during the war. Turing, who was gay, was convicted of indecency in March 1952, and lost his security clearance with GCHQ, the successor to Bletchley Park. He killed himself in June 1954. Only after a series of public campaigns was he given an official pardon by the UK government in December 2013. Public Attitudes to Science (PAS) 2014 Public Attitudes to Science (PAS) 2014 is the fifth in a series of studies looking at attitudes to science, scientists and science policy among the UK public. -
Intel Parallel Computing Centre – First Report the Hartree Centre
Benefits of Leveraging Software Defined Visualization (OSPRay) Sergi Siso IPCC @ Hartree Centre, STFC Daresbury Laboratory United Kingdom Thursday, 23 March 2017 Software Defined Visualization 2 Index 1. Visualization Challenges in HPC 2. Ray tracing with OSPRay 3. Hands-On 1: Installing and using OSPRay 4. Remote Visualization with Paraview 5. Hand-One 2: Remote vis. of OpenCH and HVAC 6. Hartree Use Case 1: HVAC simulation 7. Hartree Use Case 2: IMAT Facility 8. Questions Thursday, 23 March 2017 Software Defined Visualization 3 Section 1 VISUALIZATION CHALLENGES IN HPC Why we need visualization in HPC “Yes," said Deep Thought, "I can do it.“ [Seven and a half million years later.... ] “The Answer to the Great Question... Of Life, the Universe and Everything... Is... Forty-two,' said Deep Thought, with infinite majesty and calm.” Douglas Adams, Hitchhiker’s Guide to the Galaxy Thursday, 23 March 2017 Software Defined Visualization 5 Why we need visualization in HPC Thursday, 23 March 2017 Software Defined Visualization 6 But not just rendering . In HPC visualization incudes: – Feature extraction – Isosurfaces / Isovolumes – Thresholding – Streamlines – Clip, Slice, gradient, … Some HPC visualization workloads are better suited to CPU . Visualization is a “big data” problem architectures. – Data movement problem (IO bottleneck) – Needs lots of memory – Need scalability to solve Thursday, 23 March 2017 Software Defined Visualization 7 Dataset Size Problem . Issues to simulate large datasets… May fit in System Memory but not in GPU memory PCI bus is slow Thursday, 23 March 2017 Software Defined Visualization 8 Rasterization Libraries OpenSWR vs MESA LLVMpipe Performance Comparison (drop in replacement for OpenGL) Thursday, 23 March 2017 Software Defined Visualization 9 Rasterization Libraries *Scientific Visualization in HPC presentation from Peter Messmer (NVIDIA) Thursday, 23 March 2017 Software Defined Visualization 10 In-Situ Visualization . -
Alan Turing Institute Symposium on Reproducibility for Data-Intensive Research – FINAL REPORT Page | 1 Lucie C
Alan Turing Institute Symposium on Reproducibility for Data-Intensive Research – FINAL REPORT Page | 1 Lucie C. Burgess, David Crotty, David de Roure, Jeremy Gibbons, Carole Goble, Paolo Missier, Richard Mortier, 21 July 2016 Thomas E. Nichols, Richard O’Beirne Dickson Poon China Centre, St. Hugh’s College, University of Oxford, 6th and 7th April 2016 ALAN TURING INSTITUTE SYMPOSIUM ON REPRODUCIBILITY FOR DATA-INTENSIVE RESEARCH – FINAL REPORT AUTHORS Lucie C. Burgess1, David Crotty2, David de Roure1, Jeremy Gibbons1, Carole Goble3, Paolo Missier4, Richard Mortier5, Thomas E. Nichols6, Richard O’Beirne2 1. University of Oxford 2. Oxford University Press 3. University of Manchester 4. Newcastle University 5. University of Cambridge 6. University of Warwick TABLE OF CONTENTS 1. Symposium overview – p.2 1.1 Objectives 1.2 Citing this report 1.3 Organising committee 1.4 Format 1.5 Delegates 1.6 Funding 2. Executive summary – p.4 2.1 What is reproducibility and why is it important? 2.2 Summary of recommendations 3. Data provenance to support reproducibility – p.7 4. Computational models and simulations – p.10 5. Reproducibility for real-time big data – p.13 6. Publication of data-intensive research – p.16 7. Novel architectures and infrastructure to support reproducibility – p.19 8. References - p.22 Annex A – Symposium programme and biographies for the organising committee and speakers Annex B – Delegate list ACKNOWLEDGEMENTS The Organising Committee would like to thanks the speakers, session chairs and delegates to the symposium, and in particular all those who wrote notes during the sessions and provided feedback on the report. This report is available online at: https://osf.io/bcef5/ (report and supplementary materials) https://dx.doi.org/10.6084/m9.figshare.3487382 (report only) Alan Turing Institute Symposium on Reproducibility for Data-Intensive Research – FINAL REPORT Page | 2 Lucie C. -
CV Adilet Otemissov
CV Adilet Otemissov RESEARCH INTERESTS Global Optimization; Stochastic Methods for Optimisation; Random Matrices EDUCATION DPhil (PhD) in Mathematics 2016{2021 University of Oxford The Alan Turing Institute, the UK's national institute for data science and AI - Thesis title: Dimensionality reduction techniques for global optimization (supervised by Dr Coralia Cartis) MSc in Applied Mathematics with Industrial Modelling 2015{2016 University of Manchester - Thesis title: Matrix Completion and Euclidean Distance Matrices (supervised by Dr Martin Lotz) BSc in Mathematics 2011{2015 Nazarbayev University AWARDS & ACHIEVEMENTS - SEDA Professional Development Framework Supporting Learning award for successfully completing Developing Learning and Teaching course; more details can be found on https://www.seda.ac.uk/supporting-learning, 2020 - The Alan Turing Doctoral Studentship: highly competitive full PhD funding, providing also a vibrant interdisciplinary research environment at the Institute in London, as well as extensive training in the state-of-the-art data science subjects from world's leading researchers, 2016{2020 - NAG MSc student prize in Applied Mathematics with Industrial Modelling for achieving the highest overall mark in the numerical analysis course units in my cohort at the University of Manchester, 2016 PUBLICATIONS - C. Cartis, E. Massart, and A. Otemissov. Applications of conic integral geometry in global optimization. 