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13th IEEE Annual Information Technology, Electronics and Mobile Communication Conference, IEMCON 2022

作者： Singh, Raghubir Kiss, Tamas Department of Computer Science and Engineering Centre for Parallel Computing University of Westminster London United Kingdom

ISBN: (数字)9781665463164

ISBN: (纸本)9781665463164

If an edge-node orchestrator can partition Big Data tasks of variable computational complexity between the edge and cloud resources, major reductions in total task completion times can be achieved even at low Wide Area Network (WAN) speeds. The percentage time savings are greater with increasing task computational complexity and higher WAN speeds are required for low-complexity tasks. We demonstrate from numerical simulations that low-complexity tasks can benefit either by task partitioning between an edge node and multiple cloud servers. The orchestrator can also achieve greater time benefits by rerouting Big Data tasks directly to a single cloud resource if the balance of parameters (WAN speed and the ratio between edge and cloud processing speeds) is favourable. © 2022 IEEE.

关键词： Wide area networks

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Latent Block Regression Model 1

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17th Conference of the International Federation of Classification Societies, IFCS 2022

作者： Boutalbi, Rafika Labiod, Lazhar Nadif, Mohamed Institute for Parallel and Distributed Systems Analytic Computing University of Stuttgart Stuttgart Germany Centre Borelli UMR 9010 Université Paris Cité Paris France

ISBN: (数字)9783031090349

ISBN: (纸本)9783031090332

When dealing with high dimensional sparse data, such as in recommender systems,co-clusteringturnsouttobemorebeneficialthanone-sidedclustering,even if one is interested in clustering along one dimension only. Thereby, co-clusterwise is a natural extension of clusterwise. Unfortunately, all of the existing approaches do not consider covariates on both dimensions of a data matrix. In this paper, we propose a Latent Block Regression Model (LBRM) overcoming this limit. For inference, we propose an algorithm performing simultaneously co-clustering and regression where a linear regression model characterizes each block. Placing the estimate of the model parameters under the maximum likelihood approach, we derive a Variational Expectation–Maximization (VEM) algorithm for estimating the model’s parameters. The finality of the proposed VEM-LBRM is illustrated through simulated datasets. © 2023, The Author(s).

关键词： Data mining

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NVRAM as an Enabler to New Horizons in Graph Processing

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SN Computer Science 2022年第5期3卷 385页

作者： Capelli, Ludovic Anthony Richard Brown, Nicholas Bull, Jonathan Mark School of Informatics The University of Edinburgh Edinburgh United Kingdom Edinburgh Parallel Computing Centre The University of Edinburgh Edinburgh United Kingdom

From the world wide web, to genomics, to traffic analysis, graphs are central to many scientific, engineering, and societal endeavours. Therefore an important question is what hardware technologies are most appropriate to invest in and use for processing graphs, whose sizes now frequently reach terabytes. Non-Volatile Random Access Memory (NVRAM) technology is an interesting candidate enabling organisations to extend the memory in their systems typically by an order of magnitude compared to Dynamic Read Access Memory (DRAM) alone. From a software perspective, it permits to store a much larger dataset within a single memory space and avoid the significant communication cost incurred when going off node. However, to obtain optimal performance one must consider carefully how best to integrate this technology with their code to cope with NVRAM esoteric properties such as asymmetric read/write performance or explicit coding for deeper memory hierarchies for instance. In this paper, we investigate the use of NVRAM in the context of shared memory graph processing via vertex-centric. We find that NVRAM enables the processing of exceptionally large graphs on a single node with good performance, price and power consumption. We also explore the techniques required to most appropriately exploit NVRAM for graph processing and, for the first time, demonstrate the ability to process a graph of 750 billion edges whilst staying within the memory of a single node. Whilst the vertex-centric graph processing methodology is our main focus, not least due to its popularity since introduced by Google over a decade ago, the lessons learnt in this paper apply more widely to graph processing in general. © 2022, The Author(s).

关键词： DCPMM NVRAM Vertex-centric

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Enabling modular design of an application-level auto-scaling and orchestration framework using TOSCA-based application description templates 11

Enabling modular design of an application-level auto-scaling...

