My proposed Fellowship will revolutionise the use of High Performance Computing (HPC) within The University of Sheffield by changing perceptions of how people utilise software and are trained and supported in writing code which scales to increasingly large computer systems. I will provide leadership by demonstrating the effectiveness of specific research software engineer roles, and by growing a team of research software engineer at The University of Sheffield in order to accommodate our expanding programme of research computing. I will achieve this by: 1) developing the FLAME and FLAME GPU software to facilitate and demonstrate the impact of Graphics Processing Unit (GPU) computing on the areas of complex systems simulation; 2) vastly extending the remit of GPUComputing@Sheffield to provide advanced training and research consultancy, and to embed specific software engineering skills for high-performance data parallel computing (with GPUs and Xeon Phis) across EPSRC-remit research areas at The University of Sheffield. My first activity will enable long-term support of the extensive use of FLAME and FLAME GPU for EPSRC, industry and EU-funded research projects. The computational science and engineering projects supported will include those as diverse as computational economics, bioinformatics and transport simulation. Additionally, my software will provide a platform for more fundamental computer science research into complexity science, graphics and visualisation, programming languages and compilers, and software engineering. My second activity will champion GPU computing within The University of Sheffield (and beyond to its collaborators and industrial partners). It will demonstrate how a specific area of research software engineering can be embedded into The University of Sheffield, and act as a model for further improvement in areas such as research software and data storage. I will change the way people develop and use research software by providing training to students and researchers who can then embed GPU software engineering skills across research domains. I will also aid researchers who work on computationally demanding research by providing software engineering consultancy in areas that can benefit from GPU acceleration, such as, mobile GPU computing for robotics, deep neural network simulation for machine learning (including speech, hearing and Natural language processing) and real time signal processing. The impact of my Fellowship will vastly expand the scale and quality of research computing at The University of Sheffield, embed skills within students and researchers (with long-term and wide-reaching results) and ensure energy-efficient use of HPC. This will promote the understanding and wider use of GPU computing within research, as well as transitioning researchers to larger regional and national HPC facilities. Ultimately my research software engineer fellowship will facilitate the delivery of excellent science whilst promoting the unique and important role of the Research Software Engineer.

Wave phenomena as they arise from conservation laws are omnipresent in computational sciences, and codes simulating them typically ask for enormous compute power. However, few mathematicians and modellers have code at hand that allows them to evaluate their ideas straightforwardly on peta- and exascale machines, many wave equation solvers do not a fit to heterogeneous (GPU) hardware, and many wave simulations will require exascale capabilities from time to time, yet not 24/7. The community thus runs risk to fall into a sophistication gap, where the scaling software does not incorporate the latest numerical and algorithmic research, while the latest models and numerics are not scaled up. It runs risk to fall into a heterogeneity gap, where the particular hardware configuration that drives exascale is not appropriately supported by the software. It runs risk to fall into an economic disproportionality gap, where compute centres struggle to make the case to grant a project full machine access as its code base cannot exploit the machine efficiently. We propose to extend the FETHPC H2020 code ExaHyPE into a software called ExaClaw which tackles these risks. ExaClaw will couple the leading grid-based toolbox to model wave phenomena, ClawPack, to the scaling, high-performance ADER-DG AMR engine ExaHyPE, will be able to deploy compute-intense calculations to GPUs, and the team behind ExaClaw will prototype a new supercomputer usage scheme well-suited to accommodate bursts of extreme compute hunger. These activities pair up with community building and the release of three ExaClaw demonstrators. This makes ExaClaw a high-profile ExCALIBUR use case. ExaHyPE is an engine to write solvers for grid-based, first-order hyperbolic PDE equations. It supports block-structured Finite Volume schemes and ADER-DG, and it realises a clear separation of concern to support any application domain. User feed application domain knowledge such as flux functions, eigenvalues, initial values or refinement criteria into the engine. The engine then runs and orchestrates the actual computation. Mesh traversal, refinement, parallelisation, load balancing, limiting, and so forth all are hidden from the user. Internally, the code employs a small set of premanufactured Riemann solvers. They can be replaced by custom user implementations. To widen the engine's applicability and productivity, ExaClaw will supplement ExaHyPE's Riemann solvers with solvers from the ClawPack suite. ClawPack is the biggest open source repository for explicit wave equation system solvers, and it comprises a huge variety of well-studied, bespoke Riemann solvers for various application domains. ExaHyPE realises a task decomposition where one particular task type dominates the runtime. This type exhibits a high arithmetic intensity and will be deployed to GPUs through various technologies (OpenMP, OpenACC, OneAPI). Instead of GPUs as workhorse slaves, ExaClaw's GPUs steal their jobs actively from the compute nodes, i.e.~they are in charge of their own workload. This establishes the notion of a skeleton hardware, where GPUs or other accelerators can be dynamically added or removed to a supercomputer run, and code inherently fits to different hardware configurations. Finally, ExaClaw will investigate into a novel HPC usage scheme where the load balancing minimises the number of used machine nodes. If the workload of a run however becomes massive (due to adaptive mesh refinement, e.g.), ExaClaw will be able to book further resources dynamically. The project abandons a static hardware-to-run association and allows multiple simulations to argue with each other which one should have the biggest share of resources. Simulations thus can have (close to) full machine access when they need it, but release resources whenever their demand decreases again.

