The development of a data-driven application benchmarking approach to performance modelling

Performance modelling is a useful tool in the lifeycle of high performance scientific software, such as weather and climate models, especially as a means of ensuring efficient use of available computing resources. In particular, sufficiently accurate performance prediction could reduce the effort and experimental computer time required when porting and optimising a climate model to a new machine. In this paper, traditional techniques are used to predict the computation time of a simple shallow water model which is illustrative of the computation (and communication) involved in climate models. These models are compared with real execution data gathered on AMD Opteron-based systems, including several phases of the U.K. academic community HPC resource, HECToR. Some success is had in relating source code to achieved performance for the K10 series of Opterons, but the method is found to be inadequate for the next-generation Interlagos processor. The experience leads to the investigation of a data-driven application benchmarking approach to performance modelling. Results for an early version of the approach are presented using the shallow model as an example.

A novel ensemble method for retrieving properties of warm cloud in 3-D using ground-based scanning radar and zenith radiances

We present a novel method for retrieving high-resolution, three-dimensional (3-D) nonprecipitating cloud fields in both overcast and broken-cloud situations. The method uses scanning cloud radar and multiwavelength zenith radiances to obtain gridded 3-D liquid water content (LWC) and effective radius (re) and 2-D column mean droplet number concentration (Nd). By using an adaption of the ensemble Kalman filter, radiances are used to constrain the optical properties of the clouds using a forward model that employs full 3-D radiative transfer while also providing full error statistics given the uncertainty in the observations. To evaluate the new method, we first perform retrievals using synthetic measurements from a challenging cumulus cloud field produced by a large-eddy simulation snapshot. Uncertainty due to measurement error in overhead clouds is estimated at 20% in LWC and 6% in re, but the true error can be greater due to uncertainties in the assumed droplet size distribution and radiative transfer. Over the entire domain, LWC and re are retrieved with average error 0.05–0.08 g m-3 and ~2 μm, respectively, depending on the number of radiance channels used. The method is then evaluated using real data from the Atmospheric Radiation Measurement program Mobile Facility at the Azores. Two case studies are considered, one stratocumulus and one cumulus. Where available, the liquid water path retrieved directly above the observation site was found to be in good agreement with independent values obtained from microwave radiometer measurements, with an error of 20 g m-2.

Language learning in the public eye: an analysis of newspapers and official documents in England

This article considers the issue of low levels of motivation for foreign language learning in England by exploring how language learning is conceptualised by different key voices in that country through the examination of written data: policy documents and reports on the UK's language needs, curriculum documents, and press articles. The extent to which this conceptualisation has changed over time is explored, through the consideration of documents from two time points, before and after a change in government in the UK. The study uses corpus analysis methods in this exploration. The picture that emerges is a complex one regarding how the 'problems' and 'solutions' surrounding language learning in that context are presented in public discourse. This, we conclude, has implications for the likely success of measures adopted to increase language learning uptake in that context.

Impact of game bird release on the Adonis blue butterfly Polyommatus bellargus (Lepidoptera Lycaenidae) on chalk grassland

Polyommatus bellargus is a priority species of butterfly in the UK as a result of its scarcity and the rate of population decline over the last few years. In the UK, the species is associated with chalk grassland on hot, south-facing slopes suitable for the growth of the food plant Hippocrepis comosa. Shooting game birds is a popular pastime in the UK. Over 40 million game birds, principally Phasianus colchicus and Alectoris rufa, are bred and released into the countryside each year for shooting interests. There is a concern that the release of such a large number of non-native birds has an adverse effect on native wildlife. A study was carried out over a period of 3 years out to examine whether there was any evidence that A. rufa released into chalk grassland habitat negatively affects populations of P. bellargus. A comparison was made between sites where large numbers of A. rufa were released versus sites where no, or few, birds were released. The study involved the construction of exclosures in these sites to allow an examination of the number of butterflies emerging from H. comosa when the birds were excluded versus when the birds had free range across the area. Where birds were present the on-site vegetation was shorter than where they were absent indicating that the birds were definitely influencing habitat structure. However, the evidence that A. rufa was negatively influencing the number of adult butterflies emerging was not strong, although there was a largely non-significant tendency for higher butterfly emergence when the birds were excluded or absent.

Breeding biology of the White-headed Vulture Trigonoceps occipitalis in Kruger National Park, South Africa

Information on the breeding biology of the White-headed Vulture Trigonoceps occipitalis is limited and published data are few. Within the Kruger National Park in north-east South Africa there is a regionally important population of about 60 White-headed Vulture pairs, of which 22 pairs were monitored for five years between 2008 and 2012 to determine key aspects of their breeding biology. Across 73 pair/years the mean productivity of 55 breeding attempts was 0.69 chicks per pair. Median egg-laying date across all of the Kruger National Park was 27 June, but northern nests were approximately 30 d later than southern nests. Mean (SD) nearest-neighbour distance was 9 976  7 965 m and inter-nest distances ranged from 1 400 m to more than 20 km, but this did not differ significantly between habitat types. Breeding productivity did not differ significantly between habitat types. The results presented here are the first for this species in Kruger National Park and provide details against which future comparisons can be made.

