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Graph component labelling, which is a subset of the general graph colouring problem, is a computationally expensive operation that is of importance in many applications and simulations. A number of data-parallel algorithmic variations to the component labelling problem are possible and we explore their use with general purpose graphical processing units(More)
Graphical Processing Units (GPUs) have recently attracted attention for scientific applications such as particle simulations. This is partially driven by low commodity pricing of GPUs but also by recent toolkit and library developments that make them more accessible to scientific programmers. We report on two further application paradigms – regular mesh(More)
Data-parallel accelerator devices such as Graphical Processing Units (GPUs) are providing dramatic performance improvements over even multicore CPUs for lattice-oriented applications in computational physics. Models such as the Ising and Potts models continue to play a role in investigating phase transitions on small-world and scale-free graph structures.(More)
Simulations users at all levels make heavy use of compute resources to drive computational simulations for greatly varying applications areas of research using different simulation paradigms. Simulations are implemented in many software forms, ranging from highly stan-dardised and general models that run in proprietary software packages to ad hoc(More)
Three-dimensional simulation models are hard to visualise for dense lattice systems, even with cutaways and fly-through techniques. We use multiple Graphics Processing Units (GPUs), CUDA and OpenGL to increase our understanding of computational simulation models such as the 2-D and 3-D Ising system with small-world link rewiring by accelerating both the(More)
Resource discovery is one of the most important underpinning problems behind producing a scalable, robust and efficient global infrastructure for e-Science. A number of approaches to the resource discovery and management problem have been made in various computational grid environments and prototypes over the last decade. Computational resources and(More)
The growing uptake of semantic web and grid ideas is raising the importance of optimising distribution algorithms for semantic metadata. While it is not yet clear how real-world metadata distribution patterns ought to evolve, practical experience of social and technical networks suggests that a small-world pattern is desirable and practical. We explore(More)
Visualising computational simulation models of solid state physical systems is a hard problem for dense lattice models. Fly throughs and cutaways can aid viewer understanding of a simulated system. Interactive time model parameter updates and overlaying of measurements and graticules, cluster colour labelling and other visual highlighting cues can also(More)
Network science makes heavy use of simulation models and calculations based upon graph-oriented data structures that are intrinsically highly irregular in nature. The key to efficient use of data-parallel and multi-core parallelism on graphical processing units (GPUs) and CPUs is often to optimise the data layout and to exploit distributed memory locality(More)