There have not been many shake-ups in mainstream processor architectures, since von Neumann articulated their basic principles in 1945 and Hoff developed the microprocessor architecture in 1969. This is changing: field programmable technology has been adopted by major companies such as Microsoft and Intel for datacentre computing, and new architectures are expected which integrate processor cores and field programmable resources on the same chip. These developments are largely motivated by improvements in performance and energy efficiency of field programmable technology, which are so promising that industrial adoption takes place despite the significant challenge of developing applications for custom computing systems based on field programmable technology. Our vision is to address this challenge by advancing the foundation and applications of customisation, which involves developing hardware and software to fit design requirements. The proposed Platform project aims to pioneer new capabilities for enhancing design quality and designer productivity of custom computing systems, with potential to revolutionise many applications including those with needs for big data processing or for improved reliability and security. It builds on success of disruptive research funded by our previous Platform (EP/I012036/1). An example of such success is research in runtime reconfiguration of custom computing systems: we developed new analysis methods to enable reconfiguration to remove idle functions; we showed how reconfiguration can benefit many applications such as genomic data processing and finite-difference computation. Our work is disruptive since, in contrast to current focus on partial reconfiguration, it demonstrates that full reconfiguration can provide significant energy-efficient acceleration over conventional multicore and manycore processors reducing, for example, runtime of Bisulfite sequence alignment from hours to minutes for non-invasive prenatal and cancer diagnosis. Moreover, we invented the first field programmable architecture capable of single-cycle on-chip configuration generation, while current commercial devices are based on off-chip configuration generation that can take hours. Such exciting progress is only possible because the Platform Grant enabled high-risk research by researchers who would otherwise suffer from funding gaps: 12 Research Associates in our team enjoyed Platform support before they found permanent positions. Renewed Platform support will allow continuing development of our dynamic and ambitious research team to explore next-generation computer systems and their applications. The flexibility of the renewed Platform Grant will be used to address three new strategic areas, on which we are uniquely capable of making major impacts; we will conduct exploratory research to identify promising projects for responsive mode or other forms of funding: 1. Multi-level tradeoff-aware design automation, which includes investigating customisation strategies and the associated tradeoffs, automation of effective customisation strategies, and developing reusable demonstration facilities and testbeds. 2. Reconfigurable big data and cloud architectures, which include customisable big data processing, runtime design generation and optimisation, and domain-specific cloud optimisation. 3. Reliable system development life cycle, which includes codesign of reliable and resilient systems, high-coverage testing and verification strategies, and reliability and resilience life cycle management. The added-value aspects for this Platform Grant proposal include: (a) ensuring a critical mass of researchers in key areas, (b) exploring significant strategic areas, (c) contributing to research infrastructure, (d) attracting fresh talents, (e) pioneering and strengthening international collaborations, and (f) accelerating technology transfer.

Distributed systems are the essential elements that form the foundation for Internet infrastructure, and are critical for fulfilling the technological and societal needs of the digital age. Comprising Cloud datacenters, compute clusters, and the Internet of Things, these systems are responsible for the effective provisioning and execution of a multitude of parallelizable applications. The increased complexity and scale of these systems has resulted in the manifestation of emergent phenomena that substantially degrades overall system performance, and cannot be solved by simply increasing the number of compute nodes. This phenomena is known as The Long Tail Problem, whereby a small proportion of task stragglers - a small subset of tasks that execute abnormally slow - impede overall job completion time, and is systemic to all distributed systems that operate at sufficient scale. While work within this area attempts to address this problem through straggler detection or mitigation, their effectiveness is underpinned by understanding the precise underlying causes for straggler manifestation, and importantly determining what system conditions influence their occurrence. However achieving this understanding is incredibly challenging given the multitude of possible straggler root-causes - all of which can stem from diverse sub-system operational characteristics and their interactions with other sub-systems. As current understanding of straggler manifestation is restricted to a qualitative and high-level detail, it is presently impossible to determine what system operational conditions (e.g. cluster resource contention, temperature, failures) are highly likely to create a "perfect storm" for straggler occurrence. Determining the system conditions which influence the probability of straggler occurrence in different operational scenarios is vital towards achieving predictable and rapid parallel application execution, given the continued increase of system size and complexity. The vision of this proposed research is to address our limited understanding of straggler manifestation and conduct in-depth analysis and modelling of Internet-based distributed systems to quantify the precise relationship between straggler occurrence and system behaviour. This study will involve analysis and modelling stragglers within real systems, performed through comprehensive experimentation to identify and extract key system parameters from virtual and physical sub-system operation across the entire distributed system architecture. A framework will be constructed capable of automated analysis to determine straggler root-cause within production systems, which will interface with an event-based simulation engine for determining the optimal system conditions for avoiding stragglers. By working with leading international industrialists in massive-scale distributed systems, this work represents a significant step change towards solving The Long Tail Problem by providing much sought-out knowledge to truly understand straggler manifestation. As this problem is systemic across every type of large-scale distributed system, the impact of this work will have far reaching implications for both academia and industry, and will provide direct benefit to the competitiveness of the UKs digital economy within the short and long-term. This grant represents the first step towards realizing the research ambitious to scientifically understanding the operation of massive-scale Internet infrastructure, enabling the design of fault-tolerant techniques for future systems at unprecedented scale - a crucial objective towards realizing key emergent technologies for the future.

