Leveraging HTC for UK eScience with very large Condor pools: demand for transforming untapped power into results

We provide an insight into the demand from the UK eScience community for very large HighThroughput Computing resources and provide an example of such a resource in current productionuse: the 930-node eMinerals Condor pool at UCL. We demonstrate the significant benefits thisresource has provided to UK eScientists via quickly and easily realising results throughout a rangeof problem areas. We demonstrate the value added by the pool to UCL I.S infrastructure andprovide a case for the expansion of very large Condor resources within the UK eScience Gridinfrastructure. We provide examples of the technical and administrative difficulties faced whenscaling up to institutional Condor pools, and propose the introduction of a UK Condor/HTCworking group to co-ordinate the mid to long term UK eScience Condor development, deploymentand support requirements, starting with the inaugural UK Condor Week in October 2004

Investigation of the Effects of Image Signal-to-Noise Ratio on TSPO PET Quantification of Neuroinflammation

Neuroinflammation may be imaged using positron emission tomography (PET) and the tracer [11C]-PK11195. Accurate and precise quantification of 18 kilodalton Translocator Protein (TSPO) binding parameters in the brain has proven difficult with this tracer, due to an unfavourable combination of low target concentration in tissue, low brain uptake of the tracer and relatively high non-specific binding, all of which leads to higher levels of relative image noise. To address these limitations, research into new radioligands for the TSPO, with higher brain uptake and lower non-specific binding relative to [11C]-PK11195, is being conducted world-wide. However, factors other than radioligand properties are known to influence signal-to-noise ratio in quantitative PET studies, including the scanner sensitivity, image reconstruction algorithms and data analysis methodology. The aim of this thesis was to investigate and validate computational tools for predicting image noise in dynamic TSPO PET studies, and to employ those tools to investigate the factors that affect image SNR and reliability of TSPO quantification in the human brain. The feasibility of performing multiple (n≥40) independent Monte Carlo simulations for each dynamic [11C]-PK11195 frame- with realistic modelling of the radioactivity source, attenuation and PET tomograph geometries- was investigated. A Beowulf-type high performance computer cluster, constructed from commodity components, was found to be well suited to this task. Timing tests on a single desktop computer system indicated that a computer cluster capable of simulating an hour-long dynamic [11C]-PK11195 PET scan, with 40 independent repeats, and with a total simulation time of less than 6 weeks, could be constructed for less than 10,000 Australian dollars. A computer cluster containing 44 computing cores was therefore assembled, and a peak simulation rate of 2.84x105 photon pairs per second was achieved using the GEANT4 Application for Tomographic Emission (GATE) Monte Carlo simulation software. A simulated PET tomograph was developed in GATE that closely modelled the performance characteristics of several real-world clinical PET systems in terms of spatial resolution, sensitivity, scatter fraction and counting rate performance. The simulated PET system was validated using adaptations of the National Electrical Manufacturers Association (NEMA) quality assurance procedures within GATE. Image noise in dynamic TSPO PET scans was estimated by performing n=40 independent Monte Carlo simulations of an hour-long [11C]-PK11195 scan, and of an hour- long dynamic scan for a hypothetical TSPO ligand with double the brain activity concentration of [11C]-PK11195. From these data an analytical noise model was developed that allowed image noise to be predicted for any combination of brain tissue activity concentration and scan duration. The noise model was validated for the purpose of determining the precision of kinetic parameter estimates for TSPO PET. An investigation was made into the effects of activity concentration in tissue, radionuclide half-life, injected dose and compartmental model complexity on the reproducibility of kinetic parameters. Injecting 555 MBq of carbon-11 labelled TSPO tracer produced similar binding parameter precision to 185 MBq of fluorine-18, and a moderate (20%) reduction in precision was observed for the reduced carbon-11 dose of 370 MBq. Results indicated that a factor of 2 increase in frame count level (relative to [11C]-PK11195, and due for example to higher ligand uptake, injected dose or absolute scanner sensitivity) is required to obtain reliable binding parameter estimates for small regions of interest when fitting a two-tissue compartment, four-parameter compartmental model. However, compartmental model complexity had a similarly large effect, with the reduction of model complexity from the two-tissue compartment, four-parameter to a one-tissue compartment, two-parameter model producing a 78% reduction in coefficient of variation of the binding parameter estimates at each tissue activity level and region size studied. In summary, this thesis describes the development and validation of Monte Carlo methods for estimating image noise in dynamic TSPO PET scans, and analytical methods for predicting relative image noise for a wide range of tissue activity concentration and acquisition durations. The findings of this research suggest that a broader consideration of the kinetic properties of novel TSPO radioligands, with a view to selection of ligands that are potentially amenable to analysis with a simple one-tissue compartment model, is at least as important as efforts directed towards reducing image noise, such as higher brain uptake, in the search for the next generation of TSPO PET tracers