2021. Research completed, in preparation - C. Cartis, E. Massart, and A. Otemissov. Constrained global optimization of functions with low effective dimensionality using multiple random embeddings. ArXiv e-prints, page arXiv:2009.10446, 2020. Manuscript (41 pages) submitted for publication in Mathematical Programming. Got back from the first round of reviewing - C. Cartis and A. Otemissov. A dimensionality reduction technique for unconstrained global optimization of functions with low effective dimensionality. -
Enabling a World-Leading Regional Digital Economy Through Data Driven Innovation
©Jonathan Clark Fine Art, Representatives of the Artist's Estate Enabling a World-Leading Regional Digital Economy through Data Driven Innovation Edinburgh & South East Scotland City Region A Science and Innovation Audit Report sponsored by the Department for Business, Energy & Industrial Strategy MAIN REPORT Edinburgh and South East Scotland City Region Consortium Foreword In Autumn 2015 the UK Government announced regional Science and Innovation Audits (SIAs) to catalyse a new approach to regional economic development. SIAs enable local Consortia to focus on analysing regional strengths and identify mechanisms to realise their potential. In the Edinburgh and South East Scotland City Region, a Consortium was formed to focus on our rapidly growing strength in Data-Driven Innovation (DDI). This report presents the results which includes broad-ranging analysis of the City Region’s DDI capabilities, the challenges and the substantial opportunities for future economic growth. Data-led disruption will be at the heart of future growth in the Digital Economy, and will enable transformational change across the broader economy. However, the exact scope, scale and timing of these impacts remains unclear1. The questions we face are simple yet far-reaching – given this uncertainty, is now the right time to commit fully to the DDI opportunity? If so, what are the investments we should make to optimise regional economic growth? This SIA provides evidence that we must take concerted action now to best position the City Region to benefit from the disruptive effects of DDI. If investment is deferred, we run the risk of losing both competitiveness and output to other leading digital clusters that have the confidence to invest. -
Annual Report 2015/16
THE ALAN TURING INSTITUTE ANNUAL REPORT AND ACCOUNTS FOR THE PERIOD TO 31 March 2016 Tel: +44 (0) 300 770 1912 British Library Company number: 96 Euston Road 09512457 NW1 2DB Charity Number 1162533 [email protected] turing.ac.uk CONTENTS 04 Chair’s Foreword 05 Director’s Foreword 06 – 09 Trustees’ Report 10 – 22 Independent Auditors Report and Accounts Annual Report and Accounts 03 CHAIR’S FOREWORD formed a trading subsidiary and also Europe. This is a new source of risk for held interviews for our first cohort of all and has introduced an unexpected Turing research fellows. As I write this note of caution into our longer-term we are joined by thirty researchers plans. Like all organisations we for a summer programme and are will do our best to deal with the busily refurbishing space for the 120 consequences of this new uncertainty. or so researchers who will join us in the autumn at the start of our first full Starting the Institute has been academic year. enormously exciting and we are highly optimistic about what the This breathless pace could not Institute will achieve. We will work to have been sustained without achieve our vision to become a world enormous support from all concerned leader in data science research and throughout the UK data science innovation, and to be a great resource community. I would like to thank all for the UK’s data science community, of the colleagues who joined us on for its businesses and for its society secondment in the early months of more generally. -
Multilayer Modeling of Adoption Dynamics in Energy Demand Management
Multilayer modeling of adoption dynamics in energy demand management Cite as: Chaos 30, 013153 (2020); https://doi.org/10.1063/1.5122313 Submitted: 30 July 2019 . Accepted: 14 January 2020 . Published Online: 31 January 2020 Iacopo Iacopini , Benjamin Schäfer , Elsa Arcaute , Christian Beck , and Vito Latora COLLECTIONS Paper published as part of the special topic on Dynamics of Modern Power Grids Note: This paper is part of the Focus Issue on the Dynamics of Modern Power Grids. ARTICLES YOU MAY BE INTERESTED IN Functionability in complex networks: Leading nodes for the transition from structural to functional networks through remote asynchronization Chaos: An Interdisciplinary Journal of Nonlinear Science 30, 013105 (2020); https:// doi.org/10.1063/1.5099621 Merger of a Hénon-like attractor with a Hénon-like repeller in a model of vortex dynamics Chaos: An Interdisciplinary Journal of Nonlinear Science 30, 011105 (2020); https:// doi.org/10.1063/1.5144144 Uncertainty propagation in complex networks: From noisy links to critical properties Chaos: An Interdisciplinary Journal of Nonlinear Science 30, 023129 (2020); https:// doi.org/10.1063/1.5129630 Chaos 30, 013153 (2020); https://doi.org/10.1063/1.5122313 30, 013153 © 2020 Author(s). Chaos ARTICLE scitation.org/journal/cha Multilayer modeling of adoption dynamics in energy demand management Cite as: Chaos 30, 013153 (2020); doi: 10.1063/1.5122313 Submitted: 30 July 2019 · Accepted: 14 January 2020 · Published Online: 31 January 2020 View Online Export Citation CrossMark Iacopo Iacopini,1,2,3,a) -
