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检索条件"任意字段=7th International Conference on Algorithms and Architectures for Parallel Processing"

共 2983 条记录，以下是31-40 订阅

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Proceedings of the international conference on High Performance Computing in Asia-Pacific Region, HPC Asia 2024

Proceedings of the International Conference on High Performa...

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7th international conference on High Performance Computing in Asia-Pacific Region, HPC Asia 2024

ISBN: (纸本)9798400708893

the proceedings contain 17 papers. the topics discussed include: portable implementations of work stealing;sKokkos: enabling Kokkos with transparent device selection on heterogeneous systems using OpenACC;parallelized remapping algorithms for km-scale global weather and climate simulations with icosahedral grid system;approximate block diagonalization of symmetric matrices using quantum annealing;QUBO formulation using inequalities for problems with complex constraints;low-latency communication in RISC-V clusters;flexible systolic array platform on virtual 2-D multi-FPGA plane;an efficient task-parallel pipeline programming framework;and task-based low-rank hybrid parallel Cholesky factorization for distributed memory environment.

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Automated Cuneiform Symbol Detection and Translation Using Deep Learning Techniques 7

Automated Cuneiform Symbol Detection and Translation Using D...

引用

7th international conference on Signal processing and Information Security, ICSPIS 2024

作者： Elshehaby, Shahad Al-Saad, Mina Panthakkan, Alavikunhu Al-Ahmad, Hussain University of Dubai College of Engineering and IT Dubai United Arab Emirates

ISBN: (纸本)9798350368673

this paper introduces an automated methodology for the detection and translation of cuneiform symbols using high-level deep learning techniques. A total of five deep learning models were trained on a comprehensive dataset comprising cuneiform characters, which were evaluated according to major metrics such as precision and accuracy. After a thorough evaluation, two models demonstrated superior results and were further tested on cuneiform symbols extracted from the Hammurabi law corpus. Eventually, each model identified the Akkadian representation of each cuneiform symbol and provided its English translation. To further optimize the performance, ensemble and stacking techniques will be used in future work, combining these top models by creating hybrid architectures that improve the overall accuracy at detection. In addition to model development, this paper also explores the linguistic relations shared by Akkadian, an ancient Mesopotamian language, with Arabic, hence identifying the historical linguistic influences. this research presents an interdisciplinary investigation that bridges computational linguistics and archaeology, thus showing an emerging capability of deep learning in deciphering ancient scripts, which will contribute significantly to historical and cultural studies. © 2024 IEEE.

关键词： Akkadian Translation Archaeology Automated Deep Learning Hammurabi Law Hybrid architectures Symbol Detection

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A GPGPU-Based Algorithm Acceleration System for ECG Signal processing 7

A GPGPU-Based Algorithm Acceleration System for ECG Signal P...

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7th international conference on algorithms, Computing and Artificial Intelligence, ACAI 2024

作者： Huang, Tianyu Wang, Zhijiong Hu, Guannan Pun, Sio Hang Li, Hung Chun Vai, Mang I Science and Technology Research Institute Lingyange Semiconductor Incorporated University of Macau Joint Laboratory of Zhuhai UM Department of Electrical and Computer Engineering China Science and Technology Research Institute Lingyange Semiconductor Incorporated University of Macau Joint Laboratory of Zhuhai UM Lingyange Semiconductor Incorporated Department of Electrical and Computer Engineering China Science and Technology Research Institute Lingyange Semiconductor Incorporated Joint Laboratory of Zhuhai UM Lingyange Semiconductor Incorporated Zhuhai China Institute of Microelectronics Science and Technology Research Institute Lingyange Semiconductor Incorporated University of Macau Joint Laboratory of Zhuhai UM Department of Electrical and Computer Engineering China

ISBN: (纸本)9798331529314

Electrocardiogram (ECG) is an important non-invasive technique for diagnosing cardiovascular diseases (CVD). After acquiring the patients' raw ECG signal data, signal processing is essential for the diagnosis. Conventional ECG signal processing is usually performed serially, which takes a long time to process signals with large amounts of data. In this study, we have proposed a GPGPU-based algorithm acceleration system to improve the efficiency of ECG signal processing. this system utilizes OpenCL for parallel algorithm development on GPGPU. GPGPU executes certain steps in the QRS complex detection algorithm via parallel computing, thereby accelerating the algorithmic process. Experimental results showed that the system achieved a speedup of 1.279 compared to serial computing when processing 39 ECG signal sequences in parallel. In addition, our system has been equipped with a progressive transmission mechanism and a function that intelligently determines whether an algorithm can be accelerated. these features make this system practical not only in biomedical signal processing but also in engineering and research fields with large amounts of data. © 2024 IEEE.

