The VINEYARD Approach: Versatile, Integrated, Accelerator-Based, Heterogeneous Data Centres.

Emerging web applications like cloud computing, Big Data and social networks have created the need for powerful centres hosting hundreds of thousands of servers. Currently, the data centres are based on general purpose processors that provide high flexibility buts lack the energy efficiency of customized accelerators. VINEYARD aims to develop an integrated platform for energy-efficient data centres based on new servers with novel, coarse-grain and fine-grain, programmable hardware accelerators. It will, also, build a high-level programming framework for allowing end-users to seamlessly utilize these accelerators in heterogeneous computing systems by employing typical data-centre programming frameworks (e.g. MapReduce, Storm, Spark, etc.). This programming framework will, further, allow the hardware accelerators to be swapped in and out of the heterogeneous infrastructure so as to offer high flexibility and energy efficiency. VINEYARD will foster the expansion of the soft-IP core industry, currently limited in the embedded systems, to the data-centre market. VINEYARD plans to demonstrate the advantages of its approach in three real use-cases (a) a bio-informatics application for high-accuracy brain modeling, (b) two critical financial applications, and (c) a big-data analysis application.

A Scalable Runtime for the ECOSCALE Heterogeneous Exascale Hardware Platform

Exascale computation is the next target of high performance computing. In the push to create exascale computing platforms, simply increasing the number of hardware devices is not an acceptable option given the limitations of power consumption, heat dissipation, and programming models which are designed for current hardware platforms. Instead, new hardware technologies, coupled with improved programming abstractions and more autonomous runtime systems, are required to achieve this goal. This position paper presents the design of a new runtime for a new heterogeneous hardware platform being developed to explore energy efficient, high performance computing. By combining a number of different technologies, this framework will both simplify the programming of current and future HPC applications, as well as automating the scheduling of data and computation across this new hardware platform. In particular, this work explores the use of FPGAs to achieve both the power and performance goals of exascale, as well as utilising the runtime to automatically effect dynamic configuration and reconfiguration of these platforms. 

Laser Driven Neutron Sources: Characteristics, Applications and Prospects

The basics of laser driven neutron sources, properties and possible applications are discussed. We describe the laser driven nuclear processes which trigger neutron generation, namely, nuclear reactions induced by laser driven ion beam (ion n), thermonuclear fusion by implosion and photo-induced nuclear (gamma n) reactions. Based on their main properties, i.e. point source (< 100 μm) and short durations (< ns), different applications are described, such as radiography, time-resolved spectroscopy and pump-probe experiments. Prospects on the development of laser technology suggest that, as higher intensities and higher repetition rate lasers become available (for example, using DPSSL technology), laser driven methodologies may provide neutron fluxes comparable to that achieved by accelerator driven neutron sources in the near future.

Exploring resilience in nursing and midwifery students: a literature review

AIM: The aim of this study was to explore the concepts of 'resilience' and 'hardiness' in nursing and midwifery students in educational settings and to identify educational interventions to promote resilience.

BACKGROUND: Resilience in healthcare professionals has gained increasing attention globally, yet to date resilience and resilience education in nursing and midwifery students remain largely under-researched.

DESIGN: An integrative literature review was planned, however, only quantitative evidence was identified therefore, a review of quantitative studies was undertaken using a systematic approach.

DATA SOURCES: A comprehensive search was undertaken using Medline, CINAHL, Embase, PsycINFO and Maternity and Infant Care databases January 1980-February 2015.

REVIEW METHODS: Data were extracted using a specifically designed form and quality assessed using an appropriate checklist. A narrative summary of findings and statistical outcomes was undertaken.

RESULTS: Eight quantitative studies were included. Research relating to resilience and resilience education in nursing and midwifery students is sparse. There is a weak evidence that resilience and hardiness is associated with slightly improved academic performance and decreased burnout. However, studies were heterogeneous in design and limited by poor methodological quality. No study specifically considered student midwives.

CONCLUSION: A greater understanding of the theoretical underpinnings of resilience in nursing and midwifery students is essential for the development of educational resources. It is imperative that future research considers both nursing and midwifery training cohorts and should be of strong methodological quality.

Runtime Support for Adaptive Power Capping on Heterogeneous SoCs

Power capping is a fundamental method for reducing the energy consumption of a wide range of modern computing environments, ranging from mobile embedded systems to datacentres. Unfortunately, maximising performance and system efficiency under static power caps remains challenging, while maximising performance under dynamic power caps has been largely unexplored. We present an adaptive power capping method that reduces the power consumption and maximizes the performance of heterogeneous SoCs for mobile and server platforms. Our technique combines power capping with coordinated DVFS, data partitioning and core allocations on a heterogeneous SoC with ARM processors and FPGA resources. We design our framework as a run-time system based on OpenMP and OpenCL to utilise the heterogeneous resources. We evaluate it through five data-parallel benchmarks on the Xilinx SoC which allows fully voltage and frequency control. Our experiments show a significant performance boost of 30% under dynamic power caps with concurrent execution on ARM and FPGA, compared to a naive separate approach.