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11th International Workshop on Science Gateways, IWSG 2019

作者： DesLauriers, James Kiss, Tamas Pierantoni, Gabriele Gesmier, Gregoire Terstyanszky, Gabor Centre for Parallel Computing University of Westminster London United Kingdom

This paper presents a novel approach to writing TOSCA templates for application reusability and portability in a modular auto-scaling and orchestration framework (MiCADO). The approach defines cloud resources as well as application containers in a flexible and generic way, and allows for those definitions to be extended with specific properties related to a desired container orchestrator chosen at deployment time. The approach is demonstrated in a proof-of-concept where only a minor change was required to a previously used application template in order to achieve the successful deployment and lifecycle management of the popular web authoring tool Wordpress on a new realization of the MiCADO framework featuring a different container orchestrator. Copyright © 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Containers

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Big data and machine learning framework for clouds and its usage for text classification

Big data and machine learning framework for clouds and its u...

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作者： Pintye, István Kail, Eszter Kacsuk, Péter Lovas, Róbert Budapest Hungary Centre for Parallel Computing University of Westminster London United Kingdom John von Neumann Faculty of Informatics Óbuda University Budapest Hungary

Reference architectures for big data and machine learning include not only interconnected building blocks but important considerations (among others) for scalability, manageability and usability issues as well. Leveraging on such reference architectures, the automated deployment of distributed toolsets and frameworks on various clouds is still challenging due to the diversity of technologies and protocols. The paper focuses particularly on the widespread Apache Spark cluster with Jupyter as the particularly addressed framework, and the Occopus cloud-agnostic orchestrator tool for automating its deployment and maintenance stages. The presented approach has been demonstrated and validated with a new, promising text classification application on the Hungarian academic research infrastructure, the OpenStack-based MTA Cloud. The paper explains the concept, the applied components, and illustrates their usage with real use-case measurements. © 2020 John Wiley & Sons, Ltd.

关键词： Big data

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Unbiased Deep Solvers for Linear Parametric PDEs

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Applied Mathematical Finance 2021年第4期28卷 299-329页

作者： Sabate Vidales, Marc Šiška, David Szpruch, Lukasz Edinburgh Parallel Computing Centre University of Edinburgh London United Kingdom School of Mathematics University of Edinburgh Edinburgh United Kingdom Vega Protocol United Kingdom The Alan Turing Institute London United Kingdom

We develop several deep learning algorithms for approximating families of parametric PDE solutions. The proposed algorithms approximate solutions together with their gradients, which in the context of mathematical finance means that the derivative prices and hedging strategies are computed simultaneously. Having approximated the gradient of the solution, one can combine it with a Monte Carlo simulation to remove the bias in the deep network approximation of the PDE solution (derivative price). This is achieved by leveraging the Martingale Representation Theorem and combining the Monte Carlo simulation with the neural network. The resulting algorithm is robust with respect to the quality of the neural network approximation and consequently can be used as a black box in case only limited a-priori information about the underlying problem is available. We believe this is important as neural network-based algorithms often require fair amount of tuning to produce satisfactory results. The methods are empirically shown to work for high-dimensional problems (e.g., 100 dimensions). We provide diagnostics that shed light on appropriate network architectures. © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： control variates deep neural network Monte Carlo method partial differential equations

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Cloud apps to-go: Cloud portability with TOSCA and MiCADO

Cloud apps to-go: Cloud portability with TOSCA and MiCADO

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作者： DesLauriers, James Kiss, Tamas Ariyattu, Resmi C. Dang, Hai-Van Ullah, Amjad Bowden, James Krefting, Dagmar Pierantoni, Gabriele Terstyanszky, Gabor Research Centre for Parallel Computing University of Westminster London United Kingdom Hochschule für Technik und Wirtschaft Berlin Germany University Medical Center Göttingen Germany

As cloud adoption increases, so do the number of available cloud service providers. Moving complex applications between clouds can be beneficial—or other times necessary—but achieving this so-called cloud portability is rarely straightforward. This article presents the adoption of OASIS TOSCA, a standard in the declarative description of cloud applications, to encourage and facilitate cloud portability in MiCADO, an application-level multi-cloud orchestration and auto-scaling framework. The interface to MiCADO is an Application Description Template, which draws from the TOSCA specification to describe an application in MiCADO. The generic design of these templates is presented and their applicability for achieving portability between different container and cloud environments is analysed and evaluated. A proof-of-concept where MiCADO serves as the deployment and execution engine for a Science Gateway in Sleep Healthcare is then described. In this proof-of-concept, MiCADO facilitates the deployment of a complex healthcare application, which is then moved from one cloud service provider to another with only minimal changes to the template which originally described it. This TOSCA-based approach to templates in MiCADO encourages movement between clouds by making cloud portability more approachable. © 2020 The Authors. Concurrency and Computation: Practice and Experience published by John Wiley & Sons Ltd.

