Lattice Field Theory (LFT) provides the tools to study the fundamental forces of nature using numerical simulations. The traditional realm of application of LFT has been Quantum Chromodynamics (QCD), the theory describing the strong nuclear force within the Standard Model (SM) of particle physics. These calculations now include electromagnetic effects and achieve sub percent accuracy. Other applications span a wide range of topics, from theories beyond the Standard Model, to low-dimensional strongly coupled fermionic models, to new cosmological paradigms. At the core of this scientific endeavour lies the ability to perform sophisticated and demanding numerical simulations. The Exascale era of High Performance Computing therefore looks like a time of great opportunities. The UK LFT community has been at the forefront of the field for more than three decades and has developed a broad portfolio of research areas, with synergetic connections to High-Performance Computing, leading to significant progress in algorithms and code performance. Highlights of successes include: influencing the design of new hardware (Blue Gene systems); developing algorithms (Hybrid Monte Carlo) that are used widely by many other communities; maximising the benefits from new technologies (lattice QCD practitioners were amongst the first users of new platforms, including GPUs for scientific computing); applying LFT techniques to new problems in Artificial Intelligence. The research programme in LFT, and its impact, can be expanded in a transformative way with the advent of pre-Exascale and Exascale systems, but only if key challenges are addressed. As the number of floating point operations per second increases, the communications between computing nodes are lagging behind, and this imbalance will severely affect future LFT simulations across the board. These challenges are common to all LFT codebases, and more generally to other communities that are large users of HPC resources. The bottlenecks on new architectures need to be carefully identified, and software that minimises the communications must be designed in order to make the best usage of forthcoming large computers. As we are entering an era of heterogeneous architectures, the design of new software must clearly isolate the algorithmic progress from the details of the implementation on disparate hardware, so that our software can be deployed efficiently on forthcoming machines with limited effort. The goal of the EXA-LAT project is to develop a common set of best practices, KPIs and figures of merit that can be used by the whole LFT community in the near future and will inform the design and procurement of future systems. Besides the participation of the LFT community, numerous vendors and computing centres have joined the project, together with scholars from 'neighbouring' disciplines. Thereby we aim to create a national and international focal point that will foster the activity of scholars, industrial partners and Research Sotfware Engineers (RSEs). This synergetic environment will host training events for academics, RSEs and students, which will contribute to the creation of a skilled work force immersed in a network that comprises the leading vendors in the subject. EXA-LAT will set the foundations for a long-term effort by the LFT community to fully benefit of Exascale facilities and transfer some of the skills that characterise our scientific work to a wider group of users across disciplines.

SPH (smoothed particle hydrodynamics), and Lagrangian approaches to hydrodynamics in general, are a powerful approach to hydrodynamics problems. In this scheme, the fluid is represented by a large number of particles, moving with the flow. The scheme does not require a predefined grid making it very suitable for tracking flows with moving boundaries, particularly flows with free surfaces, and problems that involve flows with physically active elements or large dynamic range. The range of applications of the method is growing rapidly and is being adopted by a rapidly growing range of commercial companies including Airbus, Unilever, Shell, EDF, Michelin and Renault. The widespread use of SPH, and its potential for adoption across a wide range of science domains, make it a priority use case for the Excalibur project. Massively parallel simulations with billion to hundreds of billions of particles have the potential for revolutionising our understanding of the Universe and will empower engineering applications of unprecedented scale, ranging from the end-to-end simulation of transients (such as a bird strike) in jet engines to the simulation of tsunami waves over-running a series of defensive walls. The working group will identify a path to the exascale computing challenge. The group has expertise across both Engineering and Astrophysics allowing us to develop an approach that satisfies the needs of a wide community. The group will start from two recent codes that already highlight the key issues and will act as the working group's starting point. - SWIFT (SPH with Interdependent Fine-grained Tasking) implements a cutting-edge approach to task-based parallelism. Breaking the problem into a series of inter-dependent tasks allows for great flexibility in scheduling, and allows communication tasks to be entirely overlapped with communication. The code uses a timestep hierarchy to focus computational effort where is most need in response to the problems. - DualSPHysics draws its speed from effective use of GPU accelerators to execute the SPH operations on large groups of identical particles. This allows the code to gain from exceptional parallel execution. The challenge is to effectively connect multiple GPUs across large numbers of inter-connected computing nodes. The working group will build on these codes to identify the optimal approach to massively parallel execution on exa-scale systems. The project will benefit from close connections to the Excalibur Hardware Pilot working group in Durham, driving the co-design of code and hardware. The particular challenges that we will address are: - Optimal algorithms for Exascale performance. In particular, we will address the best approaches to the adaptive time-stepping and out-of-time integration, and adaptive domain decomposition. The first allows different spatial regions to be integrated forward in time optimally, the second allows the regions to be optimally distributed over the hardware. - Modularisation and Separation of Concerns. Future codes need to be flexible and modularised, so that a separation can be achieved between integration routines, task scheduling and physics modules. This will make the code future-proof and easy to adapt to new science domain requirements and computing hardware. - CPU/GPU performance optimisation. Next generation hardware will require specific (and possibly novel) techniques to be developed to optimally advance particles in the SPH scheme. We will build on the programming expertise gain in DualSPHysics to allow efficient GPU use across multiple nodes. - Communication performance optimisation. Separated computational regions need to exchange information at their boundaries. This can be done asynchronously, so that the time-lag of communication does not slow computation. While this has been demonstrated on current systems, the scale of Excalibur will overload current subsystems, and a new solution is needed.

