Multi-Tenant Virtual GPUs for Optimising Performance of a Financial Risk Application

Graphics Processing Units (GPUs) are becoming popular accelerators in modern High-Performance Computing (HPC) clusters. Installing GPUs on each node of the cluster is not efficient resulting in high costs and power consumption as well as underutilisation of the accelerator. The research reported in this paper is motivated towards the use of few physical GPUs by providing cluster nodes access to remote GPUs on-demand for a financial risk application. We hypothesise that sharing GPUs between several nodes, referred to as multi-tenancy, reduces the execution time and energy consumed by an application. Two data transfer modes between the CPU and the GPUs, namely concurrent and sequential, are explored. The key result from the experiments is that multi-tenancy with few physical GPUs using sequential data transfers lowers the execution time and the energy consumed, thereby improving the overall performance of the application.

From Customer-Oriented Strategy to Organizational Financial Performance: The Role of Human Resource Management and Customer-Linking Capability

Drawing on the organizational capabilities literature, the authors developed and tested a model of how supportive human resource management (HRM) improved firms’ financial performance perceived by marketing managers through fostering the implementation of a customer-oriented strategy. Customer-linking capability, which is the capability in managing close customer relationships, indicated the implementation of the customer-oriented strategy. Data collected from two emerging economies – China and Hungary –established that supportive HRM partially mediated the relationship between customer-oriented strategy and customer-linking capability. Customer-linking capability further explained how supportive HRM contributed to perceived financial performance. This study explicates the implication of customer-oriented strategy for HRM and reveals the
importance of HRM in strategy implementation. It also sheds some light on the ‘black box’ between HRM and performance. While making important contributions to the field of strategy, HRM and marketing, this study also offers useful practical implications.

Modeling, Analysis and Performance Comparison of Two Direct Sampling DCSK Receivers under Frequency Nonselective Fading Channels

Two direct sampling correlator-type receivers for differential chaos shift keying (DCSK) communication systems under frequency non-selective fading channels are proposed. These receivers operate based on the same hardware platform with different architectures. In the first scheme, namely sum-delay-sum (SDS) receiver, the sum of all samples in a chip period is correlated with its delayed version. The correlation value obtained in each bit period is then compared with a fixed threshold to decide the binary value of recovered bit at the output. On the other hand, the second scheme, namely delay-sum-sum (DSS) receiver, calculates the correlation value of all samples with its delayed version in a chip period. The sum of correlation values in each bit period is then compared with the threshold to recover the data. The conventional DCSK transmitter, frequency non-selective Rayleigh fading channel, and two proposed receivers are mathematically modelled in discrete-time domain. The authors evaluated the bit error rate performance of the receivers by means of both theoretical analysis and numerical simulation. The performance comparison shows that the two proposed receivers can perform well under the studied channel, where the performances get better when the number of paths increases and the DSS receiver outperforms the SDS one.

Prior knowledge transfer across transcriptional data sets and technologies using compositional statistics yields new mislabelled ovarian cell line

Here, we describe gene expression compositional assignment (GECA), a powerful, yet simple method based on compositional statistics that can validate the transfer of prior knowledge, such as gene lists, into independent data sets, platforms and technologies. Transcriptional profiling has been used to derive gene lists that stratify patients into prognostic molecular subgroups and assess biomarker performance in the pre-clinical setting. Archived public data sets are an invaluable resource for subsequent in silico validation, though their use can lead to data integration issues. We show that GECA can be used without the need for normalising expression levels between data sets and can outperform rank-based correlation methods. To validate GECA, we demonstrate its success in the cross-platform transfer of gene lists in different domains including: bladder cancer staging, tumour site of origin and mislabelled cell lines. We also show its effectiveness in transferring an epithelial ovarian cancer prognostic gene signature across technologies, from a microarray to a next-generation sequencing setting. In a final case study, we predict the tumour site of origin and histopathology of epithelial ovarian cancer cell lines. In particular, we identify and validate the commonly-used cell line OVCAR-5 as non-ovarian, being gastrointestinal in origin. GECA is available as an open-source R package.

