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2010 6th International Conference on computer Engineering and Systems, ICCES'2010

作者： Abdeen, Mohammad Tolba, Mohamed F. Department of Computer Science Faculty of Computers and Information Systems Ain-Shams University Cairo Egypt Department of Scientific Computing Faculty of Computers and Information Systems Ain-Shams University Cairo Egypt

ISBN: (纸本)9781424470426

The world-wide-web has become the favorite destiny of information seekers across the globe. With its massive amount of information that includes billions of web pages, information for just about any topic is a click-of-finger away. The Arabic web represents an important portion of the web. With Arabic as the 5th most spoken language in the world and with the increasing number of Arabic internet users at exponential rates, it is becoming important to make the Arabic web content available. Many search engines exist such as Google, Msn, and Yahoo. However, these search engines are not designed with proper consideration of the Arabic language and its special nature. This paper presents some of the work while developing an Arabic search engine. The web crawler is the focus of this paper. We present important challenges and design issues of the design of a distributed web crawler for the Arabic search engine we are developing. We also uncover some of the facts about the Arabic web. ©2010 IEEE.

关键词： Web crawler

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Detection of Arbitrarily Oriented Synchronized Clusters in high-dimensional data

Detection of Arbitrarily Oriented Synchronized Clusters in h...

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11th IEEE International Conference on Data Mining, ICDM 2011

作者： Shao, Junming Planty, Claudia Yangz, Qinli Böhm, Christian Institute for Computer Science University of Munich Munich Germany Department of Scientific Computing Florida State University Tallahassee FL United States School of Engineering University of Edinburgh Edinburgh United Kingdom

ISBN: (纸本)9780769544083

How to address the challenges of the "curse of dimensionality" in clustering? Clustering is a powerful data mining technique for structuring and organizing vast amounts of data. However, the high-dimensional data space is usually very sparse and meaningful clusters can only be found in lower dimensional subspaces. In many applications the subspaces hosting the clusters provide valuable information for interpreting the major patterns in the data. Detection of subspace clusters is challenging since usually many of the attributes are noisy, some attributes may exhibit correlations among each other and only few of the attributes truly contribute to the cluster structure. In this paper, we propose ORSC (Arbitrarily ORiented Synchronized Clusters), a novel effective and efficient method to subspace clustering inspired by synchronization. Synchronization is a basic phenomenon prevalent in nature, capable of controlling even highly complex processes such as opinion formation in a group. Control of complex processes is achieved by simple operations based on interactions between objects. Relying on the interaction model for synchronization, our approach ORSC (1) naturally detects correlation clusters in arbitrarily oriented subspaces, including (2) arbitrarilyshaped non-linear correlation clusters. Our approach is (3) robust against noise points and outliers. In contrast to previous methods, ORSC is (4) easy to parameterize, since there is no need to specify the subspace dimensionality and all interesting subspace clusters can be detected. Finally,(5) ORSC outperforms most comparison methods in terms of runtime efficiency and is highly scalable to large and high-dimensional data sets. © 2011 IEEE.

关键词： Synchronization

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Partitioned fluid–structure–acoustics interaction on distributed data: Numerical results and visualization

Partitioned fluid–structure–acoustics interaction on distr...

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International Conference on Software for Exascale computing, SPPEXA 2015

作者： Blom, David Ertl, Thomas Fernandes, Oliver Frey, Steffen Klimach, Harald Krupp, Verena Mehl, Miriam Roller, Sabine Sternel, Dörte C. Uekermann, Benjamin Winter, Tilo van Zuijlen, Alexander Aerospace Engineering Delft University of Technology Delft Netherlands Scientific Computing in Computer Science Technical University of Munich München Germany Simulation Techniques and Scientific Computing University of Siegen Siegen Germany Institute for Parallel and Distributed Systems University of Stuttgart Stuttgart Germany VISUS University of Stuttgart Stuttgart Germany Institut for Scientific Computing TU Darmstadt Darmstadt Germany Institute of Numerical Methods in Mechanical Engineering TU Darmstadt Darmstadt Germany

ISBN: (纸本)9783319405261

We present a coupled simulation approach for fluid–structure–acoustic interactions (FSAI) as an example for strongly surface coupled multi-physics problems. In addition to the multi-physics character, FSAI feature multi-scale properties as a further challenge. In our partitioned approach, the problem is split into spatially separated subdomains interacting via coupling surfaces. Within each subdomain, scalable, single-physics solvers are used to solve the respective equation systems. The surface coupling between them is realized with the scalable open-source coupling tool preCICE described in the "Partitioned Fluid–Structure– Acoustics Interaction on Distributed Data: Coupling via preCICE". We show how this approach enables the use of existing solvers and present the overall scaling behavior for a three-dimensional test case with a bending tower generating acoustic *** run this simulation with different solvers demonstrating the performance of various solvers and the flexibility of the partitioned approach with the coupling tool preCICE. An efficient and scalable in-situ visualization reducing the amount of data in place at the simulation processors before sending them over the network or to a file system completes the simulation environment. © Springer International Publishing Switzerland 2016.

