The past few years have seen significant developments in Single Board Computer (SBC) hardware capabilities. These advances in SBCs translate directly into improvements in SBC clusters. In 2018 an individual SBC has more than four times the performance of a 64-node SBC cluster from 2013. This increase in performance has been accompanied by increases in energy efficiency (GFLOPS/W) and value for money (GFLOPS/$). We present systematic analysis of these metrics for three different SBC clusters composed of Raspberry Pi 3 Model B, Raspberry Pi 3 Model B+ and Odroid C2 nodes respectively. A 16-node SBC cluster can achieve up to 60 GFLOPS, running at 80 W. We believe that these improvements open new computational opportunities, whether this derives from a decrease in the physical volume required to provide a fixed amount of computation power for a portable cluster; or the amount of compute power that can be installed given a fixed budget in expendable compute scenarios. We also present a new SBC cluster construction form factor named Pi Stack; this has been designed to support edge compute applications rather than the educational use-cases favoured by previous methods. The improvements in SBC cluster performance and construction techniques mean that these SBC clusters are realising their potential as valuable developmental edge compute devices rather than just educational curiosities.

Microfocus X-ray computed tomography (μCT) and 3D microscopy scanning create scientific data in the form images. These images are each several tens of gigabytes in size. E-Scientists in medicine require a user-friendly way of storing the data and related metadata and accessing it. Existing management systems allow computer scientists to create automatic image workflows through the use of application programming interfaces (APIs) but do not offer an easy alternative for users less familiar with programming. We present a new approach to the management and curation of biomedical image data and related metadata. Our system, Mata, uses a network file share to give users direct access to their data and also provides access to metadata. Mata also enables a variety of visualization options as required by e-Scientists in medicine.

Until recently, cluster computing was too expensive and too complex for commodity users. However the phenomenal popularity of single board computers like the Raspberry Pi has caused the emergence of the single board computer cluster. This demonstration will present a cheap, practical and portable Raspberry Pi cluster called Pi Stack. We will show pragmatic custom solutions to hardware issues, such as power distribution, and software issues, such as remote updating. We also sketch potential use cases for Pi Stack and other commodity single board computer cluster architectures.

Current commodity Single Board Computers (SBCs) are sufficiently powerful to run mainstream operating systems and workloads. Many of these boards may be linked together, to create small, low-cost clusters that replicate some features of large data center clusters. The Raspberry Pi Foundation produces a series of SBCs with a price/performance ratio that makes SBC clusters viable, perhaps even expendable. These clusters are an enabler for Edge/Fog Compute, where processing is pushed out towards data sources, reducing bandwidth requirements and decentralizing the architecture. In this paper we investigate use cases driving the growth of SBC clusters, we examine the trends in future hardware developments, and discuss the potential of SBC clusters as a disruptive technology. Compared to traditional clusters, SBC clusters have a reduced footprint, are low-cost, and have low power requirements. This enables different models of deployment—particularly outside traditional data center environments. We discuss the applicability of existing software and management infrastructure to support exotic deployment scenarios and anticipate the next generation of SBC. We conclude that the SBC cluster is a new and distinct computational deployment paradigm, which is applicable to a wider range of scenarios than current clusters. It facilitates Internet of Things and Smart City systems and is potentially a game changer in pushing application logic out towards the network edge.

Micro Computed-Tomography (μCT) scanning is opening a new world for medical researchers. Scientific data of several tens of gigabytes per image is created and usually requires storage on a common server such as Picture Archiving and Communication Systems (PACS). Previewing this data online in a meaningful way is an essential part of these systems. Radiologists who have been working with CT data for a long time are commonly looking at two-dimensional slices of 3D image stacks. Conventional web-viewers such as Google Maps and Deep Zoom use tiled multiresolution-images for faster display of large 2D data. In the medical area this approach is being adapted for high resolution 2D images. Solutions that include basic image processing still rely on browser external solutions and high-performance client-machines. In this paper we optimized and modified Brain Maps API to create an interactive orthogonal-sectioning image-viewer for medical μCT scans, based on JavaScript and HTML5. We show that tiling of images reduces the processing time by a factor of two. Different file formats are compared regarding their quality and time to display. As well a sample end-to-end application demonstrates the feasibility of this solution for custom made image acquisition systems.

UNLABELLED: Linkage disequilibrium (LD) maps increase power and precision in association mapping, define optimal marker spacing and identify recombination hot-spots and regions influenced by natural selection. Phase II of HapMap provides approximately 2.8-fold more single nucleotide polymorphisms (SNPs) than phase I for constructing higher resolution maps. LDMAP-cluster, is a parallel program for rapid map construction in a Linux environment used here to construct genome-wide LD maps with >8.2 million SNPs from the phase II data. AVAILABILITY: The LD maps, LDMAP-cluster and documentation are available from: http://www.som.soton.ac.uk/research/geneticsdiv/epidemiology/LDMAP. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Abstract Grid application developers utilize Commodity Grid (CoG) toolkits to access Globus Grid services. Existing CoG toolkits are language‐specific and have, for example, been developed for Java, Python and the Matlab scripting environment. In this paper we describe MyCoG.NET, a CoG toolkit supporting multi‐language programmability under the Microsoft .NET framework. MyCoG.NET provides a set of classes and APIs to access Globus Grid services from languages supported by the .NET Common Language Runtime. We demonstrate its programmability using FORTRAN, C++, C# and Java, and discuss its performance over LAN and WAN infrastructures. We present a Grid application, in the field of experimental aerodynamics, as a case study to show how MyCoG.NET can be exploited. We demonstrate how scientists and engineers can create and use domain‐specific workflow activity sets for rapid application development using Windows Workflow Foundation. We also show how users can easily extend and customize these activities. Copyright © 2006 John Wiley & Sons, Ltd.

