GPU Research

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Case Studies

Paul Richmond (Computer Science): FLAME GPU is a software framework for accelerating and visualising (live) agent based simulations including but not limited to pedestrian dynamics, swarming and biological cell systems. It is currently being extended to look at predictive behaviour.
SMAUG
Mike Griffiths (CICS), (SP2RC): SMAUG is based on the Sheffield Advanced Code (SAC), which is a novel fully non-linear MHD code, designed for simulations of linear and non-linear wave propagation in gravitationally strongly stratified magnetised plasma. See the reference at the Astronomy Abstracts Service. reference
OsteolyticaPaul Richmond (Computer Science) and Andrew Chantry (Medical School), developed by Twin Karmakharm (Computer Science): Osteolytica is a GPU accelerated bone volume analysis tool for assessing the osteolytic damage caused by cancerous diseases such as multiple myloma.
BEAST and BEAGLEBEAST is a cross-platform program for Bayesian MCMC analysis of molecular sequences. It is entirely orientated towards rooted, time-measured phylogenies inferred using strict or relaxed molecular clock models. It can be used as a method of reconstructing phylogenies but is also a framework for testing evolutionary hypotheses without conditioning on a single tree topology. By default, BEAST tries to use BEAGLE for treelikelihood calculations, if it is installed. BEAGLE is a high-performance library that can perform the core calculations at the heart of most Bayesian and Maximum Likelihood phylogenetics packages
GreenBrainThe ‘Green Brain Project’ combines computational neuroscience modelling, learning and decision theory, modern parallel computing methods, and robotics with data from state-of-the-art neurobiological experiments on cognition in the honeybee Apis mellifera.
Forecasting Personal Health in Uncertain EnvironmentsPredictions of polymer network structure for human mucus and the interaction of the mucus network with viral particles such as influenza using dissipative particle dynamics (DPD). The objective being to include uncertainty in the DPD simulation to understand how variation the rheology of mucus affects the infectivity of pathogens. 

GPU Researchers

Name and Contact Details
Research Interests
Paul Richmond
http://www.paulrichmond.staff.shef.ac.uk/
p.richmond@sheffield.ac.uk
High performance complex systems simulations including agent based modelling, visualisation and prediction of pedestrian dynamics, flocking, cellular level simulations and spiking neural networks. Further interest in simulations for computer graphics and visual data analysis.
Mike Griffiths
m.griffiths@sheffield.ac.uk

Sam Coveney
s.coveney@sheffield.ac.uk
Modelling polymer blend thin films using a minimal model to determine what is necessary and/or sufficient to produce observed phenomena. Using simulations to investigate behaviour within polymer films that is hard to observe experimentally.
Hongyang Qu
h.qu@sheffield.ac.uk
I am interested in applying GPU computing to speed up probabilistic model checking, which involves a sequence matrix multiplications. In particular, I would like to develop an efficient algorithm to parallelise Gauss-Seidel method.  I am also interested in GPU-based fast parameter synthesis for computational systems biology using probabilistic model checking.
Robin Oliver
robin.oliver@sheffield.ac.uk
I am a theoretical physicist with a background in the simulation of biological systems. My PhD thesis involved using the physics of continuum mechanics to model the dynamics of proteins. This was achieved by extending the range of continuum mechanics down into the biological mesocale by including thermal noise. My current interests are still in simulation working on techniques to model polymer structure using GPUs.
Dr Daniele Tartarini
d.tartarini@sheffield.ac.uk

Dr Dawn Walker
D.C.Walker@sheffield.ac.uk
Dr Daniele Tartarini (Insigneo, Mechanical Engineering) and Dr Dawn Walker (Insigneo, Computer Science) are interested in the application of cellular-based cancer models on GPUs. This work is being carried out in parallel to the development of a HPC based hypermodelling framework for cancer models associated with the CHIC (Computational Horizons in Cancer) project (http://chic-vph.eu/).

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