The Peta-5 proposal from the University of Cambridge brings together 15 world-leading HPC system and application experts from 10 different institutions to lead the creation of a breakthrough HPC and data analytics capability that will deliver significant National impact to the UK research, industry and health sectors. Peta-5 aims to make a significant contribution towards the establishment and sustainability of a new EPSRC Tier 2 HPC network. The Cambridge Tier 2 Centre working in collaboration with other Tier 1, Tier 2 and Tier 3 stakeholders aims to form a coherent, coordinated and productive National e-Infrastructure (Ne-I) ecosystem. This greatly strengthened computational research support capability will enable a significant increase in computational and data centric research outputs, driving growth in both academic research discovery and the wider UK knowledge economy. The Peta-5 system will be one of the largest heterogeneous data intensive HPC systems available to EPSRC research in the UK. In order to create the critical mass in terms of system capability and capacity needed to make an impact at National level Cambridge have pooled funding and equipment resources from the University, STFC DiRAC and this EPSRC Tier 2 proposal to create a total capital equipment value of £11.5M; the request to EPSRC is £5M. The University will guarantee to cover all operational costs of the system for 4 years from the service start date, with the option to run for a fifth year to be discussed. Cambridge will ensure that 80% of the EPSRC funded element of Peta-5 is deployed on EPSRC research projects, with 65% of the EPSRC funded element of Peta-5 being made available to any UK EPSRC funded project free of charge by use of a light weight resource allocation committee, 15% going to Cambridge EPSRC research and 20% being sold to UK industry to drive the UK knowledge economy. The Peta-5 system will be the most capable HPC system in operation in the UK when it enters service in May 2017. In total Peta-5 will provide 3 petaflops (PF) of sustained performance derived from 3 heterogeneous compute elements, 1PF Intel X86, 1PF Intel KNL and 1PF NIVIDIA Pascal GPU (Peta-1) connected via a Pb/s HPC fabric (Peta-2) to an extreme I/O solid state storage pool (Peta-3), a petascale data analytics (Machine Learning + Hadoop) pool (Peta-4) and a large 15 PB tiered storage solution (Peta-5), all under a single execution environment. This creates a new HPC capability in the UK specifically designed to meet the requirements of both affordable petascale simulation and data intensive workloads combined with complex data analytics. It is the combination of these features which unlocks a new generation of computational science research. The core science justification for the Peta-5 service is based on three broad science themes: Materials Science and Computational Chemistry; Computational Engineering and Smart Cities; Health Informatics. These themes were chosen as they represent significant EPSRC research areas, which demonstrate large benefit from the data intensive HPC capability of Peta-5. The service will clearly be valuable for many other areas of heterogeneous computing and Data Intensive science. Hence a fourth horizontal thematic of "Heterogeneous - Data Intensive Science" is included. Initial theme allocation in the RAC will be: Materials 30%, Engineering 30%, Health, 20%, Heterogeneous - Data Intensive 20%. The Peta-5 facility will drive research discovery and impact at national level, creating the largest and most cost effective petascale HPC resource in the UK, bringing petascale simulation within the reach of a wide range of research projects and UK companies. Also Peta-5 is the first UK HPC system specifically designed for large scale machine learning and data analytics, combining the areas of HPC and Big Data, promising to unlock both knowledge and economic benefit from the Big Data revolution.