Evaluating the Impact of Court Reform in England and Wales on Access to Justice
Developing the Detail: Evaluating the Impact of Court Reform in England and Wales on Access to Justice Report and recommendations arising from two expert workshops 1 Acknowledgments The author would like to thank Professor Dame Hazel Genn and Professor Abigail Adams for their helpful comments on an earlier draft. Suggested citation: Byrom, N (2019) “Developing the detail: Evaluating the Impact of Court Reform in England and Wales on Access to Justice” 2 Contents 1 Executive Summary ............................................................................................................................... 4 2 Background ............................................................................................................................................. 6 3 About the workshops ........................................................................................................................... 7 4 Scope of the recommendations ........................................................................................................... 8 5 Defining vulnerability ........................................................................................................................... 9 6 Monitoring fairness ............................................................................................................................. 12 7 Defining “Access to Justice”- An irreducible minimum standard ............................................... 14 8 Component 1: Access to the formal legal system .......................................................................... -
Hartree Centre Challenge Led Applied Research and Innovation Our Mission
The Hartree Centre Challenge led applied research and innovation Our mission Transforming UK industry by accelerating the adoption of high performance computing, big data and cognitive technologies. What we do − Collaborative R&D Define a challenge in your business and we build a team to deliver a solution − Platform as a service Give your own experts pay-as-you-go access to our compute power − Creating digital assets License the new industry-led software applications we create with IBM Research − Training and skills Drop in on our comprehensive programme of specialist training courses and events or design a bespoke course for your team Our platforms Our platforms Intel platforms Accelerated & emerging tech Bull Sequana X1000 Maxeler FPGA system Lenovo NeXtScale | 8,192 cores ARM 64-bit platform Lenovo System x iDataPlex system | 2048 cores Clustervision novel cooling demonstrator Intel Xeon Phi | Knight's Corner IBM big data analytics cluster | 288TB IBM data centric platforms IBM Power8 + NVLink + Tesla P100 IBM Power8 + Nvidia K80 How you can benefit − Access to domain specialists and a vast network of expertise − Technologies and systems that do it faster and cheaper − Full time scientists delivering on industrially relevant timescales − Collaboration with a trusted partner − Professional project management Government & public sector Network of expertise Local business networks Technology partners Universities Academia International research communities Our track record Case study | Computer aided formulation Collaborative -
Data Science and AI in the Age of COVID-19
Data science and AI in the age of COVID-19 Reflections on the response of the UK’s data science and AI community to the COVID-19 pandemic Executive summary Contents This report summarises the findings of a science activity during the pandemic. The Foreword 4 series of workshops carried out by The Alan message was clear: better data would Turing Institute, the UK’s national institute enable a better response. Preface 5 for data science and artificial intelligence (AI), in late 2020 following the 'AI and data Third, issues of inequality and exclusion related to data science and AI arose during 1. Spotlighting contributions from the data science 7 science in the age of COVID-19' conference. and AI community The aim of the workshops was to capture the pandemic. These included concerns about inadequate representation of minority the successes and challenges experienced + The Turing's response to COVID-19 10 by the UK’s data science and AI community groups in data, and low engagement with during the COVID-19 pandemic, and the these groups, which could bias research 2. Data access and standardisation 12 community’s suggestions for how these and policy decisions. Workshop participants challenges might be tackled. also raised concerns about inequalities 3. Inequality and exclusion 14 in the ease with which researchers could Four key themes emerged from the access data, and about a lack of diversity 4. Communication 17 workshops. within the research community (and the potential impacts of this). 5. Conclusions 20 First, the community made many contributions to the UK’s response to Fourth, communication difficulties Appendix A.