Cloud Benchmarking For Maximising Performance of Scientific Applications

How can applications be deployed on the cloud to achieve maximum performance? This question is challenging to address with the availability of a wide variety of cloud Virtual Machines (VMs) with different performance capabilities. The research reported in this paper addresses the above question by proposing a six step benchmarking methodology in which a user provides a set of weights that indicate how important memory, local communication, computation and storage related operations are to an application. The user can either provide a set of four abstract weights or eight fine grain weights based on the knowledge of the application. The weights along with benchmarking data collected from the cloud are used to generate a set of two rankings - one based only on the performance of the VMs and the other takes both performance and costs into account. The rankings are validated on three case study applications using two validation techniques. The case studies on a set of experimental VMs highlight that maximum performance can be achieved by the three top ranked VMs and maximum performance in a cost-effective manner is achieved by at least one of the top three ranked VMs produced by the methodology.

Advanced Business Simulations-Incorporating business and process execution data

Key Performance Indicators (KPIs) and their predictions are widely used by the enterprises for informed decision making. Nevertheless , a very important factor, which is generally overlooked, is that the top level strategic KPIs are actually driven by the operational level business processes. These two domains are, however, mostly segregated and analysed in silos with different Business Intelligence solutions. In this paper, we are proposing an approach for advanced Business Simulations, which converges the two domains by utilising process execution & business data, and concepts from Business Dynamics (BD) and Business Ontologies, to promote better system understanding and detailed KPI predictions. Our approach incorporates the automated creation of Causal Loop Diagrams, thus empowering the analyst to critically examine the complex dependencies hidden in the massive amounts of available enterprise data. We have further evaluated our proposed approach in the context of a retail use-case that involved verification of the automatically generated causal models by a domain expert.

Building performance evaluation of domestic energy efficient retrofits in current and future climates

Approximately half of the houses in Northern Ireland were built before any form of minimum thermal specification or energy efficiency standard was enforced. Furthermore, 44% of households are categorised as being in fuel poverty; spending more than 10% of the household income to heat the house to bring it to an acceptable level of thermal comfort. To bring existing housing stock up to an acceptable standard, retrofitting for improving the energy efficiency is essential and it is also necessary to study the effectiveness of such improvements in future climate scenarios. This paper presents the results from a year-long performance monitoring of two houses that have undergone retrofits to improve energy efficiency. Using wireless sensor technology internal temperature, humidity, external weather, household gas and electricity usage were monitored for a year. Simulations using IES-VE dynamic building modelling software were calibrated using the monitoring data to ASHARE Guideline 14 standards. The energy performance and the internal environment of the houses were then assessed for current and future climate scenarios and the results show that there is a need for a holistic balanced strategy for retrofitting.

Joint Analysis of Multiple Algorithms and Performance Measures

There has been an increasing interest in the development of new methods using Pareto optimality to deal with multi-objective criteria (for example, accuracy and time complexity). Once one has developed an approach to a problem of interest, the problem is then how to compare it with the state of art. In machine learning, algorithms are typically evaluated by comparing their performance on different data sets by means of statistical tests. Standard tests used for this purpose are able to consider jointly neither performance measures nor multiple competitors at once. The aim of this paper is to resolve these issues by developing statistical procedures that are able to account for multiple competing measures at the same time and to compare multiple algorithms altogether. In particular, we develop two tests: a frequentist procedure based on the generalized likelihood-ratio test and a Bayesian procedure based on a multinomial-Dirichlet conjugate model. We further extend them by discovering conditional independences among measures to reduce the number of parameters of such models, as usually the number of studied cases is very reduced in such comparisons. Data from a comparison among general purpose classifiers is used to show a practical application of our tests.

