JOB BUILDER remote batch processing subsystem

The functions of the JOB BUILDER remote batch processing subsystem are described. Instructions are given for using it as a component of a display system developed by personnel of the System Programming Laboratory, Institute of Space Research, USSR Academy of Sciences

Optimisation of solvolysis for recycling carbon fibre reinforced composites

Solvolysis processes have been used to degrade the resin of two different varieties of epoxy based carbon fibre reinforced composite (CFRC) materials. A degradation of up to 98% has been achieved when processing material at a temperature of 320 °C using a supercritical solvent mixture of acetone and water. Increasing the processing time from 1 to 2 hours shows an increase in the degradation of only 10% and there does not appear to be any benefit in processing the material beyond this time. Due to the batch conditions used, it is necessary to rinse the fibres with acetone after processing to remove remaining organic residue. Washing the fibres at supercritical batch conditions, however, does not efficiently remove the residue compared to a simple hand washing with acetone. Shredding the sample prior to processing also does not have a significant effect. The process investigated requires 19 MJ.kg-1 of fibres recovered and, since the process has not yet been optimised, shows strong potential for future development especially since it allows for the recovery and reuse of organic resinous products

Batch-normalized Recurrent Highway Networks

Gradient control plays an important role in feed-forward networks applied to various computer vision tasks. Previous work has shown that Recurrent Highway Networks minimize the problem of vanishing or exploding gradients. They achieve this by setting the eigenvalues of the temporal Jacobian to 1 across the time steps. In this work, batch normalized recurrent highway networks are proposed to control the gradient flow in an improved way for network convergence. Specifically, the introduced model can be formed by batch normalizing the inputs at each recurrence loop. The proposed model is tested on an image captioning task using MSCOCO dataset. Experimental results indicate that the batch normalized recurrent highway networks converge faster and performs better compared with the traditional LSTM and RHN based models.Comment: 5 pages, 3 figures, Published in 2017 IEEE International Conference on Image Processing (ICIP

Enabling a High Throughput Real Time Data Pipeline for a Large Radio Telescope Array with GPUs

The Murchison Widefield Array (MWA) is a next-generation radio telescope currently under construction in the remote Western Australia Outback. Raw data will be generated continuously at 5GiB/s, grouped into 8s cadences. This high throughput motivates the development of on-site, real time processing and reduction in preference to archiving, transport and off-line processing. Each batch of 8s data must be completely reduced before the next batch arrives. Maintaining real time operation will require a sustained performance of around 2.5TFLOP/s (including convolutions, FFTs, interpolations and matrix multiplications). We describe a scalable heterogeneous computing pipeline implementation, exploiting both the high computing density and FLOP-per-Watt ratio of modern GPUs. The architecture is highly parallel within and across nodes, with all major processing elements performed by GPUs. Necessary scatter-gather operations along the pipeline are loosely synchronized between the nodes hosting the GPUs. The MWA will be a frontier scientific instrument and a pathfinder for planned peta- and exascale facilities.Comment: Version accepted by Comp. Phys. Com