Our aim is to have impact creation embedded within all aspects of our research, to promote a strong impact culture, a have a diverse portfolio of impact activities and to many staff involved in impact creation.
Our strategies are:
A selection of recent and ongoing impact generation projects that are underpinned by Computer Science research follow.
ExaHyPE is an engine, i.e. a generic collection of state-of-the-art numerical ingredients (high-order time integration, high-order DG representations, block-structured Finite Volume methods, dynamically adaptive Cartesian meshes, task-based load-balancing, and so forth) to solve hyperbolic equation systems given in first-order formulation. The engine allows users to define what (equations) they want to solve, and then it decides how to solve them, where and in which order. It also commits to a particular set of numerical techniques. This degree of freedom on the application domain side in combination with the methodological focus opens the door for various algorithmic optimisations, as all ingredients can be tightly integrated and are aggressively optimised towards each other. Codes using the engine are used to implement solvers simulating various phenomena ranging from gravitational waves to tsunamis and earthquakes.
The code is currently used by multiple companies to assess their software stack and upcoming hardware generations, and it is the backbone of multiple ExCALIBUR research projects in Computer Science.
Contact: Tobias Weinzierl
MammalWeb (www.mammalweb.org) is a citizen-science project established by researchers at Durham University to monitor the UK’s mammals. Camera traps are deployed by members of the public to capture images of wildlife and the resultant images are uploaded to the MammalWeb platform where they are then classified online. MammalWeb’s objectives are to:
deliver policy-relevant data on biodiversity to the regional and national records centres in the UK
The project has collaborated with a range of partners including Durham Wildlife Trust, Great North Museum Hancock, Scottish National Heritage, NatureSpy, British Trust for Ornithology and HMP Deerbolt.
Contact: Steven Bradley
Durham Computer Science research on computer vision algorithms enables automated image understanding to provide long-term wide-area surveillance of dynamic scene objects (e.g. people, vehicles) addressing questions such as: “Is there anything there?” (detection); “What is it?” (classification); “Where is it?” (localization); and “What is it’s behaviour?” (tracking). This research, as part the SAPIENT programme, informs scientific work by the governments of UK, USA, Canada, Australia, New Zealand and Netherlands on wide-area, multi-sensor surveillance systems. Our research has contributed to £23.2 million investment in multi-sensor surveillance systems (UK/US government/industry), £11.3 million of additional commercial income to UK companies and supported the creation of around 55 additional science and engineering jobs across six organisations.
Contact: Toby Breckon
Durham Computer Science research on automatic and algorithmic prohibited item detection, using a range of computer vision techniques on both 2D X-ray and 3D Computed Tomography (CT) imagery, has directly informed UK/US government aviation security policy and provided new enhanced software capabilities for X-ray security scanners across 8 companies who supply the aviation and border security sector. Our work now directly contributes to the security of over 500 million passenger journeys per annum across five continents, with technology from Durham now available at an ever-increasing number of major international airports. The technology has commercial reach to 2-3 billion passenger journeys across 30+ countries globally, and will now help secure all air passengers attending the 2021 FIFA World Cup in Qatar.
Durham's Computer Science research on the use of automated image understanding techniques for future autonomous vehicles (driverless cars) addresses the two key algorithmic tasks within on-vehicle scene understanding: “Where am I?” (known as localization); and “What is around me?” (known as semantic scene understanding). The key challenge is to be able to address these tasks accurately, efficiently (i.e., in real-time relative to the vehicle speed) and robustly under varying environmental (weather) conditions. Our research in this area has directly informed the research and development at two of Europe's leading automotive manufacturers and supported the translation of road vehicle localization technology into rail where it now helps to protect 4.3 billion passenger journeys annually over around 57,000 km of track (Germany/UK).
