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Efficient task scheduling in the parallel result-verifying solution of nonlinear systems

Reliable computing 2006年第2期12卷 141-151页

作者： Beelitz, Thomas Lang, Bruno Bischof, Christian H. Bergische Universität Wuppertal Institute for Applied Computer Science and Scientific Computing D-42097 Wuppertal Germany RWTH Aachen University Institute for Scientific Computing D-52056 Aachen Germany

Nonlinear systems occur in diverse applications, e.g., in the steady state analysis of chemical processes. If safety concerns require the results to be provably correct then result-verifying algorithms relying on interval arithmetic should be used for solving these systems. Since such algorithms are very computationally intensive, the coarse-grained inter-box parallelism should be exploited to make them feasible in practice. In this paper we briefly describe our framework SONIC for the verified solution of nonlinear systems and give detailed information about its parallelization with OpenMP and MPI. Our numerical results show that the implemented parallelization schemes are indeed successful. The more sophisticated MPI implementation seems to be superior to the easy-to-implement OpenMP version and shows almost linear speedup up to a large number of processors. © Springer 2006.

关键词： Nonlinear systems

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THE CAMAS WORKBENCH - computer-AIDED MIGRATION OF APPLICATIONS SYSTEM

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FUTURE GENERATION computer SYSTEMS 1994年第2-3期10卷 305-308页

作者： DERONDE, JF SLOOT, PMA BEEMSTER, M HERTZBERGER, LO Parallel Scientific Computing and Simulation Group Department of Computer Science - University of Amsterdam Kruislaan 403 1098 SJ Amsterdam The Netherlands

A simulation methodology for predicting the performance of sequential programs as well as data parallel (SPMD) programs on parallel and distributed platforms is presented. The proposed methodology comprises a parameterised description of the applications as well as the target machines. An integrated simulation procedure estimates the time complexity of the (SPMD) application given a well-defined hardware platform and predicts the execution behaviour. The methodology has been actualised in terms of a toolset currently under development at the University of Amsterdam.

关键词： PERFORMANCE ESTIMATION MASSIVELY PARALLEL PLATFORMS SPMD

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A Novel Framework of Horizontal-Vertical Hybrid Federated Learning for EdgeIoT

IEEE Networking Letters

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IEEE Networking Letters 2025年

作者： Li, Kai Liang, Yilei Yuan, Xin Ni, Wei Crowcroft, Jon Yuen, Chau Akan, Ozgur B. Group Department of Engineering University of Cambridge CambridgeCB3 0FA United Kingdom Real-Time and Embedded Computing Systems Research Centre CISTER Porto4249-015 Portugal The Department of Computer Science and Technology University of Cambridge CambridgeCB3 0FA United Kingdom Commonwealth Scientific and Industrial Research Organization CSIRO SydneyNSW2122 Australia The School of Computing Science and Engineering University of New South Wales Australia Nanyang Technological University SG 639798 Singapore Group Division of Electrical Engineering Department of Engineering University of Cambridge CambridgeCB3 0FA United Kingdom The Center for neXt-generation Communications CXC Koç University Istanbul34450 Turkey

This letter puts forth a new hybrid horizontal-vertical federated learning (HoVeFL) for mobile edge computing-enabled Internet of Things (EdgeIoT). In this framework, certain EdgeIoT devices train local models using the same data samples but analyze disparate data features, while the others focus on the same features using non-independent and identically distributed (non-IID) data samples. Thus, even though the data features are consistent, the data samples vary across devices. The proposed HoVeFL formulates the training of local and global models to minimize the global loss function. Performance evaluations on CIFAR-10 and SVHN datasets reveal that the testing loss of HoVeFL with 12 horizontal FL devices and six vertical FL devices is 5.5% and 25.2% higher, respectively, compared to a setup with six horizontal FL devices and 12 vertical FL devices. © 2019 IEEE.

关键词： Federated learning

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EHands: Quantum Protocol for Polynomial Computation on Real-Valued Encoded States

arXiv

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arXiv 2025年

作者： Balewski, Jan Amankwah, Mercy G. Bethel, E. Wes Perciano, Talita Van Beeumen, Roel National Energy Research Scientific Computing Center Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Department of Mathematics Applied Mathematics and Statistics Case Western Reserve University ClevelandOH44106 United States Computer Science Department San Francisco State University San FranciscoCA94132 United States Scientific Data Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States

