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Execution Time Optimization for Pipeline Deep Network Training on Multiple GPUs

Execution Time Optimization for Pipeline Deep Network Traini...

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Bing-Jou Wu Ding-Yong Hong Pangfeng Liu Jan-Jan Wu Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Institute of Information Science Academia Sinica Taipei Taiwan

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

As neural network models become gigantic, they increasingly demand more time and memory for training. To meet these demands, advanced parallel computing techniques have become essential. Our research focuses on hybrid parallelism, an extension of pipeline parallelism. Pipeline parallelism splits the neural network into sub-networks distributed across a sequence of processing units, enabling simultaneous processing of different data segments on each device. Hybrid parallelism extends this concept by allocating multiple devices to each sub-network. Our research focuses on optimizing hybrid parallelism by improving how the model is partitioned and how computational devices are assigned. We address these issues by modeling the neural network as a directed acyclic graph of tensor operators, and then demonstrating that optimally partitioning this graph is NP-complete. Then, we propose a two-step approach. The first step is to determine a sequence of nodes. The second step is dynamic programming, which partitions the sequence to maintain balance across the assigned devices. In transforming the graph into a sequence, we explore two methods: one employs topological sorting, while the other clusters non-sequential subgraphs. We apply both methods and select the more effective one based on performance outcomes. We implement our algorithm and conduct experiments. The results show substantial enhancements in both the speed of partitioning and training throughput, with speedups reaching up to 23 in partitioning time and a 1.3 -fold increase in training throughput.

关键词： Training Directed acyclic graph Computational modeling Neural networks Pipelines Computer architecture parallel processing Throughput Dynamic programming Topology

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An Efficient Reduced-Memory GPU-based Dynamic Programming Strategy for Bounded Knapsack Problems 7

An Efficient Reduced-Memory GPU-based Dynamic Programming St...

引用

7th International conference on parallel, distributed and Grid Computing, PDGC 2022

作者： Biswas, Girish Mukherjee, Nandini Jadavpur University Department of Computer Science and Engineering Kolkata India

ISBN: (纸本)9781665454018

Many dynamic programming approaches are existing for 1-0 Knapsack problem (KP) for fast GPU-based solution. These dynamic programming methods can be used for solving the problem of Bounded Knapsack Problem (BKP) after converting it into an equivalent 1-0 Knapsack problem. But, after conversion, large problem of BKP becomes too large 1-0 KP to fathoming via these methods on GPU-based system because of limited space availability in GPU device. In this paper, we present an implementation of an efficient dynamic programming technique to solve BKP on GPU-based system via CUDA. With an aspect to preserving the original problem size, BKP is not required to convert into equivalent 1/0 knapsack problem in this approach. Large problems are carried out with only minimally requisite CPU-GPU interactions and less memory occupancy on GPU for better efficiency and memory utilization. This GPU oriented parallel procedure shows brilliant speedup over sequential implementation of the dynamic technique. © 2022 IEEE.

关键词： Graphics processing unit

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Valluvan: processing Name Board images to Enhance Communication for Native Tamil Speakers 1

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5th International conference on Advances in distributed Computing and Machine Learning, ICADCML 2024

作者： Ramesh, Sriramachandran Prasanna Kumar, N. Murali Krishnan, D.A. Parveen Sultana, H. School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (数字)9789819718412

ISBN: (纸本)9789819718405

The Valluvan app is a language solution for native Tamil speakers. The system emphasizes the recognition of name boards, translation, and speech output to enhance communication and access to information. The app utilizes advanced technology to analyze images and text on name boards, transforming it into readable text for machines using sophisticated algorithms, the text is then translated to Tamil. The Valluvan app utilizes text-to-speech conversion methods to deliver speech output in Tamil, enhancing users’ understanding of translated content. The paper presents an overview of the app, discussing its methodology, implementation details, experimental results, and analysis. An app with potential to empower native Tamil speakers by facilitating access to and comprehension of information from diverse language sources. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： image enhancement

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DiffInfinite: Large Mask-image Synthesis via parallel Random Patch Diffusion in Histopathology 37

DiffInfinite: Large Mask-Image Synthesis via Parallel Random...

