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SYCL compute kernels for ExaHyPE (2024)
Presentation / Conference Contribution
Loi, C. M., Bockhorst, H., & Weinzierl, T. (2024, March). SYCL compute kernels for ExaHyPE. Presented at 2024 SIAM Conference on Parallel Processing for Scientific Computing (PP), Baltimore, MD

We discuss three SYCL realisations of a simple Finite Volume scheme over multiple Cartesian patches. The realisation flavours differ in the way how they map the compute steps onto loops and tasks: We compare an implementation that is exclusively usin... Read More about SYCL compute kernels for ExaHyPE.

Exact-NeRF: An Exploration of a Precise Volumetric Parameterization for Neural Radiance Fields (2023)
Presentation / Conference Contribution
Isaac-Medina, B., Willcocks, C., & Breckon, T. (2023, June). Exact-NeRF: An Exploration of a Precise Volumetric Parameterization for Neural Radiance Fields. Presented at IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023, Vancouver, BC

Neural Radiance Fields (NeRF) have attracted significant attention due to their ability to synthesize novel scene views with great accuracy. However, inherent to their underlying formulation, the sampling of points along a ray with zero width may res... Read More about Exact-NeRF: An Exploration of a Precise Volumetric Parameterization for Neural Radiance Fields.

Efficient GPU Offloading with OpenMP for a Hyperbolic Finite Volume Solver on Dynamically Adaptive Meshes (2023)
Presentation / Conference Contribution
Wille, M., Weinzierl, T., Brito Gadeschi, G., & Bader, M. (2023, December). Efficient GPU Offloading with OpenMP for a Hyperbolic Finite Volume Solver on Dynamically Adaptive Meshes. Presented at ISC High Performance 2023, Hamburg

We identify and show how to overcome an OpenMP bottleneck in the administration of GPU memory. It arises for a wave equation solver on dynamically adaptive block-structured Cartesian meshes, which keeps all CPU threads busy and allows all of them to... Read More about Efficient GPU Offloading with OpenMP for a Hyperbolic Finite Volume Solver on Dynamically Adaptive Meshes.

Computational graphs for matrix functions (2023)
Journal Article
Jarlebring, E., Fasi, M., & Ringh, E. (2023). Computational graphs for matrix functions. ACM Transactions on Mathematical Software, 48(4), 1-35. https://doi.org/10.1145/3568991

Many numerical methods for evaluating matrix functions can be naturally viewed as computational graphs. Rephrasing these methods as directed acyclic graphs (DAGs) is a particularly effective approach to study existing techniques, improve them, and ev... Read More about Computational graphs for matrix functions.

CPFloat: A C library for simulating low-precision arithmetic (2023)
Journal Article
Fasi, M., & Mikaitis, M. (2023). CPFloat: A C library for simulating low-precision arithmetic. ACM Transactions on Mathematical Software, 49(2), 1-32. https://doi.org/10.1145/3585515

One can simulate low-precision floating-point arithmetic via software by executing each arithmetic operation in hardware and then rounding the result to the desired number of significant bits. For IEEE-compliant formats, rounding requires only standa... Read More about CPFloat: A C library for simulating low-precision arithmetic.

Matrix Multiplication in Multiword Arithmetic: Error Analysis and Application to GPU Tensor Cores (2023)
Journal Article
Fasi, M., Higham, N. J., Lopez, F., Mary, T., & Mikaitis, M. (2023). Matrix Multiplication in Multiword Arithmetic: Error Analysis and Application to GPU Tensor Cores. SIAM Journal on Scientific Computing, 45(1), https://doi.org/10.1137/21M1465032

In multiword arithmetic, a matrix is represented as the unevaluated sum of two or more lower precision matrices, and a matrix product is formed by multiplying the constituents in low precision. We investigate the use of multiword arithmetic for impro... Read More about Matrix Multiplication in Multiword Arithmetic: Error Analysis and Application to GPU Tensor Cores.