关键词： Noninvasive medical procedures

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SuperCut: Communication-Aware Partitioning for Near-Memory Graph processing 23

SuperCut: Communication-Aware Partitioning for Near-Memory G...

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20th ACM international conference on Computing Frontiers (CF)

作者： Zhao, Chenfeng Chamberlain, Roger D. Zhang, Xuan Washington Univ McKelvey Sch Engn St Louis MO 63110 USA

ISBN: (纸本)9798400701405

the parallel execution of many graph algorithms is frequently dominated by data communication overheads between compute nodes. this bottleneck becomes even more pronounced in Near-Memory processing (NMP) architectures with multiple memory cubes as local memory accesses are less expensive. Existing near-memory architectures typically use graph partitioning methods with a fixed vertex assignment, which limits their potential to improve performance and reduce energy consumption. Here, we argue that an NMP-based graph processing system should also consider the distribution of vertices onto memory cubes. We propose SuperCut, a framework for near-memory architectures to effectively reduce communication overheads while maintaining computational balance. We evaluate SuperCut via architectural simulation with 6 real-world datasets and 4 representative applications. the results show that it provides up to 1.8x total energy reduction and 2.6x speedup relative to current state-of-the-art approaches.

关键词： near-data processing 3D-stacked memory graph processing

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GGPA: A General Graph processing Architecture with Flexible Execution Paradigm 23

GGPA: A General Graph Processing Architecture with Flexible ...

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20th ACM international conference on Computing Frontiers (CF)

作者： Zhang, Bin Du, Haitao Chen, Song Kang, Yi Univ Sci & Technol China Hefei Anhui Peoples R China

ISBN: (纸本)9798400701405

Currently, graph data are particularly common in various fields, and graph algorithms are increasingly widely used. However, due to the features of graph datasets, such as sparsity, the acceleration of graph algorithms with traditional architectures is faced with great challenges. In terms of domain-specific architecture (DSA), accelerators are mostly designed for a specific graph algorithm because of the different features of different graph algorithms. there is still a great need for a general graph algorithmic processing architecture. In this work, we propose a parallel General Graph processing Architecture, GGPA. As a general graph computing architecture, GGPA can support multiple graph algorithms, realize parallel processing and fully explore the parallelism through a unique and effective subgraph partitioning method. GGPA implements flexibility at the execution paradigm level. During algorithm iteration, GGPA dynamically selects the execution paradigm by analyzing vertex update conditions to achieve the best performance. We verify GGPA at the CPU level and architecture simulator level, and experimental results show that GGPA achieves 1.01x to 5.86x speedup compared to other related start-of-the-art work.

关键词： graph analytics parallel processing domain-specific architecture flexible execution paradigm

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LAST: An Efficient In-place Static Binary Translator for RISC architectures 1

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23rd international conference on algorithms and architectures for parallel processing (ICA3PP)

作者： Lan, Yanzhi Hu, Qi Niu, Gen Li, Xinyu Wang, Liangpu Zhang, Fuxin Chinese Acad Sci State Key Lab Processors Inst Comp Technol Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (数字)9789819708017

ISBN: (纸本)9789819708000;9789819708017

the lack of software has been a persistent issue for emerging instruction set architecture (ISA). To overcome this challenge, binary translation has emerged as a widely adopted solution, enabling programs written for older ISA to run on new ones. In the past, dynamic binary translation (DBT) was commonly utilized for software migration, but this technique required dynamic translation and often suffered from suboptimal efficiency. In contrast, Static binary translation (SBT) is an offline technique for translating binary code without runtime translation overhead. Existing SBT systems always employ address mapping tables to handle the address relocation problem, but this approach introduces performance overhead and leads to issues with indirect jump correctness. To address these limitations, we propose a novel static in-place instruction translation method for reduced-instruction set computing (RISC) architectures. this method ensures that the address of the guest program remains unchanged after translation, leveraging the regular length of various RISC instructions. We have implemented this method in a portable SBT tool called LAST, specifically designed to run MIPS or RISCV programs on the LoongArch platform. Based on the SPEC CPU2000 benchmark results, LAST achieves over 80% performance compared to the native LoongArch program, demonstrating its effectiveness and efficiency.

关键词： Static Binary Translation In-place instruction translation instrumentation

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Deep Learning in Medical Imaging: Image processing - From Augmenting Accuracy to Enhancing Efficiency 24

Deep Learning in Medical Imaging: Image Processing - From Au...