The Centre's themes align with the 'Towards A Data Driven Future' and 'Enabling Intelligence' priority areas, meeting the needs identified by UKRI to provide a highly skilled - and in demand - workforce focused on ensuring positive, human-centred benefits accrued from innovations in data driven and intelligence-based systems. The Centre has a distinct and methodologically challenging "people-first" perspective: unlike an application-orientated approach (where techniques are applied to neatly or simplistically defined problems, sometimes called "solutionism"), this lens will ensure that intense, multi-faceted and iterative explorations of the needs, capabilities and values of people, and wider societal views, challenge and disrupt computational science. In a world of big data and artificial intelligence, the precious smallness of real individuals with their values and aspirations are easily overlooked. Even though the impact of data-driven approaches and intelligence are only beginning to be felt at a human scale, there are already signs of concern over what these will mean for life, with governments and others worldwide addressing implications for education, jobs, safety and indeed even what is unique in being human. Sociologists, economists and policy makers of course have a role in ensuring positive outcomes for people and society of data-driven and intelligence systems; but, computational scientists have a pivotal duty too. Our viewpoint, then, will always see the human as a first-class citizen in the future physical-digital world, not perceiving themselves as outwitted, devalued or marginalised by the expanding capabilities of machine computation, automation and communication. Swansea and the wider region of Wales is a place and community where new understandings of data science and machine intelligence are being formed within four challenging contexts defined in the Internet Coast City Deal: Life Science and Well-being; Smart Manufacturing; Smart and Sustainable Energy; and Economic Acceleration. Studies commissioned by the City Deal and BEIS evidence the science and innovation strengths in Swansea and region in these areas and indicate how transformational investments in these areas will be for the region and the UK. Our Centre will, then, immerse cohorts in these contexts to challenge them methodologically and scientifically. The use of data-driven and intelligence systems in each of the four contexts gives rise to security, privacy and wider ethical, legal, governance and regulatory issues and our Centre also has a cross-cutting theme to train students to understand, accommodate and shape current and future developments in these regards. Cohort members will work to consider how the Centre's challenge themes direct and drive their thinking about data and intelligence, benefitting from both the multidisciplinary team that have built strong research agendas and connections with each of the contexts and the rich set of stakeholders that are our Centre has assembled. Importantly, a process of pivoting between challenge themes will be applied: insights, methods and challenges from one theme and its research projects will be tested and extended in others with the aim of enriching all. These, along with several other mechanisms (such as intra- and inter-cohort sandpits and side projects) are designed to develop a powerful bonding and shaping "cohort effect". The need for and value of our Centre is evidenced by substantial external industrial investment we have have secured: £1,750,000 of cash and £4,136,050 in-kind (total:£5,886,050). These partners and stakeholders have helped create the vision and detail of the proposal and include: Vint Cerf ("father of the internet" and Vice President of Google); NHS; Pfizer; Tata Steel; Ford; QinetiQ; McAfee; Ordnance Survey; Facebook; IBM; Microsoft; Fujitsu; Worshipful Company of IT Spiritual and Ethical Panel; and, Vicki Hanson (CEO, Association of Computing Machinery).

