Composable code generation for high order, compatible finite element methods

It has been widely recognised in the HPC communities across the world, that exploiting modern computer architectures, including exascale machines, to a full extent requires software commu- nities to adapt their algorithms. Computational methods with a high ratio of floating point op- erations to bandwidth are favorable. For solving partial differential equations, which can model many physical problems, high order finite element methods can calculate approximations with a high efficiency when a good solver is employed. Matrix-free algorithms solve the corresponding equations with a high arithmetic intensity. Vectorisation speeds up the operations by calculating one instruction on multiple data elements. Another recent development for solving partial differential are compatible (mimetic) finite ele- ment methods. In particular with application to geophysical flows, compatible discretisations ex- hibit desired numerical properties required for accurate approximations. Among others, this has been recognised by the UK Met office and their new dynamical core for weather and climate fore- casting is built on a compatible discretisation. Hybridisation has been proven to be an efficient solver for the corresponding equation systems, because it removes some inter-elemental coupling and localises expensive operations. This thesis combines the recent advances on vectorised, matrix-free, high order finite element methods in the HPC community on the one hand and hybridised, compatible discretisations in the geophysical community on the other. In previous work, a code generation framework has been developed to support the localised linear algebra required for hybridisation. First, the framework is adapted to support vectorisation and further, extended so that the equations can be solved fully matrix-free. Promising performance results are completing the thesis.Open Acces

Algorithmic and infrastructural software development for cryo electron tomography

Many Cryo Electron Microscopy (cryoEM) software packages have accumulated significant technical debts over the years, resulting in overcomplicated codebases that are costly to maintain and that slow down development. In this thesis, we advocate for the development of open-source cryoEM core libraries as a solution to this debt and with the ultimate goal of improving the developer and user experience. First, a brief summary of cryoEM is presented, with an emphasis on projection algorithms and tomography. Second, the requirements of modern and future cryoEM image processing are discussed. Third, a new experimental cryoEM core library written in modern C++ is introduced. This library prioritises performance and code reusability, and is designed around a few core functions which offers an efficient model to manipulate multidimensional arrays at an index-wise and element-wise level. C++ template metaprogramming allowed us to develop modular and transparent compute backends, that provide great CPU and GPU performance, unified in an easy to use interface. Fourth, new projection algorithms will be described, notably a grid-driven approach to accurately insert and sample central slices in 3-dimensional (3d) Fourier space. A Fourier-based fused backward-forward projection, further improving the computational efficiency and accuracy of reprojections, will also be presented. Fifth, and as part of our efforts to test and showcase the library, we have started to implement a tilt series alignment package that gathers existing and new techniques into an automated pipeline. The current program first estimates the per-tilt translations and specimen stage rotation using a coarse alignment based on cosine stretching. It then fits the Thon rings of each tilt image as part of a global optimization to estimate the specimen inclination. Finally, we are using our Fourier-based fused reprojection to efficiently refine the per-tilt translations, and are starting to explore ways that would allow us to refine the per-tilt stage rotations

End-to-end deep reinforcement learning in computer systems

Abstract The growing complexity of data processing systems has long led systems designers to imagine systems (e.g. databases, schedulers) which can self-configure and adapt based on environmental cues. In this context, reinforcement learning (RL) methods have since their inception appealed to systems developers. They promise to acquire complex decision policies from raw feedback signals. Despite their conceptual popularity, RL methods are scarcely found in real-world data processing systems. Recently, RL has seen explosive growth in interest due to high profile successes when utilising large neural networks (deep reinforcement learning). Newly emerging machine learning frameworks and powerful hardware accelerators have given rise to a plethora of new potential applications. In this dissertation, I first argue that in order to design and execute deep RL algorithms efficiently, novel software abstractions are required which can accommodate the distinct computational patterns of communication-intensive and fast-evolving algorithms. I propose an architecture which decouples logical algorithm construction from local and distributed execution semantics. I further present RLgraph, my proof-of-concept implementation of this architecture. In RLgraph, algorithm developers can explore novel designs by constructing a high-level data flow graph through combination of logical components. This dataflow graph is independent of specific backend frameworks or notions of execution, and is only later mapped to execution semantics via a staged build process. RLgraph enables high-performing algorithm implementations while maintaining flexibility for rapid prototyping. Second, I investigate reasons for the scarcity of RL applications in systems themselves. I argue that progress in applied RL is hindered by a lack of tools for task model design which bridge the gap between systems and algorithms, and also by missing shared standards for evaluation of model capabilities. I introduce Wield, a first-of-its-kind tool for incremental model design in applied RL. Wield provides a small set of primitives which decouple systems interfaces and deployment-specific configuration from representation. Core to Wield is a novel instructive experiment protocol called progressive randomisation which helps practitioners to incrementally evaluate different dimensions of non-determinism. I demonstrate how Wield and progressive randomisation can be used to reproduce and assess prior work, and to guide implementation of novel RL applications

