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检索条件"任意字段=5th International Conference on Algorithms and Architectures for Parallel Processing"

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LSSM-SpMM: A Long-Row Splitting and Short-Row Merging Approach for parallel SpMM on PEZY-SC3s 1

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24th international conference on algorithms and architectures for parallel processing, ICA3PP 2024

作者： Cao, Ligang Wang, Qinglin Yang, Shun Xia, Rui Guo, Weihao Liu, Jie Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Changsha410073 China National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology Changsha410073 China

ISBN: (数字)9789819615513

ISBN: (纸本)9789819615506

Sparse Matrix-Dense Matrix Multiplication (SpMM) is a crucial kernel used in a wide range of fields including machine learning and linear algebra solvers. thus, enhancing the performance of SpMM is essential. the uneven distribution of non-zeros in sparse matrices and the tight data dependency between sparse and dense matrices make efficiently running SpMM on various hardware platforms challenging. To address these issues, optimisations are tailored according to the characteristics of the different hardware platforms. In this study, we propose a Long Row Split and Short Row Merge (LSSM) approach on the new MIMD computing platform PEZY-SC3s, utilising the standard Compressed Sparse Row (CSR) format to optimise SpMM. Specifically, LSSM divides the rows of the sparse matrix into short rows (rows with a number of non-zeros less than blockSize) and long rows (rows with a number of non-zeros greater than blockSize), applying splitting to the long rows and merging to the short rows to optimize their computations separately. Additionally, based on the hardware features of PEZY-SC3s, we employed the atomic cache for workload scheduling, SIMD instructions to accelerate computation, and Local Memory to reduce data read-write operations, addressing issues of poor data locality, workload imbalance, and vectorisation during SpMM execution. As the first study of SpMM on PEZY-SC3s, compared with BS-SpMM and RoDe-SpMM implemented on PEZY-SC3s, LSSM-SpMM offers up to 26.17× and 13.59× acceleration on the SuiteSparse and deep learning datasets, respectively, with geometric mean speedups of 1.56× and 1.71×. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning

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Skywalker: Efficient Alias-method-based Graph Sampling and Random Walk on GPUs 30

Skywalker: Efficient Alias-method-based Graph Sampling and R...

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30th international conference on parallel architectures and Compilation Techniques (PACT)

作者： Wang, Pengyu Li, Chao Wang, Jing Wang, Taolei Zhang, Lu Leng, Jingwen Chen, Quan Guo, Minyi Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai Peoples R China Shanghai Qi Zhi Inst Shanghai Peoples R China

ISBN: (纸本)9781665442787

Graph sampling and random walk operations, capturing the structural properties of graphs, are playing an important role today as we cannot directly adopt computing-intensive algorithms on large-scale graphs. Existing system frameworks for these tasks are not only spatially and temporally inefficient, but many also lead to biased results. this paper presents Skywalker, a high-throughput, quality-preserving random walk and sampling framework based on GPUs. Skywalker makes three key contributions: first, it takes the first step to realize efficient biased sampling with the alias method on a GPU. Second, it introduces well-crafted load-balancing techniques to effectively utilize the massive parallelism of GPUs. third, it accelerates alias table construction and reduce the GPU memory requirement with efficient memory management scheme. We show that Skywalker greatly outperforms the state-of-the-art CPU-based and GPU-based baselines, in a wide spectrum of workload scenarios.

关键词： Graph sampling random walk graphics processing unit

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Prediction of mechanical properties of hot-rolled steel based on multi-channel convolutional neural network 5

Prediction of mechanical properties of hot-rolled steel base...

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5th international conference on Internet of things, Automation and Artificial Intelligence, IoTAAI 2023

作者： Wang, Zhengzhong Wang, Shengdong Zhang, Kai Tang, Xingchang Wang, Yichao Jiugang Group Carbon Steel Sheet Factory Jiayuguan735100 China State Key Laboratory of Advanced Processing and Recycling of Nonferrous Metals Lanzhou University of Technology Lanzhou730050 China College of Materials Science and Engineering Lanzhou University of Technology Lanzhou730050 China College of Computer and Communication Lanzhou University of Technology Lanzhou730050 China

ISBN: (纸本)9798400716485

A multi-channel parallel convolutional neural network model is proposed to predict the mechanical properties of hot-rolled steel by chemical content and process parameters. the innovative contribution of this paper is to propose an improved Gramian Angular field method in the field of steel performance prediction, which converts the original data into a two-dimensional image matrix and introduces new features. Compared with the traditional convolutional neural network model, the multi-channel parallel convolutional neural network uses private blocks to decouple data and public blocks to integrate key data, which effectively improves the prediction accuracy of the model. Experiments in this paper show that the optimal hyperparameter structure model proposed in this paper has higher prediction accuracy than similar models proposed in other literatures. © 2023 ACM.