Impacts of FDI Renewable Energy Technology Spillover on China's Energy Industry Performance

Environmental friendly renewable energy plays an indispensable role in energy industry development. Foreign direct investment (FDI) in advanced renewable energy technology spillover is promising to improve technological capability and promote China’s energy industry performance growth. In this paper, the impacts of FDI renewable energy technology spillover on China’s energy industry performance are analyzed based on theoretical and empirical studies. Firstly, three hypotheses are proposed to illustrate the relationships between FDI renewable energy technology spillover and three energy industry performances including economic, environmental, and innovative performances. To verify the hypotheses, techniques including factor analysis and data envelopment analysis (DEA) are employed to quantify the FDI renewable energy technology spillover and the energy industry performance of China, respectively. Furthermore, a panel data regression model is proposed to measure the impacts of FDI renewable energy technology spillover on China’s energy industry performance. Finally, energy industries of 30 different provinces in China based on the yearbook data from 2005 to 2011 are comparatively analyzed for evaluating the impacts through the empirical research. The results demonstrate that FDI renewable energy technology spillover has positive impacts on China’s energy industry performance. It can also be found that the technology spillover effects are more obvious in economic and technological developed regions. Finally, four suggestions are provided to enhance energy industry performance and promote renewable energy technology spillover in China.

Developing an engineering approach for migrating from prescriptive to performance-based specification for concrete

The integral variability of raw materials, lack of awareness and appreciation of the technologies for achieving quality control and lack of appreciation of the micro and macro environmental conditions that the structures will be subjected, makes modern day concreting a challenge. This also makes Designers and Engineers adhere more closely to prescriptive standards developed for relatively less aggressive environments. The data from exposure sites and real structures prove, categorically, that the prescriptive specifications are inadequate for chloride environments. In light of this shortcoming, a more pragmatic approach would be to adopt performance-based specifications which are familiar to industry in the form of specification for mechanical strength. A recently completed RILEM technical committee made significant advances in making such an approach feasible.
Furthering a performance-based specification requires establishment of reliable laboratory and on-site test methods, as well as easy to perform service-life models. This article highlights both laboratory and on-site test methods for chloride diffusivity/electrical resistivity and the relationship between these tests for a range of concretes. Further, a performance-based approach using an on-site diffusivity test is outlined that can provide an easier to apply/adopt practice for Engineers and asset managers for specifying/testing concrete structures.

Does video feedback analysis improve CPR performance in phase 5 medical students?

Background The use of simulation in medical education is increasing, with students taught and assessed using simulated patients and manikins. Medical students at Queen’s University of Belfast are taught advanced life support cardiopulmonary resuscitation as part of the undergraduate curriculum. Teaching and feedback in these skills have been developed in Queen’s University with high-fidelity manikins. This study aimed to evaluate the effectiveness of video compared to verbal feedback in assessment of student cardiopulmonary resuscitation performance Methods Final year students participated in this study using a high-fidelity manikin, in the Clinical Skills Centre, Queen’s University Belfast. Cohort A received verbal feedback only on their performance and cohort B received video feedback only. Video analysis using ‘StudioCode’ software was distributed to students. Each group returned for a second scenario and evaluation 4 weeks later. An assessment tool was created for performance assessment, which included individual skill and global score evaluation. Results One hundred thirty eight final year medical students completed the study. 62 % were female and the mean age was 23.9 years. Students having video feedback had significantly greater improvement in overall scores compared to those receiving verbal feedback (p = 0.006, 95 % CI: 2.8–15.8). Individual skills, including ventilation quality and global score were significantly better with video feedback (p = 0.002 and p < 0.001, respectively) when compared with cohort A. There was a positive change in overall score for cohort B from session one to session two (p < 0.001, 95 % CI: 6.3–15.8) indicating video feedback significantly benefited skill retention. In addition, using video feedback showed a significant improvement in the global score (p < 0.001, 95 % CI: 3.3–7.2) and drug administration timing (p = 0.004, 95 % CI: 0.7–3.8) of cohort B participants, from session one to session two. Conclusions There is increased use of simulation in medicine but a paucity of published data comparing feedback methods in cardiopulmonary resuscitation training. Our study shows the use of video feedback when teaching cardiopulmonary resuscitation is more effective than verbal feedback, and enhances skill retention. This is one of the first studies to demonstrate the benefit of video feedback in cardiopulmonary resuscitation teaching.