The novel constructive EHands protocol defines a universal set of quantum operations for multivariable polynomial transformations on quantum processors by introducing four basic subcircuits—multiplication, addition, negation, and parity flip—and by using expectation-value encoding (EVEN) to represent real numbers in quantum states. These elementary arithmetic operations can be systematically composed to compute degree-d polynomials, Pd(x), on a QPU. The resulting quantum circuit structure closely mirrors the stepwise evaluation of polynomials on a classical calculator, providing an intuitive and efficient approach to polynomial computation on quantum hardware. By enabling direct, predictable polynomial and nonlinear data transformations on a QPU, our method reduces dependence on classical post-processing in hybrid quantum-classical algorithms, enabling advancements in many quantum algorithms. The EHands quantum circuits are compact enough to deliver meaningful and accurate results on today’s noisy quantum processors. We present a detailed implementation of P4(x) and report experimental results for polynomial approximations of common functions, obtained using IBM’s Heron-class quantum processors and an ideal Qiskit simulator. Copyright © 2025, The Authors. All rights reserved.

关键词： Polynomial approximation

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Convolutional Neural Network for Cancer Treatment Response Prediction 18

Convolutional Neural Network for Cancer Treatment Response P...

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18th International computer Engineering Conference, ICENCO 2022

作者： Ahmed, Hanan Shedeed, Howida A. Hamad, Safwat Hussein, Ashraf S. Faculty of Computer and Information Science Scientific Computing Cairo Egypt

ISBN: (纸本)9781665458955

Recently, efforts are exerted on cancer treatment prediction based on the biomarkers related to the tumor. Many biomarkers are used to discriminate cancer but there is no accurate evidence which is more accurate. To save resources and due to the enormous computational power, the analysis of huge sequences such as DNA (DesoxyriboNucleic Acid) becomes a valid process. Machine learning and deep learning algorithms have been used to predict drug response. The indicators show that the performance of deep learning models is better than the performance of machine learning based one. In this paper, we introduce the use of Convolutional Neural Network (CNN) to predict the response of 265 different drugs using the gene expression profile and mutation profile of 19702 genes. DeepInsight algorithm used to convert the input data to images to be more suitable as input to the CNN. We proposed eight CNNs with different architectures. At first, the whole data input passes through the DeepInsight model which converts the gene expression data and mutation data to images. Dimension reduction is then applied using the helper technique inside the DeepInsignt. The CNN are trained with different settings such as image size, no of nodes in layers...etc. Comparative analysis shows that the proposed architectures improve the prediction accuracy in a range between 16% and 24% as a reduction in mean squared error (MSE). © 2022 IEEE.

关键词： Mean square error

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Blockchain-Assisted Trust Framework for Increased Survivability in Internet of Vehicles

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Internet Technology Letters 2025年第3期8卷

作者： Sharma, Kanta Prasad Al-Rashed, Atheer Johar, M.G.M. Alsoud, Anas Ratib Singh, Amrita Dey, Protyay Pant, Shivani Amity University Uttar Pradesh Greater Noida India College of Administrative Sciences Applied Science University Al Eker Bahrain Management and Science University Selangor Shah Alam Malaysia Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan Department of Computer Science and Engineering Chandigarh Engineering College Chandigarh Group of Colleges-Jhanjeri Mohali India NIMS School of Computing Science and Artificial Intelligence NIMS University Rajasthan Jaipur India Uttaranchal Institute of Technology Uttaranchal University Dehradun India

VANETs enhance traffic efficiency and road safety, but they can be attacked by malicious vehicles. These malicious vehicles can cause accidents or endanger lives by broadcasting false event messages and disrupting Internet of Vehicles applications. Before responding to sender messages, receiver vehicles must estimate the legitimacy and trustworthiness of the source vehicles. Existing solutions struggle to balance security and efficiency effectively. This paper introduces a model that combines the advantages of blockchain technology and trust model models to improve the trustworthiness, efficacy, and security of vehicular networks, alongside secure trust-based optimized routing. Extensive experiments demonstrate the security and efficiency of the proposed model. The proposed model is adaptable to diverse VANET scenarios, addressing all security and privacy needs more comprehensively than current trust schemes. Efficiency analysis and simulation results show that our proposed framework outperforms baseline models, highlighting its security, effectiveness, and robustness in enhancing IoV communication security. © 2025 John Wiley & Sons Ltd.