Dynamic Unary Convolution in Transformers (2023)
Journal Article
Duan, H., Long, Y., Wang, S., Zhang, H., Willcocks, C. G., & Shao, L. (2023). Dynamic Unary Convolution in Transformers. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(11), 12747 - 12759. https://doi.org/10.1109/tpami.2022.3233482

It is uncertain whether the power of transformer architectures can complement existing convolutional neural networks. A few recent attempts have combined convolution with transformer design through a range of structures in series, where the main cont... Read More about Dynamic Unary Convolution in Transformers.

Denoising Diffusion Probabilistic Models on SO(3) for Rotational Alignment (2022)
Presentation / Conference Contribution
Leach, A., Schmon, S. M., Degiacomi, M. T., & Willcocks, C. G. (2022, April). Denoising Diffusion Probabilistic Models on SO(3) for Rotational Alignment. Presented at ICLR 2022 Workshop on Geometrical and Topological Representation Learning

Probabilistic diffusion models are capable of modeling complex data distributions on high-dimensional Euclidean spaces for a range applications. However, many real world tasks involve more complex structures such as data distributions defined on mani... Read More about Denoising Diffusion Probabilistic Models on SO(3) for Rotational Alignment.

Unleashing Transformers: Parallel Token Prediction with Discrete Absorbing Diffusion for Fast High-Resolution Image Generation from Vector-Quantized Codes (2022)
Presentation / Conference Contribution
Bond-Taylor, S., Hessey, P., Sasaki, H., Breckon, T., & Willcocks, C. (2022, October). Unleashing Transformers: Parallel Token Prediction with Discrete Absorbing Diffusion for Fast High-Resolution Image Generation from Vector-Quantized Codes. Presented at ECCV 2022: European Conference on Computer Vision, Tel Aviv, Israel

Whilst diffusion probabilistic models can generate high quality image content, key limitations remain in terms of both generating high-resolution imagery and their associated high computational requirements. Recent Vector-Quantized image models have... Read More about Unleashing Transformers: Parallel Token Prediction with Discrete Absorbing Diffusion for Fast High-Resolution Image Generation from Vector-Quantized Codes.

MedNeRF: Medical Neural Radiance Fields for Reconstructing 3D-aware CT-Projections from a Single X-ray (2022)
Presentation / Conference Contribution
Corona-Figueroa, A., Frawley, J., Bond-Taylor, S., Bethapudi, S., Shum, H. P., & Willcocks, C. G. (2022, July). MedNeRF: Medical Neural Radiance Fields for Reconstructing 3D-aware CT-Projections from a Single X-ray. Presented at 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Glasgow, Scotland

Computed tomography (CT) is an effective med-ical imaging modality, widely used in the field of clinical medicine for the diagnosis of various pathologies. Advances in Multidetector CT imaging technology have enabled additional functionalities, inclu... Read More about MedNeRF: Medical Neural Radiance Fields for Reconstructing 3D-aware CT-Projections from a Single X-ray.

Multi-view Vision Transformers for Object Detection (2022)
Presentation / Conference Contribution
Isaac-Medina, B., Willcocks, C., & Breckon, T. (2022, August). Multi-view Vision Transformers for Object Detection. Presented at International Conference on Pattern Recognition, Montreal, Canada

The Dynamical Functional Particle Method for Multi-Term Linear Matrix Equations (2022)
Journal Article
Dmytryshyn, A., Fasi, M., & Gulliksson, M. (2022). The Dynamical Functional Particle Method for Multi-Term Linear Matrix Equations. Applied Mathematics and Computation, 435, Article 127458. https://doi.org/10.1016/j.amc.2022.127458

Recent years have seen a renewal of interest in multi-term linear matrix equations, as these have come to play a role in a number of important applications. Here, we consider the solution of such equations by means of the dynamical functional particl... Read More about The Dynamical Functional Particle Method for Multi-Term Linear Matrix Equations.