Optimal shape design with automatically differentiated CAD parametrisations

PhD ThesisTypical engineering workflow for aerodynamic design could be considered as a three-stage process: modelling of a new component in a CAD system, its detailed aerodynamic analysis on the computational grid using flow simulations (CFD) and manufacturing of the CAD component. Numerical shape optimisation is becoming an essential industrial method to improve the aerodynamic performance of shapes immersed in fluids. High-fidelity optimisation requires fine design spaces with many design variables, which can only be tackled with gradient-based optimisation methods. Adjoint CFD can efficiently calculate the necessary flow sensitivities on computational grids and ideally, also CAD parametrisation should be kept inside the loop to maintain a consistent CAD model during the optimisation and streamline the design process. However, (i) typical commercial CAD systems do not offer derivative computation and (ii) standard CAD parametrisations may not define a suitable design space for the optimisation. This thesis presents an automatically differentiated (AD) version of the open-source CAD kernel OpenCascade Technology (OCCT), which robustly provides shape derivatives with respect to CAD parameters. Developed block-vector AD mode outperforms commonly used finite difference approaches in both efficiency and accuracy. Coupling of OCCT with an adjoint CFD solver provides for the first time a fully differentiated design chain. Extension of OCCT to perform shape optimisation is demonstrated by using CAD parametrisations based on (a) user-defined parametric CAD models and (b) BRep (NURBS) models. The imposition of geometric constraints, a salient part of the industrial design, is shown for both approaches. Novel parametrisation techniques that can handle components with surface-surface intersections or simultaneously incorporate approaches (a) and (b) for the optimisation of a single component are demonstrated. The CAD-based methodology is successfully applied for aerodynamic shape optimisation of three industrial test cases. Additionally, advantages of the differentiated CAD is showcased for the commonly occurring CAD re-parametrisation and mesh-to-CAD fitting problems

Deep learning for fast and robust medical image reconstruction and analysis

Medical imaging is an indispensable component of modern medical research as well as clinical practice. Nevertheless, imaging techniques such as magnetic resonance imaging (MRI) and computational tomography (CT) are costly and are less accessible to the majority of the world. To make medical devices more accessible, affordable and efficient, it is crucial to re-calibrate our current imaging paradigm for smarter imaging. In particular, as medical imaging techniques have highly structured forms in the way they acquire data, they provide us with an opportunity to optimise the imaging techniques holistically by leveraging data. The central theme of this thesis is to explore different opportunities where we can exploit data and deep learning to improve the way we extract information for better, faster and smarter imaging. This thesis explores three distinct problems. The first problem is the time-consuming nature of dynamic MR data acquisition and reconstruction. We propose deep learning methods for accelerated dynamic MR image reconstruction, resulting in up to 10-fold reduction in imaging time. The second problem is the redundancy in our current imaging pipeline. Traditionally, imaging pipeline treated acquisition, reconstruction and analysis as separate steps. However, we argue that one can approach them holistically and optimise the entire pipeline jointly for a specific target goal. To this end, we propose deep learning approaches for obtaining high fidelity cardiac MR segmentation directly from significantly undersampled data, greatly exceeding the undersampling limit for image reconstruction. The final part of this thesis tackles the problem of interpretability of the deep learning algorithms. We propose attention-models that can implicitly focus on salient regions in an image to improve accuracy for ultrasound scan plane detection and CT segmentation. More crucially, these models can provide explainability, which is a crucial stepping stone for the harmonisation of smart imaging and current clinical practice.Open Acces

Groupwise non-rigid registration for automatic construction of appearance models of the human craniofacial complex for analysis, synthesis and simulation

Finally, a novel application of 3D appearance modelling is proposed: a faster than real-time algorithm for statistically constrained quasi-mechanical simulation. Experiments demonstrate superior realism, achieved in the proposed method by employing statistical appearance models to drive the simulation, in comparison with the comparable state-of-the-art quasi-mechanical approaches.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Deep learning applied to the semantic segmentation of tyre stockpiles

