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检索条件"任意字段=International Conference on Parallel and Distributed Processing Techniques and Applications"

共 11317 条记录，以下是801-810 订阅

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Enhancing Storage Efficiency of HDFS for Small Files by Dynamic File Pooling

Enhancing Storage Efficiency of HDFS for Small Files by Dyna...

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international conference on parallel distributed and Grid Computing (PDGC)

作者： Raveena Aggarwal Jyoti Verma Manvi Siwach Department of Computer Engineering JC Bose University of Science &Technology YMCA Faridabad India Department of Computer Applications JC Bose University of Science & Technology YMCA Faridabad India

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

The technology is evolving fast. The data is growing faster. Apache Hadoop comes up as a capable platform to foster this rapidly progressing data. However, it is quite evident that the framework for Hadoop has been designed to manage large files. As soon as it has to face a lot of small files, the performance sinks. This further leads to metadata overheads, processing overheads, network clogging and a list of other issues. This paper proposes a novel framework for merging these small files into large files known as pools. These pools can be updated dynamically whenever subsequent datasets are fed into the system according to the empty space available. Dynamic file pooling significantly improves the storage efficiency of Hadoop distributed File System (HDFS). The results are obtained over a smaller dataset which can be scaled for over a million of files in real scenario.

关键词： File systems Merging Cluster computing Metadata Grid computing Faces

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Design and Implementation of FIR Filter Using Different Architectures: A Comparative Study

Design and Implementation of FIR Filter Using Different Arch...

引用

international conference on Signal processing and Communication (ICSC)

作者： Teena Soni Anil Kumar Manoj Kumar Panda PDPM-IIITDM Jabalpur India

ISBN: (数字)9798331511890

ISBN: (纸本)9798331511906

The article proposes a hybrid sparse particle swarm optimization algorithm (HSPSO). This paper also presents an improved low-pass FIR filter design utilizing the HSPSO algorithm. The effectiveness of the proposed algorithm is contrasted with particle swarm optimization and sparse PSO in resolving the FIR filter design problem. HSPSO offered smaller best cost and execution time values. Furthermore, the suggested approach competes with the existing approaches in the literature, providing higher stopband attenuation values and smaller passband ripple values. The proposed work implemented the designed FIR filters, employing various multiplierless techniques, including the canonical signed digit (CSD) number system, fractional CSD (FCSD), distributed arithmetic (DA), and parallel distributed arithmetic (PDA). The comparative study of all the architectures and multiplier-based structure (MULT) is performed for the FPGA and ASIC environments. The DA outperforms all the architecture in area and power. The PDA with decomposition factor 4 provides the highest throughput and also smaller area and power compared with MULT, CSD, and FCSD-based architecture.

关键词： Costs Finite impulse response filters Signal processing algorithms Throughput Attenuation Passband Particle swarm optimization Field programmable gate arrays Optimization Arithmetic

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BrahMap: A scalable and modular map-making framework for the CMB experiments

BrahMap: A scalable and modular map-making framework for the...

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Avinash Anand Giuseppe Puglisi Dipartimento di Fisica Università di Roma Tor Vergata Via della Ricerca Scientifica 1 Roma Italy INFN Sezione di Roma2 Università di Roma Tor Vergata Via della Ricerca Scientifica 1 Roma Italy Dipartimento di Fisica e Astronomia Università degli Studi di Catania Via S. Sofia 64 Catania Italy INAF Osservatorio Astrofisico di Catania Via S.Sofia 78 Catania Italy INFN Sezione di Catania Via S.Sofia 64 Catania Italy

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

The cosmic microwave background (CMB) experiments have reached an era of unprecedented precision and complexity. Aiming to detect the primordial B-mode polarization signal, these experiments will soon be equipped with $10^{4}$ to $10^{5}$ detectors. Consequently, future CMB missions will face the substantial challenge of efficiently processing vast amounts of raw data to produce the initial scientific outputs - the sky maps - within a reasonable time frame and with available computational resources. To address this, we introduce BrahMap, a new map-making framework that will be scalable across both CPU and GPU platforms. Implemented in C++ with a user-friendly Python interface for handling sparse linear systems, BrahMap employs advanced numerical analysis and high-performance computing techniques to maximize the use of super-computing infrastructure. This work features an overview of the BrahMap’s capabilities and preliminary performance scaling results, with application to a generic CMB polarization experiment.

关键词： Numerical analysis Scalability Object oriented modeling Pipelines Graphics processing units Numerical models parallel algorithms Object oriented programming Physics Python

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Diabetes Mellitus Prediction and Classification Using Firefly Optimization Based Support Vector Machine

Diabetes Mellitus Prediction and Classification Using Firefl...