关键词： Convolutional neural networks

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A Review on Application of Deep Learning in Natural Language processing 5

A Review on Application of Deep Learning in Natural Language...

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5th international conference on Contemporary Computing and Informatics, IC3I 2022

作者： Kshirsagar, Pravin Ramdas Reddy, Dhoma Harshavardhan Dhingra, Mallika Dhabliya, Dharmesh Gupta, Ankur G H Raisoni College of Engineering Department of Artificial Intelligence Maharashtra Nagpur India Trainee Security Analyst JunoClinic Davman Technology Services Private Limited Maharashtra Mumbai India Manipal University Jaipur Department of Mathematics and Statistics Rajasthan Jaipur India Vishwakarma Institute of Information Technology Department of Information Technology Maharashtra Pune India Vaish College of Engineering Department of Computer Science and Engineering Haryana Rohtak India

ISBN: (纸本)9798350398267

Natural Language processing (NLP) is a developing method utilized in building different sorts of Artificial Intelligence (AI) that is available in today's time. More intellectual applications will tend to be a primary goal for ongoing and upcoming research. the requirement and desire for data-driven strategies for automatic semantic analysis have risen as a result of recent improvements in processing capacity as well as the accessibility of enormous several linguistic records. A boom in the applications throughout the previous several years of deep learning approaches has advanced the area of natural language processing. this review offers a succinct summary of deep learning architectures and techniques as well as a basic introduction to the area. Our goal is to develop a theoretical study of numerous sectors where NLP may have a significant impact and completely alter the situation with its automated approaches. Everyone is interested in investing in it since it is a hot issue. An in-depth investigation of NLP and its field is used to create these applications. Natural language processing (NLP) trends and its constituent parts are covered in the introduction to this article before it discusses applications for NLP, its emergence, and related issues. © 2022 IEEE.

关键词： Deep learning

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Towards Automatic Block Size Tuning for Image processing algorithms on CUDA

Towards Automatic Block Size Tuning for Image Processing Alg...

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17th international conference on Software Technologies (ICSOFT)

作者： Guerfi, Imene Kriaa, Lobna Saidane, Leila Azouz Univ Manouba Natl Sch Comp Sci ENSI CRISTAL Lab RAMSIS Pole Manouba Tunisia

ISBN: (纸本)9789897585883

With the growing amount of data, computational power has became highly required in all fields. To satisfy these requirements, the use of GPUs seems to be the appropriate solution. But one of their major setbacks is their varying architectures making writing efficient parallel code very challenging, due to the necessity to master the GPU's low-level design. CUDA offers more flexibility for the programmer to exploit the GPU's power with ease. However, tuning the launch parameters of its kernels such as block size remains a daunting task. this parameter requires a deep understanding of the architecture and the execution model to be well-tuned. Particularly, in the Viola-Jones algorithm, the block size is an important factor that improves the execution time, but this optimization aspect is not well explored. this paper aims to offer the first steps toward automatically tuning the block size for any input without having a deep knowledge of the hardware architecture, which ensures the automatic portability of the performance over different GPUs architectures. the main idea is to define techniques on how to get the optimum block size to achieve the best performance. We pointed out the impact of using static block size for all input sizes on the overall performance. In light of the findings, we presented two dynamic approaches to select the best block size suitable to the input size. the first one is based on an empirical search;this approach provides the optimal performance;however, it is tough for the programmer, and its deployment is time-consuming. In order to overcome this issue, we proposed a second approach, which is a model that automatically selects a block size. Experimental results show that this model can improve the execution time by up to 2.5x over the static approach.