SLA-Oriented Resource Provisioning for Cloud Computing: Challenges, Architecture, and Solutions

Cloud computing systems promise to offer subscription-oriented, enterprise-quality computing services to users worldwide. With the increased demand for delivering services to a large number of users, they need to offer differentiated services to users and meet their quality expectations. Existing resource management systems in data centers are yet to support Service Level Agreement (SLA)-oriented resource allocation, and thus need to be enhanced to realize cloud computing and utility computing. In addition, no work has been done to collectively incorporate customer-driven service management, computational risk management, and autonomic resource management into a market-based resource management system to target the rapidly changing enterprise requirements of Cloud computing. This paper presents vision, challenges, and architectural elements of SLA-oriented resource management. The proposed architecture supports integration of marketbased provisioning policies and virtualisation technologies for flexible allocation of resources to applications. The performance results obtained from our working prototype system shows the feasibility and effectiveness of SLA-based resource provisioning in Clouds.Comment: 10 pages, 7 figures, Conference Keynote Paper: 2011 IEEE International Conference on Cloud and Service Computing (CSC 2011, IEEE Press, USA), Hong Kong, China, December 12-14, 201

An R*-Tree Based Semi-Dynamic Clustering Method for the Efficient Processing of Spatial Join in a Shared-Nothing Parallel Database System

The growing importance of geospatial databases has made it essential to perform complex spatial queries efficiently. To achieve acceptable performance levels, database systems have been increasingly required to make use of parallelism. The spatial join is a computationally expensive operator. Efficient implementation of the join operator is, thus, desirable. The work presented in this document attempts to improve the performance of spatial join queries by distributing the data set across several nodes of a cluster and executing queries across these nodes in parallel. This document discusses a new parallel algorithm that implements the spatial join in an efficient manner. This algorithm is compared to an existing parallel spatial-join algorithm, the clone join. Both algorithms have been implemented on a Beowulf cluster and compared using real datasets. An extensive experimental analysis reveals that the proposed algorithm exhibits superior performance both in declustering time as well as in the execution time of the join query

An Integrated Pipeline of Open Source Software Adapted for Multi-CPU Architectures: Use in the Large-Scale Identification of Single Nucleotide Polymorphisms

The large amounts of EST sequence data available from a single species of an organism as well as for several species within a genus provide an easy source of identification of intra- and interspecies single nucleotide polymorphisms (SNPs). In the case of model organisms, the data available are numerous, given the degree of redundancy in the deposited EST data. There are several available bioinformatics tools that can be used to mine this data; however, using them requires a certain level of expertise: the tools have to be used sequentially with accompanying format conversion and steps like clustering and assembly of sequences become time-intensive jobs even for moderately sized datasets. We report here a pipeline of open source software extended to run on multiple CPU architectures that can be used to mine large EST datasets for SNPs and identify restriction sites for assaying the SNPs so that cost-effective CAPS assays can be developed for SNP genotyping in genetics and breeding applications. At the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), the pipeline has been implemented to run on a Paracel high-performance system consisting of four dual AMD Opteron processors running Linux with MPICH. The pipeline can be accessed through user-friendly web interfaces at http://hpc.icrisat.cgiar.org/PBSWeb and is available on request for academic use. We have validated the developed pipeline by mining chickpea ESTs for interspecies SNPs, development of CAPS assays for SNP genotyping, and confirmation of restriction digestion pattern at the sequence level

Performance measurement and analysis of PC based cluster server using SET of Architecture and modeling a scalable High performance cluster

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GRIDCC: Real-time workflow system

The Grid is a concept which allows the sharing of resources between distributed communities, allowing each to progress towards potentially different goals. As adoption of the Grid increases so are the activities that people wish to conduct through it. The GRIDCC project is a European Union funded project addressing the issues of integrating instruments into the Grid. This increases the requirement of workflows and Quality of Service upon these workflows as many of these instruments have real-time requirements. In this paper we present the workflow management service within the GRIDCC project which is tasked with optimising the workflows and ensuring that they meet the pre-defined QoS requirements specified upon them