The global push for manufacturing which is environmentally sustainable has disrupted standard methods of waste tyre disposal. This push is further intensified by the health and safety risks discarded tyres pose to the surrounding population. Waste tyre recycling initiatives in South Africa are on the increase; however, there is still a growing number of undocumented tyre stockpiles developing throughout the country. The plans put in place to eradicate these tyre stockpiles have been met with collection, transport and storage logistical issues caused by the remoteness and distant locales. Eastwood (2016) aimed at optimising the logistics associated with collection, by estimating the number of visible tyres from images of tyre stockpiles. This research was limited by the need for manual segmentation of each tyre stockpile located within each image. This research proposes the use of semantic segmentation to automatically segment images of tyre stockpiles. An initial review of neural network, convolutional network and semantic segmentation literature resulted in the selection of Dilated Net as the semantic segmentation architecture for this research. Dilated Net builds upon the VGG-16 classification architecture to perform semantic segmentation. This resulted in classification experiments which were evaluated using precision, recall and f1-score. The results indicated that regardless of tyre stockpile image dimension, fairly accurate levels of classification accuracy can be attained. This was followed by semantic segmentation experiments which made use of intersection over union (IoU) and pixel accuracy to evaluate the effectiveness of Dilated Net on images of tyre stockpiles. The results indicated that accurate tyre stockpile segmentation regions can be obtained and that the trained model generalises well to unseen images

Groupwise non-rigid registration for automatic construction of appearance models of the human craniofacial complex for analysis, synthesis and simulation

Finally, a novel application of 3D appearance modelling is proposed: a faster than real-time algorithm for statistically constrained quasi-mechanical simulation. Experiments demonstrate superior realism, achieved in the proposed method by employing statistical appearance models to drive the simulation, in comparison with the comparable state-of-the-art quasi-mechanical approaches

Inference of transport phenomena in quantum devices

This thesis is concerned with charge transport in electrostatically defined quantum dot devices. Such devices display a wide range of transport phenomena in both open and closed configurations. The transport regime can be tuned experimentally by controlling the voltages applied to gate electrodes, but the precise electrostatic landscape which determines the transport regime is unknown. This uncertainty is given by variations in device fabrication, material defects, and sources of electrostatic disorder. The research chapters of this thesis consider a range of transport regimes in quantum dot devices, and infer properties of the device using both experimental and theoretical techniques. The first research chapter considers the detection of single charge transport events through a double quantum dot. By fitting an open quantum systems model to the sub-attoampere currents measured, tunnel rates are inferred. The second results chapter considers an electrostatic simulation of a quantum dot device and how it can be accelerated using deep learning. This accelerated model is then used in the third results chapter, along with experimental measurements of the transport regime, to inform a Bayesian inference algorithm and produce a set of disorder potentials to narrow the gap between simulation and reality. The final results chapter develops a differentiable quantum master equation solver which is used for parameter estimation in a theoretical study of transport in single and double quantum dots

Visual articulated tracking in cluttered environments

This thesis is concerned with the state estimation of an articulated robotic manipulator during interaction with its environment. Traditionally, robot state estimation has relied on proprioceptive sensors as the single source of information about the internal state. In this thesis, we are motivated to shift the focus from proprioceptive to exteroceptive sensing, which is capable to represent a holistic interpretation of the entire manipulation scene. When visually observing grasping tasks, the tracked manipulator is subject to visual distractions caused by the background, the manipulated object and by occlusions from other objects present in the environment. The aim of this thesis is to investigate and develop methods for the robust visual state estimation of articulated kinematic chains in cluttered environments which suffer from partial occlusions. To make these methods widely applicable to a variety of kinematic setups and unseen environments, we intentionally refrain from using prior information about the internal state of the articulated kinematic chain, and we do not explicitly model visual distractions such as the background and manipulated objects in the environment. We approach this problem with model-fitting methods, in which an articulated model is associated to the observed data using discriminative information. We explore model-fitting objectives that are robust to occlusions and unseen environments, methods to generate synthetic training data for data-driven discriminative methods, and robust optimisers to minimise the tracking objective. This thesis contributes (1) an automatic colour and depth image synthesis pipeline for data-driven learning without depending on a real articulated robot; (2) a training strategy for discriminative model-fitting objectives with an implicit representation of objects; (3) a tracking objective that is able to track occluded parts of a kinematic chain; and finally (4) a robust multi-hypotheses optimiser. These contributions are evaluated on two robotic platforms in different environments and with different manipulated and occluding objects. We demonstrate that our image synthesis pipeline generalises well to colour and depth observations of the real robot without requiring real ground truth labelled images. While this synthesis approach introduces a visual simulation-to-reality gap, the combination of our robust tracking objective and optimiser enables stable tracking of an occluded end-effector during manipulation tasks