The main theme that underlies this research project is automatedreasoning, an applied sub-discipline of mathematical logic. Logichas found applications in many areas of computer sciencesuch as the verification of digital circuits, reasoning aboutprograms and knowledge representation. One of the most fundamentalaspects in this context is to automatically decide whether aparticular formula is a logical consequence of a given set ofassumptions. The set of assumptions may describe complex relationsbetween diseases and their symptoms, and one possible reasoning taskwould be to confirm or reject a diagnosis based on observed symptomsand medical history.In this research project, we investigate applications ofmathematical logic in knowledge representation. One of the primechallenges in this area is to design logical formalisms that strikea balance between the two conflicting goals of expressiveness (theability to formally represent the application domain) andcomputational tractability. The family of modal logics, conceived ina broad way, combines both aspects and serves as the mathematicalfoundation of a large number of knowledge representation formalisms.The core ingredient of modal logic is the possibility to qualifylogical assertions to hold in a certain way. Depending on thecontext, we may for instance stipulate that assertion holds `alwaysin the future', `with a likelihood of at least 50%' or `normally'.Together with names for individual entities, this allows us toformulate assertions like `the likelihood of congestion on Queen'sRoad is greater than 30%', and complex knowledge bases arise bycombining different logical primitives. Automated reasoning thenallows us to mechanically verify e.g. the consistency of scientifichypotheses against an existing knowledge base. Our goal is to builda modular and practical knowledge representation system that allowsto represent and reason about knowledge represented in this way,based on a large and diverse class of logical primitives, includinge.g. the coalitional behaviour of agents, quantitative uncertainty,counterfactual reasoning and default assumptions. This goes waybeyond the current state of the art, where only logical primitiveswith a relational interpretation are supported by automated tools.Recent research has shown these new logical features can beaccounted for in a uniform way by passing to a more generalmathematical model, known as `coalgebraic semantics'. This richerframework does not only provide a uniform umbrella for a largenumber of reasoning principles, but also supports a richmathematical theory that has by now matured to the extent which putsthe development of automated tools within reach. The researchchallenge that this proposal addresses is the further development of thesetheoretical results as to bring them to bear on practical applications.As a concrete case study, we will use the Cool system to formalisequantitative models in Systems Biology.

Until relatively recently the processing speed of computer systems increased at an exponential rate. Each year it was possible to use computers to solve computational problems that the previous year were out of reach. Around 2005, physical limits stopped this trend: it became infeasible to increase the clock rate of a processor without consuming an exorbitant amount of energy. To counter this, processor manufacturers have since aimed to provide increased performance by designing "multicore" processors, which consist of two or more processing units on a chip. Many computational tasks are parallelisable, in which case they can be distributed across the cores of a multicore processor. Recently there has been a trend towards "many-core" processors, with hundreds or thousands of processing elements. For highly parallel applications, many-core processors can offer a massive speedup. The most readily available many-core processors are graphics processing units (GPUs) from companies such as NVIDIA, AMD, Intel, ARM and Imagination Technologies. GPUs were originally designed to accelerate graphical computations, but have become sufficiently general purpose for accelerating computational tasks in a variety of domains, including financial analysis, medical imaging, media processing and simulation. GPUs are programmed by writing a "kernel" function, a program which will be executed by hundreds or thousands of threads running in parallel on the GPU. Parallel threads can communicate using shared memory, and synchronise using barrier statements. Writing correct GPU kernels is more challenging than writing sequential software due to two main problems: data races and barrier divergence. A data race occurs when two GPU threads access a shared memory location, at least one of the threads modifies this location, and no barrier statement separates the accesses. Data races almost always signify bugs in the kernel and lead to nondeterministic behaviour. Barrier divergence occurs when distinct threads reach different barrier statements in the kernel, and leads to deadlock. Because GPUs are becoming widely used for general purpose software development, there is an urgent need for analysis techniques to help GPU programmers write correct code. Techniques to analyse GPU kernels with respect to data races and barrier divergence would significantly speed up the GPU software development process, leading to shorter time-to-market for GPU-accelerated applications. In this project we plan to design formal techniques for verifying race- and divergence-freedom for GPU kernels. To be adopted and trusted by industrial practitioners our techniques must be highly automatic, scalable, and based on rigorous semantic foundations. We plan to achieve these aims by developing a rigorous GPU memory model specification, and a formal semantics for GPU kernel execution that makes no assumptions about the structure of the kernel to be analysed. Based on these semantic foundations, we will design a verification technique that aims to prove absence of data races and barrier divergence by generating a "contract" for the kernel: a machine-checkable proof that kernel execution cannot lead to these defects. Contract-based verification is modular - each kernel procedure is analysed separately - and thus scalable. We will design a template-based contract generation method that captures domain-specific knowledge about common GPU programming idioms. This will allow efficient verification of GPU kernels that use typical data access patterns. For more intricate kernels that implement highly optimised algorithms, we will design a method based on Craig interpolation. This method will construct a proof of race- and divergence-freedom up to a bounded execution depth, and then attempt to extract a general contract from this proof. Throughout, we will evaluate our methods using open source and industrial GPU kernels, including kernels provided by our industrial collaborators.