The WFDEI meteorological forcing data set: WATCH Forcing Data methodology applied to ERA-Interim reanalysis data

The WFDEI meteorological forcing data set has been generated using the same methodology as the widely used WATCH Forcing Data (WFD) by making use of the ERA-Interim reanalysis data. We discuss the specifics of how changes in the reanalysis and processing have led to improvement over the WFD. We attribute improvements in precipitation and wind speed to the latest reanalysis basis data and improved downward shortwave fluxes to the changes in the aerosol corrections. Covering 1979–2012, the WFDEI will allow more thorough comparisons of hydrological and Earth System model outputs with hydrologically and phenologically relevant satellite products than using the WFD.

Freshwater resources

This is Chapter 3 of the report of Working Group II to the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC). It describes the risks posed by climate change to freshwater resources and their management.

A benchmark-driven modelling approach for evaluating deployment choices on a multi-core architecture

The complexity of current and emerging architectures provides users with options about how best to use the available resources, but makes predicting performance challenging. In this work a benchmark-driven model is developed for a simple shallow water code on a Cray XE6 system, to explore how deployment choices such as domain decomposition and core affinity affect performance. The resource sharing present in modern multi-core architectures adds various levels of heterogeneity to the system. Shared resources often includes cache, memory, network controllers and in some cases floating point units (as in the AMD Bulldozer), which mean that the access time depends on the mapping of application tasks, and the core's location within the system. Heterogeneity further increases with the use of hardware-accelerators such as GPUs and the Intel Xeon Phi, where many specialist cores are attached to general-purpose cores. This trend for shared resources and non-uniform cores is expected to continue into the exascale era. The complexity of these systems means that various runtime scenarios are possible, and it has been found that under-populating nodes, altering the domain decomposition and non-standard task to core mappings can dramatically alter performance. To find this out, however, is often a process of trial and error. To better inform this process, a performance model was developed for a simple regular grid-based kernel code, shallow. The code comprises two distinct types of work, loop-based array updates and nearest-neighbour halo-exchanges. Separate performance models were developed for each part, both based on a similar methodology. Application specific benchmarks were run to measure performance for different problem sizes under different execution scenarios. These results were then fed into a performance model that derives resource usage for a given deployment scenario, with interpolation between results as necessary.

Acceleration and visualization of Dynamic Network Optimization

With the emerging prevalence of smart phones and 4G LTE networks, the demand for faster-better-cheaper mobile services anytime and anywhere is ever growing. The Dynamic Network Optimization (DNO) concept emerged as a solution that optimally and continuously tunes the network settings, in response to varying network conditions and subscriber needs. Yet, the DNO realization is still at infancy, largely hindered by the bottleneck of the lengthy optimization runtime. This paper presents the design and prototype of a novel cloud based parallel solution that further enhances the scalability of our prior work on various parallel solutions that accelerate network optimization algorithms. The solution aims to satisfy the high performance required by DNO, preliminarily on a sub-hourly basis. The paper subsequently visualizes a design and a full cycle of a DNO system. A set of potential solutions to large network and real-time DNO are also proposed. Overall, this work creates a breakthrough towards the realization of DNO.

Distributed parallelization of greedy Mobile Network Optimization algorithms

The Mobile Network Optimization (MNO) technologies have advanced at a tremendous pace in recent years. And the Dynamic Network Optimization (DNO) concept emerged years ago, aimed to continuously optimize the network in response to variations in network traffic and conditions. Yet, DNO development is still at its infancy, mainly hindered by a significant bottleneck of the lengthy optimization runtime. This paper identifies parallelism in greedy MNO algorithms and presents an advanced distributed parallel solution. The solution is designed, implemented and applied to real-life projects whose results yield a significant, highly scalable and nearly linear speedup up to 6.9 and 14.5 on distributed 8-core and 16-core systems respectively. Meanwhile, optimization outputs exhibit self-consistency and high precision compared to their sequential counterpart. This is a milestone in realizing the DNO. Further, the techniques may be applied to similar greedy optimization algorithm based applications.

Asynchronous distributed parallelization of mobile network optimization algorithms

It has been years since the introduction of the Dynamic Network Optimization (DNO) concept, yet the DNO development is still at its infant stage, largely due to a lack of breakthrough in minimizing the lengthy optimization runtime. Our previous work, a distributed parallel solution, has achieved a significant speed gain. To cater for the increased optimization complexity pressed by the uptake of smartphones and tablets, however, this paper examines the potential areas for further improvement and presents a novel asynchronous distributed parallel design that minimizes the inter-process communications. The new approach is implemented and applied to real-life projects whose results demonstrate an augmented acceleration of 7.5 times on a 16-core distributed system compared to 6.1 of our previous solution. Moreover, there is no degradation in the optimization outcome. This is a solid sprint towards the realization of DNO.

Staphylococcus aureus MnhF mediates cholate efflux and facilitates survival under human colonic conditions

Resistance to the innate defences of the intestine is crucial for the survival and carriage of Staphylococcus aureus, a common coloniser of the human gut. Bile salts produced by the liver and secreted into the intestines are one such group of molecules with potent anti-microbial activity. The mechanisms by which S. aureus is able to resist such defences in order to colonize and survive in the human gut are unknown. Here we show that mnhF confers resistance to bile salts, which can be abrogated by efflux pump inhibitors. MnhF mediates efflux of radiolabelled cholic acid in both S. aureus and when heterologously expressed in Escherichia coli, rendering them resistant. Deletion of mnhF attenuated survival of S. aureus in an anaerobic three stage continuous culture model of the human colon (gut model), which represent different anatomical areas of the large intestine.