关键词： Highway accidents

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An Improved Robust Sparse Convex Clustering

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Tsinghua science and Technology 2023年第6期28卷 989-998页

作者： Jinyao Ma Haibin Zhang Shanshan Yang Jiaojiao Jiang Gaidi Li Beijing Institute for Scientific and Engineering Computing Faculty of ScienceBeijing University of TechnologyBeijing 100124China School of Computer Science and Engineering University of New South WalesSydney 2052Australia

Convex clustering,turning clustering into a convex optimization problem,has drawn wide *** overcomes the shortcomings of traditional clustering methods such as K-means,Density-Based Spatial Clustring of Applications with Noise(DBSCAN)and hierarchical clustering that can easily fall into the local optimal ***,convex clustering is vulnerable to the occurrence of outlier features,as it uses the Frobenius norm to measure the distance between data points and their corresponding cluster centers and evaluate *** accurately identify outlier features,this paper decomposes data into a clustering structure component and a normalized component that captures outlier *** from existing convex clustering evaluating features with the exact measurement,the proposed model can overcome the vast difference in the magnitude of different features and the outlier features can be efficiently identified and *** solve the proposed model,we design an efficient algorithm and prove the global convergence of the *** on both synthetic datasets and UCI datasets demonstrate that the proposed method outperforms the compared approaches in convex clustering.

关键词： convex clustering outlier features block coordinate descent Newton’s method

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A dynamic load balancing system for parallel cluster computing

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FUTURE GENERATION computer SYSTEMS-THE INTERNATIONAL JOURNAL OF GRID computing AND Escience 1996年第1期12卷 101-115页

作者： Overeinder, BJ Sloot, PMA Heederik, RN Hertzberger, LO Parallel Scientific Computing and Simulation Group Department of Computer Science University of Amsterdam Kruislaan 403 1098 SJ Amsterdam Netherlands

In this paper we discuss a new approach to dynamic load balancing of parallel jobs in clusters of workstations and describe the implementation into a Unix run-time environment. The efficiency of the proposed methodolo... 详细信息

关键词： dynamic load balancing task migration distributed computing systems clusters of workstations

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An adaptive framework for the execution of data-intensive MapReduce applications in the Cloud

An adaptive framework for the execution of data-intensive Ma...

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25th IEEE International Parallel and Distributed Processing Symposium, Workshops and Phd Forum, IPDPSW 2011

作者： Koehler, Martin Kaniovskyi, Yuriy Benkner, Siegfried University of Vienna Faculty of Computer Science Department of Scientific Computing Austria

ISBN: (纸本)9780769543857

Cloud computing technologies play an increasingly important role in realizing data-intensive applications by offering a virtualized compute and storage infrastructure that can scale on demand. A programming model that has gained a lot of interest in this context is MapReduce, which simplifies processing of large-scale distributed data volumes, usually on top of a distributed file system layer. In this paper we report on a self-configuring adaptive framework for developing and optimizing data-intensive scientific applications on top of Cloud and Grid computing technologies and the Hadoop framework. Our framework relies on a MAPE-K loop, known from autonomic computing, for optimizing the configuration of data-intensive applications at three abstraction layers: the application layer, the MapReduce layer, and the resource layer. By evaluating monitored resources, the framework configures the layers and allocates the resources on a per job basis. The evaluation of configurations relies on historic data and a utility function that ranks different configurations regarding to the arising costs. The optimization framework has been integrated in the Vienna Grid Environment (VGE), a service-oriented application development environment for providing applications on HPC systems, clusters and Clouds as services. An experimental evaluation of our framework has been undertaken with a data-analysis application from the field of molecular systems biology. © 2011 IEEE.

关键词： MapReduce

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Scalable relational query results handling in service oriented architectures

Scalable relational query results handling in service orient...

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International Conference for Internet Technology and Secured Transactions, ICITST 2009

作者： Wöhrer, Alexander Lustig, Thomas Brezany, Peter Institute of Scientific Computing Faculty of Computer Science University of Vienna Austria

ISBN: (纸本)9781424456482

The focus of Grid computing shifted in recent years towards data-intensive applications. Additionally, it became more standard-based and the adherence to and usage of them has to be further fostered in order to enable and ease exchange and interoperability among the research community. The OGSA-DAI framework for data access and integration together with its default XML output format for relational data, namely Java's WebRowSet, are two important representatives of these developments. The research reported in this paper is being conducted in the context of the EU project ADMIRE. The paper's contribution is twofold and targeted on improving the scalability of query result handling in SoA. First, an XML indexing approach is elaborated to provide an out-of-core Java implementation of the standard Java WebRowSet interface to handle big XML files with low main memory consumption. Second, the concept of fine grained data statistics for data preprocessing tasks calculated on-the-fly data service side is presented. Copyright © 2009 by the Institute of Electrical and Electronics Engineers, Inc. All rights reserved.

关键词： XML

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