关键词： GPU Computing parallel Programming Program Optimization Auto-tuning and Face Detection

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DRRA-based Reconfigurable Architecture for Mixed-Radix FFT

DRRA-based Reconfigurable Architecture for Mixed-Radix FFT

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international conference on VLSI Design

作者： Reeshita Kallapu Dimitrios Stathis Srinivas Boppu Ahmed Hemani School of Electrical Sciences Indian Institute of Technology Bhubaneswar (IITBBS) India Royal Institute of Technology (KTH) Sweden

Fast-Fourier Transform is an important algorithm which is used in digital signal processing and communication applications. Furthermore, mixed-radix FFT provides flexibility and increases the speed of FFT computation. For real-time processing, efficient hardware implementation using reconfigurable architectures is preferred which can offer higher performance and flexibility. In this paper, we propose an architecture for the implementation of the FFT that is derived from the Dynamically Reconfigurable Resource Array and has multiple parallel processing cells while also providing the flexibility to select the radix for each stage of the FFT. the twiddle factor generator proposed in this architecture minimizes the memory requirements and simplifies the hardware. Using the proposed architecture, various length FFTs were mapped onto either single cell or multiple cells in parallel. It is observed that the proposed architecture improves the performance by 2x times when compared to the existing FFT architectures.

关键词： Memory management Signal processing algorithms Computer architecture Transforms Switches Very large scale integration Hardware

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iPUG for Multiple Graphcore IPUs: Optimizing Performance and Scalability of parallel Breadth-First Search 28

iPUG for Multiple Graphcore IPUs: Optimizing Performance and...

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28th Annual IEEE international conference on High Performance Computing, Data, and Analytics (HiPC)

作者： Burchard, Luk Cai, Xing Langguth, Johannes Simula Res Lab Oslo Norway Univ Oslo Oslo Norway BI Norwegian Business Sch Oslo Norway

ISBN: (纸本)9781665410168

parallel graph algorithms have become one of the principal applications of high-performance computing besides numerical simulations and machine learning workloads. However, due to their highly unstructured nature, graph algorithms remain extremely challenging for most parallel systems, with large gaps between observed performance and theoretical limits. Furthermore, most mainstream architectures rely heavily on single instruction multiple data (SIMD) processing for high floating-point rates, which is not beneficial for graph processing which instead requires high memory bandwidth, low memory latency, and efficient processing of unstructured data. On the other hand, we are currently observing an explosion of new hardware architectures, many of which are adapted to specific purposes and diverge from traditional designs. A notable example is the Graphcore Intelligence processing Unit (IPU), which is developed to meet the needs of upcoming machine intelligence applications. Its design eschews the traditional cache hierarchy, relying on SRAM as its main memory instead. the result is an extremely high-bandwidth, low-latency memory al the cost of capacity. In addition, the IPU consists of a large number of independent cores, allowing for true multiple instruction multiple data (MIMD) processing. Together, these features suggest that such a processor is well suited for graph processing. We test the limits of graph processing on multiple IPUs by implementing a low-level, high-performance code for breadth-first search (RFS), following the specifications of Graph500, the most widely used benchmark for parallel graph processing. Despite the simplicity of the BFS algorithm, implementing efficient parallel codes for it has proven to be a challenging task in the past. We show that our implementation scales well on a system with 8 IPUs and attains roughly twice the performance of an equal number of NVIDIA V100 GPUs using slate-of-the-art CUDA code.

关键词： BFS Graph500 IPU Performance Optimization

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Alzheimer Disease Detection using Convolutional Neural Network and Particle Swarm Optimization

Alzheimer Disease Detection using Convolutional Neural Netwo...

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Electronic Engineering and Signal processing (EESP), international conference on

作者： Iheb Chemss El Dine Hagani Nacéra Benamrane Department of Informatics SIMPA Laboratory University of Sciences and Technology of Oran - Mohamed Boudiaf. Oran Algeria

ISBN: (数字)9798331520632

ISBN: (纸本)9798331520649

Alzheimer's disease (AD) is a major global health concern, particularly with aging populations. Early detection and diagnosis play a critical role in managing the progression of the disease. Advances in neuroimaging have enabled the use of 3D brain images, which offer valuable insights into disease biomarkers. this paper proposes a specialized Con-volutional Neural Network (CNN) models for interpreting 3D brain MRI scans in the context of Alzheimer's disease detection. By training distinct models for the axial, coronal, and sagittal orientations, we aimed to capture the unique anatomical features and variations inherent to each plane, which enhanced feature extraction and classification accuracy. Our approach contrasts with traditional unified models by focusing on optimizing the individual contributions of each orientation. Furthermore, we employed a Particle Swarm Optimization (PSO) algorithm to assign coefficients to each model, enabling a refined integration of their outputs. this approach facilitated a notable improvement in overall performance, reflecting the advantages of combining specialized CNN architectures with optimization techniques for medical image analysis.