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distributed Computing and Optimization techniques (ICDCOT), international conference on

作者： B Chitradevi Supriya N Subhash Chandra Chitradevi T N Haider Alabdeli Department of Computer Applications SRM Institute of Science and Technology (Deemed to be University) Trichy India Dept of ISE Nitte Meenakshi Institute of Technology Bengaluru India Department of CSE CVR College of Engineering Hyderabad Telangana India Department of computer science and engineering Bannari Amman Institute of technology Eroad India The Islamic university Najaf Iraq

The Diabetes mellitus is the most dangerous disease all over the world and it is deadly disease determined by the rapid growth levels of the glucose in a blood. The Machine Learning (ML) approaches supports in the prediction and detection of the diabetes. This research aims to propose a Firefly Optimized-based Support Vector Machine (FSVM) for the prediction and classification of diabetes. The datasets utilized for diabetic prediction are PIMA Indian Diabetic dataset. The pre-processing is done by using the data normalization, cleaning as well as data splitting techniques. The Principal Component Analysis (PCA)-based feature selection approach is used for selecting the features in a acquired dataset. Finally, the ML-based algorithms are used for the classification of the diabetes disease. The effectiveness of proposed method is evaluated by PIMA dataset with the various performance metrices such as accuracy, precision, recall, and F1-score of values about 98.45%, 98.69%, 99.37%, and 98.25% respectively when compared to previous approaches such as Naïve Bayes, Integrated kernel as well as K-Nearest Neighbour.

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SLIDEX: Sliding Window Extension for Image processing

SLIDEX: Sliding Window Extension for Image Processing

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international conference on parallel Architecture and Compilation techniques (PACT)

作者： Raúl Taranco José-María Arnau Antonio González Universitat Politècnica de Catalunya Barcelona Spain

With the rising need for efficient image processing in emerging applications such as Autonomous Driving (AD) and Augmented/Virtual Reality (AR/VR), many existing solutions do not meet their performance and energy efficiency requirements or are domain-specific and lack generality. In this work, we introduce SLIDEX, a novel ISA extension that leverages Sliding Window processing (SWP) to bridge this gap in the image processing domain. SWP is a novel SIMD model that exposes to the programmer and natively manipulates vector registers as groups of overlapped windows of pixels to exploit the sliding-window dataflow found in convolutions and other stencil operations. SWP amplifies the available Data Level parallelism (DLP) and reduces memory and register file accesses. We evaluated SLIDEX benefits in the critical image processing task of a state-of-the-art visual localization system widely used in AD and AR/VR. SLIDEX obtains an $\sim 1.2\times$ overall speedup and 22% energy reduction.

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Speeding-Up LULESH on HPX: Useful Tricks and Lessons Learned using a Many-Task-Based Approach

Speeding-Up LULESH on HPX: Useful Tricks and Lessons Learned...

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High Performance Computing, Networking, Storage and Analysis, SC-W: Workshops of the international conference for

作者： Torben Kalkhof Andreas Koch Embedded Systems and Applications Group Technical University of Darmstadt Darmstadt Germany

ISBN: (数字)9798350355543

ISBN: (纸本)9798350355550

Current programming models face challenges in dealing with modern supercomputers’ growing parallelism and heterogeneity. Emerging programming models, like the task-based programming model found in the asynchronous many-task HPX programming framework, offer new ways to express parallelism, enhance scalability, and mask synchronization and communication latency on multi-core and distributed systemsRegular high-performance computing benchmarks are often unsuitable for comparing different programming models due to their limited code complexity. However, real-world scientific applications are usually too complex. As a middle ground, proxy applications model the behavior of actual scientific problems, while reducing code complexityIn our research on using HPX to program machines with heterogeneous compute units (e.g., GPU and FPGA/AI Engines), we have also substantially optimized a pure HPX-based software baseline of the LULESH proxy application. This paper discusses the techniques we applied yielding single-node speed-ups of 1.33x to 2.25x for different problem sizes relative to the LULESH OpenMP reference implementation.

关键词： Codes Computational modeling Scalability High performance computing Source coding Programming parallel processing Software Synchronization Kernel

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Weather Forecasting Using Advanced Artificial Intelligence techniques Based on Swin Transformer Network

Weather Forecasting Using Advanced Artificial Intelligence T...

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distributed Computing and Optimization techniques (ICDCOT), international conference on

作者： Kavitha S Nagamani H S Sumanth S Piyush Kumar Pareek P. Uma Mageswari Department of MCA Nitte Meenakshi Institute of Technology Bangalore India Department of Computer Science Govt. Smt VHD Central Institute of Home Science Bengaluru India Department of Computer Science and Applictaions Government College for Women Kolar India Department of AI/ML Nitte Meenakshi Institute of Technology Bangalore India Department of Computer Applications Presidency College India

It is still difficult for weather prediction (NWP) representations to exactly anticipate rainfall, especially the highest rainfall events. The severely intermittent character rainfall, together with subgrid-scale are critical to the multi-scale dynamics that produce rainfall, might be the reasons for this. Because of this inherent fallibility of weather prediction model projections, it is standard practice to fix these forecasts by post-processing the model's output. To improve weather prediction, the study suggests using a Spectral Swin Transformer Network (SSTN). An operating high-resolution model system, the PRUFS model, which runs for East China at 3 km grid intermissions, was used to test the model. The 311 conventional ground-based weather stations to collect data on 2 m temperature, 2 m relative speed. A year's worth of historical PRUFS perfect outputs and surface comments make up the training dataset for SSTN, while two real-time SSTN outputs form the basis of the act evaluation.