关键词： Health care

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A Deep Learning Approach to Geographical Candidate Selection through Toponym Matching 20

A Deep Learning Approach to Geographical Candidate Selection...

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28th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL GIS 2020

作者： Ardanuy, Mariona Coll Hosseini, Kasra McDonough, Katherine Krause, Amrey Van Strien, Daniel Nanni, Federico Alan Turing Institute London United Kingdom Edinburgh Parallel Computing Centre Edinburgh United Kingdom British Library London United Kingdom

ISBN: (纸本)9781450380195

Recognizing toponyms and resolving them to their real-world referents is required to provide advanced semantic access to textual data. This process is often hindered by the high degree of variation in toponyms. Candidate selection is the task of identifying the potential entities that can be referred to by a previously recognized toponym. While it has traditionally received little attention, candidate selection has a significant impact on downstream tasks (i.e. entity resolution), especially in noisy or non-standard text. In this paper, we introduce a deep learning method for candidate selection through toponym matching, using state-of-the-art neural network architectures. We perform an intrinsic toponym matching evaluation based on several datasets, which cover various challenging scenarios (cross-lingual and regional variations, as well as OCR errors) and assess its performance in the context of geographical candidate selection in English and Spanish. © 2020 Owner/Author.

关键词： Deep learning

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Towards a Cloud Native Big Data Platform using MiCADO

Towards a Cloud Native Big Data Platform using MiCADO

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International Symposium on parallel and Distributed computing

作者： Abdelkhalik Mosa Tamas Kiss Gabriele Pierantoni James DesLauriers Dimitrios Kagialis Gabor Terstyanszky Centre for Parallel Computing University of Westminster London UK

ISBN: (数字)9781728189468

ISBN: (纸本)9781728189475

In the big data era, creating self-managing scalable platforms for running big data applications is a fundamental task. Such self-managing and self-healing platforms involve a proper reaction to hardware (e.g., cluster nodes) and software (e.g., big data tools) failures, besides a dynamic resizing of the allocated resources based on overload and underload situations and scaling policies. The distributed and stateful nature of big data platforms (e.g., Hadoop-based cluster) makes the management of these platforms a challenging task. This paper aims to design and implement a scalable cloud native Hadoopbased big data platform using MiCADO, an open-source, and a highly customisable multi-cloud orchestration and auto-scaling framework for Docker containers, orchestrated by Kubernetes. The proposed MiCADO-based big data platform automates the deployment and enables an automatic horizontal scaling (in and out) of the underlying cloud infrastructure. The empirical evaluation of the MiCADO-based big data platform demonstrates how easy, efficient, and fast it is to deploy and undeploy Hadoop clusters of different sizes. Additionally, it shows how the platform can automatically be scaled based on user-defined policies (such as CPU-based scaling).

关键词： Big Data Cloud computing Containers Software Tools Monitoring Task analysis

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A highly scalable approach to solving linear systems using two-stage multisplitting

arXiv

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arXiv 2020年

作者： Brown, Nick Mark Bull, J. Bethune, Iain Edinburgh Parallel Computing Centre James Clerk Maxwell Building Kings Buildings Edinburgh United Kingdom

Iterative methods for solving large sparse systems of linear equations are widely used in many HPC applications. Extreme scaling of these methods can be difficult, however, since global communication to form dot products is typically required at every iteration. To try to overcome this limitation we propose a hybrid approach, where the matrix is partitioned into blocks. Within each block, we use a highly optimised (parallel) conventional solver, but we then couple the blocks together using block Jacobi or some other multisplitting technique that can be implemented in either a synchronous or an asynchronous fashion. This allows us to limit the block size to the point where the conventional iterative methods no longer scale, and to avoid global communication (and possibly synchronisation) across all processes. Our block framework has been built to use PETSc, a popular scientific suite for solving sparse linear systems, as the synchronous intra-block solver, and we demonstrate results on up to 32768 cores of a Cray XE6 system. At this scale, the conventional solvers are still more efficient, though trends suggest that the hybrid approach may be beneficial at higher core counts. Copyright © 2020, The Authors. All rights reserved.

关键词： Iterative methods

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