关键词： Solid modeling three-dimensional displays Magnetic resonance imaging Biological system modeling Signal processing algorithms Brain modeling Classification algorithms Convolutional neural networks Alzheimer's disease Particle swarm optimization

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Challenges of Data Consistency in High-Concurrency Environments: algorithms and Implementation for the Electric Power Industrial Internet Platform

Challenges of Data Consistency in High-Concurrency Environme...

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Information Science, parallel and Distributed Systems (ISPDS), international conference on

作者： Zhongran Zhou Lili Wang Chunhe Song Yaowei Shen Mafeng Li Sai Liu NARI Information & Communication Technology Co.Ltd Nanjing China Shenyang Institute of Automation Chinese Academy of Sciences China School of Information Technology Liaoning University Shenyang China

ISBN: (数字)9798350352719

ISBN: (纸本)9798350352726

With the rapid development of the Electric Power Industrial Internet, challenges in high-concurrency data processing have become increasingly prominent, particularly issues related to data consistency, which directly affect the stability and reliability of the system. this study addresses the distributed data consistency problems in the Electric Power Industrial Internet platform, proposing an optimized algorithmic framework designed specifically for high concurrency and dynamic environments to enhance the response speed and data processing capabilities of the power system. this paper first analyzes common data consistency challenges in high-concurrency environments within the power system, including data synchronization delays, complexity in fault recovery, and resource allocation issues. Based on these challenges, we introduce and optimize distributed data consistency algorithms, combined with load balancing and resource optimization techniques, significantly reducing communication costs and enhancing processing efficiency. Additionally, the paper designs a robust fault tolerance mechanism to ensure data integrity and consistency in the event of node failures. through simulation experiments, we have confirmed the effectiveness of the proposed algorithms in terms of processing speed, system response time, and fault recovery, significantly optimizing the performance of the Electric Power Industrial Internet platform. this research not only enhances the robustness of the power system in facing high-concurrency data challenges but also provides important technical support and a theoretical basis for future research and applications in the Electric Power Industrial Internet.

关键词： Protocols Heuristic algorithms Distributed databases Process control Power system stability Data processing Power systems

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An Automatic System for Manipulation and Rotation of Early Stage 2-Cell Mouse Embryos 5

An Automatic System for Manipulation and Rotation of Early S...

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5th international conference on Manipulation, Automation, and Robotics at Small Scales (MARSS)

作者： Abu Zanouneh, Basil Mills, James K. Univ Toronto Dept Mech & Ind Engn Toronto ON M5S 3G8 Canada Univ Toronto Mech & Ind Engn Toronto ON M5S 3G8 Canada

ISBN: (数字)9781665459730

ISBN: (纸本)9781665459730

Micromanipulation of biological cells such as embryos during preimplantation genetic diagnosis (PGD) requires delicate handling of cells. Specifically, accurate control of the embryo orientation is essential to gain a more favourable position for zona breaching and blastomere biopsy, ensuring embryo survival. Manual embryo reorientation is achieved by aspirating and releasing the embryo arbitrarily using a vacuum-equipped micropipette until the embryo reorients itself in a favourable manner. Unfortunately, this trial and error approach heavily relies on the operator's skill, ultimately reducing biopsy success rates due to reduced precision in reorientation and ablation. In this study, an automatic image-based feedback orientation controller for early-stage embryos, using conventional IVF lab equipment, is proposed to automate the process of cell reorientation. Rotation of the embryo is achieved by rolling the embryo using a micropipette which grasps the embryo but allows slippage to permit the embryo to roll while in contact with a glass slide substrate. the substrate is mounted on a variable speed x,y stage which is controlled using one of two image-based rotation controllers investigated. the proposed control algorithms estimate the angular velocity of the embryo to determine the rotation angle, creating an error signal when differenced from the reference angle to drive a PID controller that changes the speed of the substrate. the first proposed controller uses the optical flow of the image feed to detect the cell rotation event and uses the kinematic model of the setup to determine the rotation angle. the second proposed controller uses the optical flow as a feedback signal in real-time to estimate the embryo rotation angle. Experimental results with both proposed controllers demonstrate accurate reorientation of the blastomeres, to lay the first steps towards a fully automated cell manipulation system.

关键词： Embryo Automation Robot kinematics Biopsy Process control parallel processing Real-time systems

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