Strings in gravity with torsion

A theory of gravitation in 4D is presented with strings used in the material action in $U_4$ spacetime. It is shown that the string naturally gives rise to torsion. It is also shown that the equation of motion a string follows from the Bianchi identity, gives the identical result as the Noether conservation laws, and follows a geodesic only in the lowest order approximation. In addition, the conservation laws show that strings naturally have spin, which arises not from their motion but from their one dimensional structure.Comment: 16 page

The AFGL absolute gravity program

A brief discussion of the AFGL's (Air Force Geophysics Laboratory) program in absolute gravity is presented. Support of outside work and in-house studies relating to gravity instrumentation are discussed. A description of the current transportable system is included and the latest results are presented. These results show good agreement with measurements at the AFGL site by an Italian system. The accuracy obtained by the transportable apparatus is better than 0.1 microns sq sec 10 microgal and agreement with previous measurements is within the combined uncertainties of the measurements

Tensor-Scalar Torsion

A theory of gravity with torsion is examined in which the torsion tensor is constructed from the exterior derivative of an antisymmetric rank two potential plus the dual of the gradient of a scalar field. Field equations for the theory are derived by demanding that the action be stationary under variations with respect to the metric, the antisymmetric potential, and the scalar field. A material action is introduced and the equations of motion are derived. The correct conservation law for rotational angular momentum plus spin is observed to hold in this theory.Comment: 10 pages, LaTeX, Mod. Phys. Lett. A accepte

Ride quality systems for commuter aircraft

The state-of-the-art in Active Ride Augmentation, specifically in terms of its feasibility for commuter aircraft applications. A literature survey was done, and the principal results are presented here through discussion of different Ride Quality Augmentation System (RQAS) designs and advances in related technologies. Recommended follow-on research areas are discussed, and a preliminary RQAS configuration for detailed design and development is proposed

Parallelising wavefront applications on general-purpose GPU devices

Pipelined wavefront applications form a large portion of the high performance scientific computing workloads at supercomputing centres. This paper investigates the viability of graphics processing units (GPUs) for the acceleration of these codes, using NVIDIA's Compute Unified Device Architecture (CUDA). We identify the optimisations suitable for this new architecture and quantify the characteristics of those wavefront codes that are likely to experience speedups

Experiences with porting and modelling wavefront algorithms on many-core architectures

We are currently investigating the viability of many-core architectures for the acceleration of wavefront applications and this report focuses on graphics processing units (GPUs) in particular. To this end, we have implemented NASA’s LU benchmark – a real world production-grade application – on GPUs employing NVIDIA’s Compute Unified Device Architecture (CUDA). This GPU implementation of the benchmark has been used to investigate the performance of a selection of GPUs, ranging from workstation-grade commodity GPUs to the HPC "Tesla” and "Fermi” GPUs. We have also compared the performance of the GPU solution at scale to that of traditional high perfor- mance computing (HPC) clusters based on a range of multi- core CPUs from a number of major vendors, including Intel (Nehalem), AMD (Opteron) and IBM (PowerPC). In previous work we have developed a predictive “plug-and-play” performance model of this class of application running on such clusters, in which CPUs communicate via the Message Passing Interface (MPI). By extending this model to also capture the performance behaviour of GPUs, we are able to: (1) comment on the effects that architectural changes will have on the performance of single-GPU solutions, and (2) make projections regarding the performance of multi-GPU solutions at larger scale