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TAIWANESE ACROSS TAIWAN CORPUS AND ITS applications 25

TAIWANESE ACROSS TAIWAN CORPUS AND ITS APPLICATIONS

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25th conference of the Oriental-COCOSDA-international-Committee-for-the-Co-Ordination-and-Standardisation-of-Speech-Databases-and-Assessment-techniques (O-COCOSDA)

作者： Liao, Yuan-Fu Tsay, Jane S. Kang, Peter Khoo, Hui-Lu Tan, Le-Kun Chang, Li-Chen Iunn, Un-Gian Su, Huang-Lan Thiann, Tsun-Guan Tiun, Hak-Khiam Liao, Su-Lian Natl Yang Ming Chiao Tung Univ Hsinchu Taiwan Natl Chung Cheng Univ Minxiong Taiwan Natl Taiwan Normal Univ New Taipei Taiwan Natl Cheng Kung Univ Tainan Taiwan Natl Taipei Univ Educ Expt Elementary Sch New Taipei Taiwan Natl Taichung Univ Educ Taichung Taiwan Natl Taitung Univ Taitung Taiwan Taiwan Railway Adm Taipei Taiwan

ISBN: (数字)9798350398564

ISBN: (纸本)9798350398564

Taiwanese across Taiwan (TAT) corpus is the first large-scale and publicly released Taiwanese speech corpus which represents the modern Taiwanese around Taiwan. This paper briefly reviews the TAT corpus and a corresponding parallel Chinese, Han-Lo-Tai-bun, Tai-Luo and Pe.h-oe-ji lexicon and demonstrate some of their potential applications including ASR, TTS and voice conversion. The corresponding pretrained ASR and TTS models, sample model usage codes and training scripts will also be released. More information could be found on the Formosa Speech in the Wild website: https://***/***/fsw.

关键词： Taiwanese speech corpus automatic speech recognition speech synthesis voice conversion

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Power and area-efficient digital filters for communication applications

Power and area-efficient digital filters for communication a...

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Advanced Computing and Emerging Technologies (ACET), international conference on

作者： Pradnya Zode Pravin Zode Shweta Dhondse Kapil K. Jajulwar Deptt. of Electronics Engg. Y.C.College of Engineering Nagpur India Deptt. of Electronics and Telecommunication Engg. G H RAISONI College of Engineering Nagpur India

ISBN: (数字)9798350367720

ISBN: (纸本)9798350367737

The paper introduces an innovative time-dependent finite impulse response (FIR) filter designed for utilization in time-mode signal processing systems like mobile phones and digital transmitters-receivers. The filter’s functioning structure is embedded in the principles of high-level transformation techniques like parallel processing and folding. In this paper 6-tap FIR filter is designed and implemented using parallel processing and folding techniques for low power consumption and area minimization. The proposed FIR filter is modeled in Xilinx using Verilog hardware descriptive language and synthesized with the XST tool. The target implementation platform is the Xilinx ML605 embedded development kit to investigate the LUTs and power consumption of the design. The proposed filter reduces the major concern parameters of a design as power and area. The implementation results discover that the area is reduced to 90.83% of the existing method and power is reduced to 67.25% of the existing method. The findings represent a significant step ahead in hardware efficiency optimization.

关键词： Power demand Finite impulse response filters Computer architecture parallel processing Very large scale integration Hardware Registers Delays Hardware design languages Optimization

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parallel Computing with GPU: An accelerator for Data-Centric High Performance Computing

Parallel Computing with GPU: An accelerator for Data-Centric...

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Innovative Engineering Sciences and Technological Research (ICIESTR), international conference on

作者： Mohammed Siddique Mohammed Waleed Ashour Faculty of Information Technology Majan University College Muscat Oman

ISBN: (数字)9798350348637

ISBN: (纸本)9798350348644

In recent years, the demand for high-performance computing in various disciplines of ICTs has affected the dependency on single core processes due to its decline in efficiency. Whereas, this requirement of advance in processing power has seen the emergence of Graphical processing Units (GPU). In this paper, the role of GPUs in parallel computing along with its advantages, limitations and performance in various domains have been presented. The massive parallelism feature of GPUs can break down complex problems into smaller bits of computation with greater efficiency. The ability to compute complex distributed processes is ideal for implementing Machine Learning (ML) and Deep Learning (DL) algorithms. The evaluation of GPUs performance in medical physics, large scale data mining, CUDA workload analysis and applications of ML and DL has been deliberated on the high-performance computing. As GPUs continue to evolve, processing performance and efficiency should improve, and stand out as a cost-effective and powerful solution for optimizing faster and reliable large-scale data processing. Future research is expected to optimize GPU architectures for developing computing paradigms to keep them relevant in high-performance computing's fast-changing industry.

关键词： Training Performance evaluation Tensors High performance computing Computational modeling Graphics processing units parallel processing Programming Reliability Physics

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