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37th conference on Neural Information processing Systems (NeurIPS)

作者： Aversa, Marco Nobis, Gabriel Haegele, Miriam Standvoss, Kai Chirica, Mihaela Murray-Smith, Roderick Alaa, Ahmed Ruff, Lukas Ivanova, Daniela Samek, Wojciech Klauschen, Frederick Sanguinetti, Bruno Oala, Luis Univ Glasgow Glasgow Lanark Scotland Dotphoton Glasgow Lanark Scotland Fraunhofer HHI Berlin Germany Aignostics Berlin Germany Ludwig Maximilians Univ Munchen Inst Pathol Munich Germany Univ Calif Berkeley Berkeley CA USA TU Berlin Berlin Germany Dotphoton Zug Switzerland

ISBN: (纸本)9781713899921

We present DiffInfinite, a hierarchical diffusion model that generates arbitrarily large histological images while preserving long-range correlation structural information. Our approach first generates synthetic segmentation masks, subsequently used as conditions for the high-fidelity generative diffusion process. The proposed sampling method can be scaled up to any desired image size while only requiring small patches for fast training. Moreover, it can be parallelized more efficiently than previous large-content generation methods while avoiding tiling artifacts. The training leverages classifier-free guidance to augment a small, sparsely annotated dataset with unlabelled data. Our method alleviates unique challenges in histopathological imaging practice: large-scale information, costly manual annotation, and protective data handling. The biological plausibility of DiffInfinite data is evaluated in a survey by ten experienced pathologists as well as a downstream classification and segmentation task. Samples from the model score strongly on anti-copying metrics which is relevant for the protection of patient data. [GRAPHICS] .

关键词： Data handling

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Towards a SOLAP Based Decision Support System for Forest Fire Management

Towards a SOLAP Based Decision Support System for Forest Fir...

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2023 International conference on Earth Observation and Geo-Spatial Information, ICEOGI 2023

作者： Belaroussi, Asma Derbal, Khalissa Benabdellah, Rachid Belhadj-Aissa, Mostefa Image Processing and Radiation Laboratory Ltir Usthb Algiers Algeria Lsi Laboratory Usthb Algiers Algeria General Directorate of Forestry Dgf Algiers Algeria

ISBN: (纸本)9798350396720

Risk, suppression, and damage assessment are essential parts of forest fire management. The involved data is distributed and heterogeneous, concerning meteorology, topomorphology, and socio-economy. In addition, it has a multiscale spatiotemporal analysis aspect. These reinforce the need to use the appropriate methods and technologies to enhance decision-making. This research investigates the potential of adopting spatial data warehousing techniques to build a SOLAP-based DSS that supports the whole decisional chain. (i) source collection and integration;(ii) data repository design and population;(iii) SOLAP analytics;and finally, (iv) data visualization in numerous formats, including tabular, graphic, and dynamic maps. © 2023 IEEE.

关键词： Data visualization

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New Approaches to Monitoring Respiratory Activity as Part of an Intelligent Model for Stress Assessment 15th

New Approaches to Monitoring Respiratory Activity as Part of...

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15th International conference on Computational Collective Intelligence (ICCCI)

作者： Ribeiro, Goncalo Postolache, Octavian Inst Univ Lisboa ISCTE Lisbon Portugal Inst Telecomunicacoes Lisbon Portugal

ISBN: (纸本)9783031414558;9783031414565

Abnormal breathing patterns have been linked to many diseases and stress-related effect. Visually counting breaths per minute is the gold standard for measuring respiratory rate. In hospital research, most nurses recognize the physiological importance of respiratory rate however its measurement it is not considered mandatory. Current research studies offer viable options for continuous monitoring of respiratory activity, although with degraded performance due to artefact. This paper proposes five new respiratory rate estimation methods considering their strengths and drawbacks to determine the most suitable one for various activities. Photoplethysmography, accelerometry, infrared temperature and pressure sensors are therefore used to monitor respiratory activity. In addition, we present a method for estimating respiratory rate via thermographic video image processing. In terms of novelty and innovation, we highlight the intelligent algorithms developed for real-time respiratory rate extraction from Photoplethysmography signals, the mechanical sensor prototype based on pressure sensors, and the facial recognition, focus zone identification, and image pixel analysis algorithms for thermographic image processing. In addition, a multichannel sensing system characterized by distributed platform computation is utilized to extract physiological parameters forming the basis for the proposed Fuzzy Logic-based model to detect and classify stress levels. To validate the suggested approaches, an experimental protocol was established to monitor the volunteers' respiratory activity in a controlled setting, as well as health monitoring throughout the induction of thermal stress and its classification, yielding excellent indications of efficiency and accuracy.