关键词： Data visualization

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Computational Life sciences 1

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丛书名： Studies in Big Data

1000年

作者： Jens Dörpinghaus Sebastian Schaaf Vera Weil Alexander Apke

ISBN: (数字)9783031084119

ISBN: (纸本)9783031084102;9783031084133

This book broadly covers the given spectrum of disciplines in Computational Life sciences, transforming it into a strong helping hand for teachers, students, practitioners and researchers. In Life sciences, problem-solving and data analysis often depend on biological expertise combined with technical skills in order to generate, manage and efficiently analyse big data. These technical skills can easily be enhanced by good theoretical foundations, developed from well-chosen practical examples and inspiring new strategies. This is the innovative approach of Computational Life sciences-Data Engineering and Data Mining for Life sciences: We present basic concepts, advanced topics and emerging technologies, introduce algorithm design and programming principles, address data mining and knowledge discovery as well as applications arising from real projects. Chapters are largely independent and often flanked by illustrative examples and practical advise.

关键词： Data Engineering Bioinformatics Big Data Computational Intelligence

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Proceedings of International Conference on Computational Intelligence 1

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丛书名： Algorithms for Intelligent Systems

1000年

作者： Ritu Tiwari Apoorva Mishra Neha Yadav Mario Pavone

ISBN: (数字)9789811638022

ISBN: (纸本)9789811638015;9789811638046

The book presents high quality research papers presented at International Conference on Computational Intelligence (ICCI 2020) held at Indian Institute of Information Technology, Pune, India during 12–13 December, 2020. The topics covered are artificial intelligence, neural network, deep learning techniques, fuzzy theory and systems, rough sets, self-organizing maps, machine learning, chaotic systems, multi-agent systems, computational optimization ensemble classifiers, reinforcement learning, decision trees, support vector machines, hybrid learning, statistical learning. metaheuristics algorithms: evolutionary and swarm-based algorithms like genetic algorithms, genetic programming, differential evolution, particle swarm optimization, whale optimization, spider monkey optimization, firefly algorithm, memetic algorithms. And also machine vision, Internet of Things, image processing, image segmentation, data clustering, sentiment analysis, big data, computer networks, signal processing,supply chain management, web and text mining, distributed systems, bioinformatics, embedded systems, expert system, forecasting, pattern recognition, planning and scheduling, time series analysis, human-computer interaction, web mining, natural language processing, multimedia systems, and quantum computing.

关键词： Computational Intelligence Artificial Intelligence Cyber-physical systems, IoT Professional computing Algorithms

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HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach 31

HPC Application Parameter Autotuning on Edge Devices: A Band...

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31st Annual IEEE International Conference on High Performance computing, Data, and Analytics, HiPC 2024

作者： Hossain, Abrar Badawy, Abdel-Hameed A. Islam, Mohammad A. Patki, Tapasya Ahmed, Kishwar Department of Electrical Engineering and Computer Science The University of Toledo United States Department of Electrical and Computer Engineering New Mexico State University United States Department of Computer Science and Engineering The University of Texas Arlington United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory United States

ISBN: (纸本)9798331509095

The growing necessity for enhanced processing capabilities in edge devices with limited resources has led us to develop effective methods for improving high-performance computing (HPC) applications. In this paper, we introduce LASP (Lightweight Autotuning of scientific Application Parameters), a novel strategy designed to address the parameter search space challenge in edge devices. Our strategy employs a multi-armed bandit (MAB) technique focused on online exploration and exploitation. Notably, LASP takes a dynamic approach, adapting seamlessly to changing environments. We tested LASP with four HPC applications: Lulesh, Kripke, Clomp, and Hypre. Its lightweight nature makes it particularly well-suited for resource-constrained edge devices. By employing the MAB framework to efficiently navigate the search space, we achieved significant performance improvements while adhering to the stringent computational limits of edge devices. Our experimental results demonstrate the effectiveness of LASP in optimizing parameter search on edge devices. © 2024 IEEE.