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7th international conference on Digital Medicine and Image processing, DMIP 2024

作者： Lakshmi Vara Prasad, G. Murali, A. Bhaskar Reddy, Y.V. Marturi, Haribabu Paul, Valeru Vision Centre for Data Science AI and ML QIS College of Engineering and Technology Andhra Pradesh Ongole India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9798400709586

Deep learning has transformed medical imaging by significantly improving accuracy and efficiency in image processing tasks such as disease detection, segmentation, and classification. this paper explores the role of convolutional neural networks (CNNs), recurrent neural networks (RNNs), and other advanced architectures in enhancing image interpretation across various medical imaging modalities. By analyzing MRI, CT, and ultrasound data, this study demonstrates the efficacy of deep learning in automating critical diagnostic tasks, reducing human error, and improving patient outcomes. We propose a comprehensive framework for applying deep learning techniques in medical imaging, providing insights into future advancements in the field. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Convolutional neural networks

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Text summarization implementing abstractive and extractive methods 7

Text summarization implementing abstractive and extractive m...

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7th international conference on Electronics, Materials Engineering and Nano-Technology, IEMENTech 2023

作者： Ghosh, Debjyoti Mazumder, Abhirup Mahata, Sainik Kumar University of Engineering and Management Institute of Engineering and Management Kolkata India

ISBN: (纸本)9798350305517

How often do we come across paragraphs which contain important information but are too long to read? Most people tend to overlook humungous paragraphs at the expense of losing out on crucial information. this leads to a gap in topics which otherwise connect other significant concepts to make a meaningful learning experience - something we term as knowledge void. this report aims to highlight the importance of summarization using the two different methods of summarization - abstractive and extractive. We shall discuss the methods in detail including the methodologies, architectures and algorithms involved. this includes the preprocessing of data, the introduction of word embeddings, the application of algorithms like TextRank, building sequence-to-sequence models using LSTMs, applying encoder-decoder architecture and other advanced NLP techniques. We shall also evaluate our work using appropriate evaluation metrics. there has been experimentation using different approaches like unidirectional LSTMs, bidirectional LSTMs, a variety of tokenizers, and incorporation of attention layer to obtain the model with optimal accuracy and consistency. © 2023 IEEE.

关键词： Natural language processing systems

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A parallel PCA-SIFT Algorithm Based on Raspberry Pi 4B 7

A Parallel PCA-SIFT Algorithm Based on Raspberry Pi 4B

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7th international conference on Electronic Information Technology and Computer Engineering (EITCE)

作者： Wang, Wei Li, Wang Zhang, Zongliang Jimei Univ Coll Comp Engn Yinjiang Rd 185 Xiamen 361021 Peoples R China

ISBN: (纸本)9798400708305

Aiming at the problems of limited computing power of embedded platform, low target detection probability and high false alarm rate in the process of video small target tracking, a parallel PCA-SIFT algorithm based on Raspberry Pi 4B microprocessor is proposed. the data-level parallel method is used to realize parallel feature extraction and feature point matching, and the computing tasks are assigned to each CPU for parallel processing to improve the efficiency of the algorithm. the experimental results show that the algorithm has good adaptability to factors such as target and background clutter, geometric deformation, noise interference and illumination influence, and is close to the serial PCA-SIFT algorithm in image matching effect.

关键词： parallel image matching PCA-SIFT inter-frame correlation

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Development of GPU-based parallel CFD Solver for Unstructured Meshes 7

Development of GPU-based Parallel CFD Solver for Unstructure...

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7th international conference on Electrical, Mechanical and Computer Engineering, ICEMCE 2023

作者： Zhang, Ye Ren, Weijie Yu, Hang Li, Hua College of Aerospace Science and Engineering National University of Defense Technology Hunan Changsha410073 China

ISBN: (纸本)9798350382877

As computer technology and numerical algorithms rapidly develop, the role of computational fluid dynamics (CFD) in the fields of mechanical and aerospace engineering has become increasingly prominent. However, the outdated hardware level has hindered the application of CFD in both theory and engineering. In this article, we developed a CFD solver based on unstructured grids for compressible hypersonic flow. this solver achieved parallel computing based on GPUs using the CUDA programming model. We optimized the code to cater to the characteristics of CPU/GPU heterogeneous systems. the solver used a second-order finite volume method and an adaptive hybrid Riemann solver to accurately and robustly solve hypersonic flow problems. For grid sizes Up to tens of millions, the solver can achieve two orders of magnitude speedup. © 2023 IEEE.

关键词： Graphics processing unit

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