Seasonal changes in broodstock spawning performance and egg quality in ballan wrasse (Labrus bergylta)

Sea lice continue to be one of the largest issues for the salmon farming industry and the use of ballan wrasse (Labrus bergylta) as a biological control is considered to be one of the most sustainable solutions in development. Broodstock management has proved challenging in the initial phases due to the significant lack of understanding of basic reproductive physiology and behaviour in the species. The aim of the study was to monitor captive breeding populations throughout a spawning season to examine timing and duration of spawning,quantify egg production, and look at seasonal changes in egg quality parameters as well as investigate the parental contribution to spawning events. A clear spawning rhythm was shown with 3-5 spawning periods inclusive of spawning windows lasting 1-9 days followed by inter spawning intervals of 8-12 days. Fertilization rate remained consistently high (> 87.5%) over the spawning season and did not differ significantly between spawning populations. Hatch rate was variable (0-97.5 %), but peaked in the middle of the spawning season. Meanoocyte diameter and gum layer thickness decreased slightly over the spawning season with no significant differences between spawning populations. Fatty acid (FA) profile of eggs remained consistent throughout the season and with the exception of high levels of ARA (3.8 ± 0.5 % of total FA) the FA profile was similar to that observed in other marine fish species. Parental contribution analysis showed 3 out of 6 spawning events to be single paired mating while the remaining 3 had contributions from multiple parents. Furthermore, the proposed multiple batch spawning nature of this species was confirmed with proof of a single femalecontributing to two separate spawning events. Overall this work represents the first comprehensive data set of spawning activity of captive ballan wrasse, and as such and will be helpful in formulating sustainable broodstock management plans for the species.

Continuous statistical modelling for rapid detection of adulteration of extra virgin olive oil using mid infrared and Raman spectroscopic data

The main objective of this work was to develop a novel dimensionality reduction technique as a part of an integrated pattern recognition solution capable of identifying adulterants such as hazelnut oil in extra virgin olive oil at low percentages based on spectroscopic chemical fingerprints. A novel Continuous Locality Preserving Projections (CLPP) technique is proposed which allows the modelling of the continuous nature of the produced in-house admixtures as data series instead of discrete points. The maintenance of the continuous structure of the data manifold enables the better visualisation of this examined classification problem and facilitates the more accurate utilisation of the manifold for detecting the adulterants. The performance of the proposed technique is validated with two different spectroscopic techniques (Raman and Fourier transform infrared, FT-IR). In all cases studied, CLPP accompanied by k-Nearest Neighbors (kNN) algorithm was found to outperform any other state-of-the-art pattern recognition techniques.

Investigation of the effects of flow conditions at rotor inlet on mixed flow turbine performance for automotive applications

Current trends in the automotive industry have placed increased importance on engine downsizing for passenger vehicles. Engine downsizing often results in reduced power output and turbochargers have been relied upon to restore the power output and maintain drivability. As improved power output is required across a wide range of engine operating conditions, it is necessary for the turbocharger to operate effectively at both design and off-design conditions. One off-design condition of considerable importance for turbocharger turbines is low velocity ratio operation, which refers to the combination of high exhaust gas velocity and low turbine rotational speed. Conventional radial flow turbines are constrained to achieve peak efficiency at the relatively high velocity ratio of 0.7, due the requirement to maintain a zero inlet blade angle for structural reasons. Several methods exist to potentially shift turbine peak efficiency to lower velocity ratios. One method is to utilize a mixed flow turbine as an alternative to a radial flow turbine. In addition to radial and circumferential components, the flow entering a mixed flow turbine also has an axial component. This allows the flow to experience a non-zero inlet blade angle, potentially shifting peak efficiency to a lower velocity ratio when compared to an equivalent radial flow turbine.
This study examined the effects of varying the flow conditions at the inlet to a mixed flow turbine and evaluated the subsequent impact on performance. The primary parameters examined were average inlet flow angle, the spanwise distribution of flow angle across the inlet and inlet flow cone angle. The results have indicated that the inlet flow angle significantly influenced the degree of reaction across the rotor and the turbine efficiency. The rotor studied was a custom in-house design based on a state-of-the-art radial flow turbine design. A numerical approach was used as the basis for this investigation and the numerical model has been validated against experimental data obtained from the cold flow turbine test rig at Queen’s University Belfast. The results of the study have provided a useful insight into how the flow conditions at rotor inlet influence the performance of a mixed flow turbine.