A multiresolution Discrete Element Method for triangulated objects with implicit time stepping (2022)
Journal Article
Noble, P., & Weinzierl, T. (2022). A multiresolution Discrete Element Method for triangulated objects with implicit time stepping. SIAM Journal on Scientific Computing, 44(4), A2121-A2149. https://doi.org/10.1137/21m1421842

Simulations of many rigid bodies colliding with each other sometimes yield particularly interesting results if the colliding objects differ significantly in size and are nonspherical. The most expensive part within such a simulation code is the colli... Read More about A multiresolution Discrete Element Method for triangulated objects with implicit time stepping.

Spherical accretion of collisional gas in modified gravity I: self-similar solutions and a new cosmological hydrodynamical code (2022)
Journal Article
Zhang, H., Weinzierl, T., Schulz, H., & Li, B. (2022). Spherical accretion of collisional gas in modified gravity I: self-similar solutions and a new cosmological hydrodynamical code. Monthly Notices of the Royal Astronomical Society, 515(2), 2464-2482. https://doi.org/10.1093/mnras/stac1991

The spherical collapse scenario has great importance in cosmology since it captures several crucial aspects of structure formation. The presence of self-similar solutions in the Einstein-de Sitter (EdS) model greatly simplifies its analysis, making i... Read More about Spherical accretion of collisional gas in modified gravity I: self-similar solutions and a new cosmological hydrodynamical code.

Cross-modal Image Synthesis in Dual-Energy X-Ray Security Imagery (2022)
Presentation / Conference Contribution
Isaac-Medina, B., Bhowmik, N., Willcocks, C., & Breckon, T. (2022, June). Cross-modal Image Synthesis in Dual-Energy X-Ray Security Imagery. Presented at 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), New Orleans, Louisiana

Dual-energy X-ray scanners are used for aviation security screening given their capability to discriminate materials inside passenger baggage. To facilitate manual operator inspection, a pseudo-colouring is assigned to the effective composition of th... Read More about Cross-modal Image Synthesis in Dual-Energy X-Ray Security Imagery.

AnoDDPM: Anomaly Detection With Denoising Diffusion Probabilistic Models Using Simplex Noise (2022)
Presentation / Conference Contribution
Wyatt, J., Leach, A., Schmon, S. M., & Willcocks, C. G. (2022, June). AnoDDPM: Anomaly Detection With Denoising Diffusion Probabilistic Models Using Simplex Noise. Presented at 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, New Orleans, LA

Generative models have been shown to provide a powerful mechanism for anomaly detection by learning to model healthy or normal reference data which can subsequently be used as a baseline for scoring anomalies. In this work we consider denoising diffu... Read More about AnoDDPM: Anomaly Detection With Denoising Diffusion Probabilistic Models Using Simplex Noise.

Dynamic task fusion for a block-structured finite volume solver over a dynamically adaptive mesh with local time stepping (2022)
Book Chapter
Li, B., Schulz, H., Weinzierl, T., & Zhang, H. (2022). Dynamic task fusion for a block-structured finite volume solver over a dynamically adaptive mesh with local time stepping. In High Performance Computing 37th International Conference, ISC High Performance 2022, Hamburg, Germany, May 29 – June 2, 2022, Proceedings (153-173). Springer Verlag. https://doi.org/10.1007/978-3-031-07312-0_8

Load balancing of generic wave equation solvers over dynamically adaptive meshes with local time stepping is dicult, as the load changes with every time step. Task-based programming promises to mitigate the load balancing problem. We study a Finite V... Read More about Dynamic task fusion for a block-structured finite volume solver over a dynamically adaptive mesh with local time stepping.

Stochastic rounding: implementation, error analysis and applications (2022)
Journal Article
Croci, M., Fasi, M., Higham, N. J., Mary, T., & Mikaitis, M. (2022). Stochastic rounding: implementation, error analysis and applications. Royal Society Open Science, 9(3), Article 211631. https://doi.org/10.1098/rsos.211631

Stochastic rounding (SR) randomly maps a real number x to one of the two nearest values in a finite precision number system. The probability of choosing either of these two numbers is 1 minus their relative distance to x. This rounding mode was first... Read More about Stochastic rounding: implementation, error analysis and applications.