We propose to establish a new national Tier-2 HPC service ("NICE19") by purchasing and operating a novel architecture supercomputer, based upon IBM Power9 CPU and Nvidia Volta GPUs. This architecture is used in both #1 and #2 of the most recent list of the Top500 supercomputers in the world, within the USA government lab-based SUMMIT and SIERRA supercomputers. Currently this architecture is not widely available in the UK, so by establishing a national Tier-2 facility we will add significant diversity and capability to the UK e-infrastructure. This architecture supports memory coherence between the GPU and CPU and a hierarchy of interconnects to allow effective distributed GPU use, extending problem sizes that can tackled beyond that of other GPU-accelerated architectures, increasing data sizes for accelerated simulation and analysis codes, and reducing the 'time to science' for a range of 'hard' problems. The purpose of this facility is to enable new science across many different disciplines. Our primary focus will be on experimental users who generate large data sets that need analysis (e.g. cyro-EM facilities) and for modellers who use machine learning. By supporting and bringing together these two communities, there will be many opportunities for new and exciting science. This will bring HPC to many communities who have not engage with HPC before now, with many benefits in getting better value out of experiments and existing datasets. In addition, existing investments in network infrastructure for the DiRAC consortium of STFC users will be leveraged to connect the facility to experimental facilities and other Tier-2 centres for optimal data flows between sites. This proposal is led by the N8 Centre of Excellence in Computationally Intensive Research (N8-CIR) and supported by the N8 partnership. This will provide 8 FTE of Research Software Engineers (RSEs) to support these communities, port and optimize their software for this new platform and train users, which will be essential to maximize the benefit of this novel facility. These codes will then be able to run efficiently on the facility, supported by trained users. The architectural similarity with SUMMIT and SIERRA will also provide a route to exascale computing. This proposal will therefore have a long-lasting impact, beyond the lifetime fo this facility.

There are currently unprecedented amounts of biological data that need to be analysed to advance our understanding of the biological sciences. This is due to the increase in large-scale, high-throughput research projects in genomics and post-genomics, of which the Human Genome Project is the best known. This leads to an increase in data volumes that need to be stored, analysed and curated. Consequently, greater computing capacity is required for research at the forefront of international research. The types of questions we now wish to address involve wide-scale comparisons, such as comparing genomes from different organisms and require considerable computing infrastructure. Naturally, the limits of what can be addressed are dependent on computing support. As a group of researchers, we propose to develop and apply new computational methods to study a whole range of biological problems. We will study how DNA is organised in the cell, and how it gives rise to cellular functions. We aim to understand how the agents of action in a cell (proteins) interact to give rise to complex biological systems, and how these interactions and other functions are dependent on the three-dimensional shapes of each of the interacting molecular components. We will compare cellular components and systems from different organisms to understand how they evolve. Only through comparison of many sets of data, often from several organisms, can a new understanding of general trends and characteristics of biological organisms be obtained.

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