关键词： Edge computing

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Comparing CUDA, OpenCL and OpenGL implementations of the cardiac monodomain equations

Comparing CUDA, OpenCL and OpenGL implementations of the car...

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9th International Conference on Parallel Processing and Applied Mathematics, PPAM 2011

作者： Sachetto Oliveira, Rafael Rocha, Bernardo Martins Amorim, Ronan Mendonça Campos, Fernando Otaviano Meira Jr., Wagner Toledo, Elson Magalhães Dos Santos, Rodrigo Weber Federal University of São João del Rey Department of Computer Science Brazil Federal University of Minas Gerais Department of Computer Science Brazil Federal University of Juiz de Fora Department of Computer Science Computational Modeling Program Brazil National Laboratory of Scientific Computing Brazil Medical University of Graz Institute of Biophysics Austria

ISBN: (纸本)9783642314995

computer simulations of cardiac electrophysiology are a helpful tool in the study of bioelectric activity of the heart. The cardiac monodomain model comprises a nonlinear system of partial differential equations and its numerical solution represents a very intensive computational task due to the required fine spatial and temporal resolution. Recent studies have shown that the use of GPU as a general purpose processor can greatly improve the performance of simulations. The aim of this work is to study the performance of different GPU programming interfaces for the solution of the cardiac monodomain equations. Three different GPU implementations are compared, OpenGL, NVIDIA CUDA and OpenCL, to a CPU multicore implementation that uses OpenMP. The OpenGL approach showed to be the fastest with a speedup of 446 (compared to the multicore implementation) for the solution of the nonlinear system of ordinary differential equations (ODEs) associated to the solution of the cardiac model, whereas CUDA was the fastest for the numerical solution of the parabolic partial differential equation with a speedup of 8. Although OpenCL provides code portability between different accelerators, the OpenCL version was slower for the solution of the parabolic equation and as fast as CUDA for the solution of the system of ODEs, showing to be a portable way of programming scientific applications but not as efficient as CUDA when running on Nvidia GPUs. © 2012 Springer-Verlag.

关键词： Nonlinear systems

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Analysis of Cache-Coherence Bottlenecks with Hybrid Hardware/Software Techniques

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ACM Transactions on Architecture and Code Optimization 2006年第4期3卷 390-423页

作者： Marathe, Jaydeep Mueller, Frank De Supinski, Bronis R. North Carolina State University Department of Computer Science Raleigh NC 27695-7534 United States Lawrence Livermore National Laboratory Center for Applied Scientific Computing Livermore L-557 CA 94551 United States

Application performance on high-performance shared-memory systems is often limited by sharing patterns resulting in cache-coherence bottlenecks. Current approaches to identify coherence bottlenecks incur considerable run-time overhead and do not scale. We present two novel hardwareassisted coherence-analysis techniques that reduce trace sizes by two orders of magnitude over full traces. First, hardware performance monitoring is combined with capturing stores in software to provide a lossy-trace mechanism, which is an order of magnitude faster than softwareinstrumentation- based full-tracing and retains accuracy. Second, selected long-latency loads are instrumented via binary rewriting, which provides even higher accuracy and control over tracing, but requires additional overhead. © 2006, ACM. All rights reserved.

关键词： Cache analysis Coherence protocols Dynamic binary rewriting Hardware performance monitoring Measurement Performance Program instrumentation Smps

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Explicit Platform Descriptions for Heterogeneous Many-Core Architectures

Explicit Platform Descriptions for Heterogeneous Many-Core A...

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IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Martin Sandrieser Siegfried Benkner Sabri Pllana Department of Scientific Computing Faculty of Computer Science University of Technology Vienna Austria

Heterogeneous many-core architectures offer a way to cope with energy consumption limitations of various computing systems from small mobile devices to large data-centers. However, programmers typically must consider a large diversity of architectural information to develop efficient software. In this paper we present our ongoing work towards a Platform Description Language (PDL) that enables to capture key architectural patterns of commonly used heterogeneous computing systems. PDL architecture patterns support programmers and tool chains by providing platform information in a well-defined and explicit manner. We have developed a source-to-source compiler that utilizes PDL descriptors to transform sequential task-based programs to a form that is convenient for execution on heterogeneous many-core computing systems. We show various usage scenarios of our PDL and demonstrate our approach for a commonly used scientific kernel.

关键词： Hardware computer architecture Programming Concrete Program processors Computational modeling

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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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IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

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

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.

关键词： Monitoring Runtime Systems biology Distributed databases Adaptation models XML

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