Abstract In this paper, we present the Grid enabled data management system that has been deployed for the Grid ENabled Integrated Earth system model (GENIE) project. The database system is an augmented version of the Geodise Database Toolbox and provides a repository for scripts, binaries and output data in the GENIE framework. By exploiting the functionality available in the Geodise toolboxes we demonstrate how the database can be employed to tune parameters of coupled GENIE Earth System Model components to improve their match with observational data. A Matlab client provides a common environment for the project Virtual Organization and allows the scripting of bespoke tuning studies that can exploit multiple heterogeneous computational resources. We present the results of a number of tuning exercises performed on GENIE model components using multi‐dimensional optimization methods. In particular, we find that it is possible to successfully tune models with up to 30 free parameters using Kriging and Genetic Algorithm methods. Copyright © 2006 John Wiley & Sons, Ltd.

The tuning of parameters in climate models is essential to provide reliable long-term forecasts of Earth system behaviour. In this paper we present the first application of the multiobjective non-dominated sorting genetic algorithm (NSGA-II) to the GENIE-1 Earth System Model (ESM). Twelve model parameters are tuned to improve four objective measures of fitness to observational data. Grid computing and data handling technology is harnessed to perform the concurrent simulations that comprise the generations of the genetic algorithm. Recent advances in the method exploit Response Surface Modelling to provide surrogate models of each objective. This enables more extensive and efficient searching of the design space. We assess the performance of the NSGA-II using surrogates by repeating a tuning exercise that has been performed using a proximal analytical centre plane cutting method and the Ensemble Kalman Filter on the GENIE-1 model. We find that the multiobjective algorithm locates Pareto-optimal solutions which are of comparable quality to those obtained using the single objective optimisation methods.

In this paper we propose a distributed knowledge management framework for semantics and knowledge creation, population, and reuse on the grid. Its objective is to evolve the Grid toward the Semantic Grid with the ultimate purpose of facilitating problem solving in e‐Science. The framework uses ontology as the conceptual backbone and adopts the service‐oriented computing paradigm for information‐ and knowledge‐level computation. We further present a semantics‐based approach to problem solving, which exploits the rich semantic information of grid resource descriptions for resource discovery, instantiation, and composition. The framework and approach has been applied to a UK e‐Science project—Grid Enabled Engineering Design Search and Optimisation in Engineering (GEODISE). An ontology‐enabled problem solving environment (PSE) has been developed in GEODISE to leverage the semantic content of GEODISE resources and the Semantic Grid infrastructure for engineering design. Implementation and initial experimental results are reported.

This paper presents the experiences gained from ongoing research collaboration between the School of Computer Engineering at Nanyang Technological University and the Southampton e-Science centre at the University of Southampton using a service-oriented approach for complex engineering design optimisation. The service-oriented approach enables programmatic collaboration to be realized while maintaining the autonomy of individual codes at the different institutes and organizations. In the current work, a genetic algorithm optimisation logic implemented as a grid service at Southampton is used to drive the design search process, while the aerodynamic analysis code located in Singapore is used to evaluate the objective function of the design points. Experience gathered from the current study on airfoil shape optimisation is valuable for establishing effective, efficient and customised programmatic links between institutions to solve complicated engineering design problems.

The future success of Grid-enabled e-Science depends on the availability of semantic/knowledge-rich resources on the Grid, i.e., the so-called semantic Grid. This requires not only novel knowledge modelling and representation formalisms but also a shift of knowledge acquisition and population from a limited number of specialised knowledge engineers to resource providers. To this end we have developed a lightweight ontology-enabled tool, "Function Annotator", to support resource providers in capturing and publishing resource semantics and knowledge. Function Annotator takes a different line to most knowledge acquisition tools in that it is designed for use by resource providers, probably in the absence of a knowledge engineer. Its aim is to facilitate large scale knowledge population on the Grid. Function Annotator is built on the state-of-the-art of semantic web technologies, such as ontologies, OWL, instance store and DL-based reasoning, thus ensuring flexibility and scalability on the Grid. This paper describes the tool's role in a Grid-oriented resource lifecycle, its underlying technologies and implementation. It also illustrates the usage of the tool in the context of engineering design search and optimisation.

Open-standard Web technologies, such as XML and Web Services, offer new opportunities for the construction of Grid infrastructures that are flexible and extensible. As the Grid community focuses on the development of open grid computing protocols, it is important to consider how existing (and emerging) open standard technologies could be exploited and deployed effectively on the Grid. In this paper we introduce our work on applying the Web technologies for the construction of a Grid component that wraps the Condor distributed computing system and its application in a Grid scenario. Furthermore we propose a model for building Web Services on the Grid

We are developing a Grid Enabled Optimisation and Design Search system (GEODISE). It offers grid-based access to a state-of-the-art collection of optimisation and design search tools, industrial strength analysis codes, and distributed computing and data resources.