In a project funded by the AHRC's Towards a National Collection program, Leonardo Impett from Durham Computer Science and Joasia Krysa from Liverpool John Moores University are working on machine-learning-powered curation in the 2020-21 Liverpool Biennial. The project looks at how the public interact differently with visual art events than they would with physical ones. In particular, it looks at how interaction might change when visitors no longer passively watch an event but actively participate in its curation. In an online edition of the 2020-21 Liverpool Biennial, visitors will co-curate the event with a machine learning algorithm, taking existing techniques beyond the “search engine” context in which they have mostly been used to date.
Contact: Leonardo Impett
Computer Science has a collaborative project with the NHS University Hospital of North Durham to reduce the ionising radiation exposure of CT scans through utilising deep neural networks. CT scans are expensive in terms of costs and availability, whereas deep generative neural networks are capable of rapidly reconstructing high-quality 3D CT-like images. The success and scalability of generating CT-like images from a small amount of 2D X-ray radiation exposure will have a significant impact on patients (thanks to reduced radiation exposure) and hospitals due to the prohibitively expensive nature of CT scans. Practitioners are obliged by law to consider available alternative techniques which have the same objective but expose patients to less ionising radiation.
Contact: Chris Willcocks
TechUP is a training programme focusing on training individuals from minority groups into tech careers. Working closely with industry, TechUP creates a programme tailored to industry-needs and participant learning experience.
Our most recent programme TechUPWomen took 100 women from the Midlands and North of England, particularly from underrepresented communities: BAME (54%); LGBTQ+ (21%); with disabilities (46%) or dependants (40), with degrees or experience in any subject area, retraining them in technology via a six-month online programme, developed in collaboration with industry, in preparation for roles as software developer, data scientist, agile project manager and business analyst. Our graduates have found new roles or promotions in a wide range of industries: including manufacturing (Jaguar Land Rover, MSP), software (Double Eleven Ltd), education (JISC, Code Nation), service (HR in One) and the public sector (Newcastle City Council, Durham Constabulary). TechUPWomen won the Employment and Skills category in the UK Impact Awards 2020.
Contact Sue Black and Alexandra Cristea
Durham University's Department of Computer Science is excited to host Intel's first United Kingdom (UK) oneAPI Academic Center of Excellence (CoE). The centre will conduct research around task-based programming using oneAPI development for heterogeneous architectures and will champion oneAPI training. Our goal is to extend the hyperbolic PDE engine ExaHyPE into a OneExaHyPE code that scales across a wide variety of GPU-accelerated machines. The algorithmic and methodological insights will be beneficial for many other simulation codes. Furthermore, the centre will organise workshops and tutorials around oneAPI cross-architecture programming. These will be open to Durham students and faculty, and available to colleagues from all over the UK and beyond.
ExaHyPE is an engine, i.e. a generic collection of state-of-the-art numerical ingredients (high-order time integration, high-order DG representations, block-structured Finite Volume methods, dynamically adaptive Cartesian meshes, task-based load-balancing, and so forth) to solve hyperbolic equation systems given in first-order formulation. The engine allows users to define what (equations) they want to solve, and then it decides how to solve them, where and in which order. It also commits to a particular set of numerical techniques. This degree of freedom on the application domain side in combination with the methodological focus opens the door for various algorithmic optimisations, as all ingredients can be tightly integrated and are aggressively optimised towards each other. Codes using the engine are currently used to implement solvers simulating various phenomena ranging from gravitational waves to tsunamis and earthquakes. Written originally under the umbrella of a FET HPC H2020 grant, the second generation of the code is a complete rewrite and a UK ExCALIBUR showcase code and backbone of an Excalibur Design and Development Working Group. The Exascale Computing ALgorithms and Infrastructures Benefiting UK Research (ExCALIBUR) programme's strategic vision is to prepare the UK for the exascale era. It also serves as one of three showcase codes for ExCALIBUR's cross-cutting task-parallelism theme.