Advances in Artificial Intelligence (AI) and Machine Learning (ML) have enabled the scientific community to advance the frontiers of knowledge by learning from complex, large-scale experimental datasets. With the scientific community generating huge amounts of data from observatories to large-scale experimental facilities, AI for Science at Exascale is on the horizon. However, in the absence of systematic approaches to evaluate AI models and AI algorithms at exascale, the AI for Science community, and, in fact, the general AI community, are facing a major barrier ahead. This proposal aims to setup a working group with an overarching goal of identifying the scope and plans for developing AI benchmarks to enable the development of AI for Science at Exascale, in ExCALIBUR - Phase II. Although AI Benchmarking is becoming a well-explored topic, a number of issues are still to be addressed, including, but not limited to: a) There are no efforts aimed at AI benchmarking at exascale, particularly for science; b) A range of scientific problems involving real-world large-scale scientific datasets, such as those from experimental facilities or observatories, are largely ignored in benchmarking; and c) It is worth having benchmarks to serve as a catalogue of techniques offering template solutions to different types of scientific problems. In this proposal, when scoping the development of an AI benchmark suite, we will aim to address these issues. In developing a vision, a scope and a plan for this significant challenge, the working group will not only engage with the community of scientists from a number of disciplines, and industry, but will also engineer a scalable and functional AI benchmark, so as to learn and embed the practical aspects of developing an AI benchmark into the vision, scope, and plan. The exemplary benchmark will focus on removing noise from images, which is a common issue across multiple disciplines including, life sciences, material sciences and astronomy. The specific problems from each of these disciplines are, removing noise from cryogenic electron microscopic (cryo-em) datasets, denoising X-Ray tomographic images, and minimising the noise from weak lensing images, respectively.

Wearable neurotechnology utilization is expected to increase dramatically in the coming years, with applications in enabling movement-independent control and communication, rehabilitation, treating disease and improving health, recreation and sport among others. There are multiple driving forces:- continued advances in underlying science and technology; increasing demand for solutions to repair the nervous system; increase in the ageing population worldwide producing a need for solutions to age-related, neurodegenerative disorders, and "assistive" brain-computer interface (BCI) technologies; and commercial demand for nonmedical BCIs. There is a significant opportunity for the UK to lead in the development of AI-enabled neurotechnology R&D. There are a number of key challenges to be addressed, mainly associated with the complexity of signals measured from the brain. AI has the potential to revolutionise the neurotechnology industry and neurotechnology presents an excellent challenge for AI. This fellowship will build on the award-winning AI and neurotechnology research of the fellow and offer real potential for impact through established clinical partnerships and in the neurotechnology industry. The objective of this project is to build on award-winning AI and neurotechnology R&D to address key shortcomings of neurotechnology that limit its widespread use and adoption using a range of key neural network technologies in a state-of-the-art framework for processing neural signals developed by the proposed fellow. The AI technologies developed for neurotechnology will be applied across sectors to demonstrate translational AI through engagement with at least 10 companies across at least 5 sectors during the fellowship, to demonstrate societal and economic benefit and interdisciplinary and translational AI skills development. The project has multiple industry, clinical and academic partners and is expected to produce world-leading AI technologies and propel the fellow to world-leading status in developing AI for neurotechnology which will impact widely. A major focus of the project is ensuring the expectations of the fellow role are met. This includes:- -Ensuring the processes and resources are in place to build a world-leading profile by the end of the fellowship; -Focusing on planning research of the team as new results emerge and hypothesis are tested, to refine and develop a high-quality programme of ambitious, novel and creative research, in AI-enabled Neurotechnology. Specific focus will be ensuring meticulous planning, execution and follow-up to produce world-leading results; -Continuing to perform my leadership role as director of the ISRC and leader of the data analytics theme, expanding the team and actively seek to develop into a position of higher leadership of the research agenda at Ulster, and in the national and international research community; -Focusing on strengthening relationships and collaborations with colleagues in industry and academia, and maximising the potential for flexible career paths for researchers within the team -Acting as an ambassador and advocate for AI, science and ED&I including by continuing to actively provide opinions and engaging with questions around AI and ethics, and responsible research and innovation (RRI). A focus will be embedding this throughout the activities of the fellowship but across the region and internationally; -Seeking to engage with and influence the strategic direction of the UK AI research and innovation landscape through engagement with their peers, policymakers, and other stakeholders including the public through. -Ensuring that the fundamental research is developed to have a high likelihood of impact on UK society/economy through trials across a range of patient groups to develop the evidence base and transfer of intellectual property to products, in particular through NeuroCONCISE Ltd, a main project partner.