ExaHyPE's compute core is entirely written in C++, and as of summer 2021, has a backend for OpenMP 5 to realise both its task-based parallelism and generic compute kernels in classic BSP style. It also offers OpenMP offloading to GPUs. This homogeneous software view of the hardware behind the code yields a maintainable, C++-only, vendor-independent code base. Through collaborations between scientists and research software engineers from Intel and Durham's Advanced Research Computing, the OneExaHyPE team at Durham's Department of Computer Science will develop an alternative oneAPI version. A oneAPI implementation will allow us to intermix the scheduling of data-parallel and task-parallel regions. In state-of-the-art codes, these two paradigms often compete with each other, while data-parallel regions are either completely deployed to a GPU, an FPGA, or not at all. In OneExaHyPE, we plan to study modes to balance between the parallelisation strategies, and to identify well-suited problem cardinalities for an accelerator on-the-fly.
"Current HPC codes often run efficiently either on multicore nodes or accelerators, but typically struggle to balance between the two paradigms and to get the best performance out of both architectures working together" says Tobias Weinzierl, Durham's Principal Investigator. "The added value and big promise behind oneAPI is that we get one programming model for all parts of the machine and then can let algorithms decide dynamically which steps of the code to run where."
ExaHyPE's new OneExaHyPE generation will provide opportunities to extend new research in astrophysics. The team around Weinzierl joined forces with colleagues from Durham's Institute for Computational Cosmology (ICC) to study new gravitational models and theories and to better understand how systems of binary black holes or neutron stars merge. Such mergers emit gravitational waves that we can measure, and serves as cosmological test benches for new theories. ExaHyPE's other traditional application is seismic waves, where achieving better performance and scalability will help scientists improve our understanding of these waves and their interplay with the actual fault geometries and material ruptures. The biggest promise behind OneExaHyPE's research, however, is the identification, validation and documentation of novel parallelisation patterns and concepts that will help many other codes to exploit the added value behind oneAPI: multi-architecture performance portable, scaling, maintainable code.
“Durham has always been a stronghold of theoretical computer science. It hosts three regional and national supercomputers for the University, DiRAC and the N8 (the N8 Research Partnership is a collaboration of the eight most research-intensive Universities in the North of England). This makes Durham a unique place to be for the enablement of next-generation research through [the embedding of] Exascale methods and technologies” clarifies Alan Real, head of Durham’s directorate of Advanced Research Computing (ARC). Durham also is home to international research institute flagships such as the ICC, whose research is relying on top-notch, large-scale computational infrastructure. Finally, the University recently started to invest into the provision and promotion of Research Software Engineers (RSE) - a role that offers an alternative to the classical academic career path for colleagues who want to focus on software as an enabling technology. It is the combination of theory- and algorithm-driven computer scientists, RSEs, compute centre specialists and team work with Intel engineers which will deliver the research programme.
The oneAPI endeavour also complements Durham's recent research projects funded under the UK's exascale programme ExCALIBUR. These include a project around task-based parallel programming in collaboration with Hartree and the MetOffice, two projects around ExaHyPE and the SPH code SWIFT (a former Intel Parallel Computing Centre code), as part of a Design and Development Working Group, along with novel hardware testbeds as part of ExCALIBUR's Hardware and Enabling Software activities.
The foundation and value of oneAPI cross-architecture programming training will be integrated into our postgraduate degree programmes. Durham's Computer Science is consistently ranked as one of the best places to study computer science in the UK. It recently established the Master in Scientific Computing and Data Analysis (MISCADA), while a consortium of Computer Science, ARC and DiRAC together started to include professional HPC training for postgraduate and professional colleagues from all over the UK. Together with Intel, the centre plans to deliver advanced workshops and lectures about task-based programming and SYCL-based accelerator usage using the Intel® oneAPI Toolkits.
oneAPI is an open, unified and cross-architecture programming model for CPUs and accelerator architectures (GPUs, FPGAs, and others). Based on standards, the programming model simplifies software development and delivers uncompromised performance for accelerated compute without proprietary lock-in, while enabling the integration of legacy code. With oneAPI, developers can choose the best accelerator architecture for the specific problem they are trying to solve without needing to rewrite software for the next architecture and platform.