关键词： Contactless Health status Monitoring Wearable Sensors Infrared Temperature Thermography Digital Signal processing Fuzzy Logic Photoplethysmography Respiratory Rate Stress Classification

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DCT-HistoTransformer: Efficient Lightweight Vision Transformer with DCT Integration for histopathological image analysis 31

DCT-HistoTransformer: Efficient Lightweight Vision Transform...

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31st National and 9th International Iranian conference on Biomedical Engineering, ICBME 2024

作者： Cherakhloo, Mahdi Ranjbar, Mahtab Mohebbi, Mehdi Vahdat, Bijan Vosoughi Sharif University of Technology Department of Medical Engineering Electrical Engineering Department Iran Kharazmi University Department of Mathematical and Computer Sciences Iran

ISBN: (纸本)9798331529710

In recent years, the integration of advanced imaging techniques and deep learning methods has significantly advanced computer-aided diagnosis (CAD) systems for breast cancer detection and classification. Transformers, which have shown great promise in computer vision, are now being applied to medical image analysis. However, their application to histopathological images presents challenges due to the need for extensive manual annotations of whole-slide images (WSIs), as these models require large amounts of data to work effectively, which is costly and time-consuming. Furthermore, the quadratic computational cost of Vision Transformers (ViTs) is particularly prohibitive for large, high-resolution histopathological images, especially on edge devices with limited computational resources. In this study, we introduce a novel lightweight breast cancer classification approach using transformers that operates effectively without large datasets. By incorporating parallel processing pathways for Discrete Cosine Transform (DCT) Attention and MobileConv, we convert image data from the spatial domain to the frequency domain to utilize the benefits such as filtering out high frequencies in the image, which reduces computational cost. This demonstrates the potential of our approach to improve breast cancer classification in histopathological images, offering a more efficient solution with reduced reliance on extensive annotated datasets. Our proposed model achieves an accuracy of 9 6 0 0% ± 0 4 8% for binary classification and 8 7 8 5% ± 0.93% for multiclass classification, which is comparable to state-of-the-art models while significantly reducing computational costs. This demonstrates the potential of our approach to improve breast cancer classification in histopathological images, offering a more efficient solution with reduced reliance on extensive annotated datasets. © 2024 IEEE.

关键词： Frequency domain analysis

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FAST: A Scalable Subgraph Matching Framework over Large Graphs

FAST: A Scalable Subgraph Matching Framework over Large Grap...

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IEEE High Performance Extreme Computing Virtual conference (HPEC)

作者： He, Jiezhong Liu, Zhouyang Chen, Yixin Pan, Hengyue Huang, Zhen Li, Dongsheng Natl Univ Def Technol Beijing Peoples R China

ISBN: (数字)9781665497862

ISBN: (纸本)9781665497862

As one of the most fundamental operations in graph analysis, subgraph matching is widely used in various fields such as social network analysis, knowledge graph query, and fraud detection. Due to its NP-complete complexity, subgraph matching is challenging on large graphs. Previous work is limited on either scalability or the types of queries that can be handled. To address these problems, we propose a fast, scalable subgraph matching framework that consists of filtering, ordering, and enumeration stages. We exploit the parallelism in the filtering stage, and design a learning-based filtering method to remove false matching candidates;propose heuristic constraint and ordering generation methods to improve the matching efficiency;devise a distributed enumeration algorithm that is further optimized with the introduction of graph cache. Our learning-based filtering method delivers over 90% accuracy for basic queries. Compared with PruneJuice, our matching framework achieves 2-8 x speedup in triangle enumeration and up to 3-4 orders of magnitude higher throughput on generic query enumeration. The caching mechanism further boosts the performance by about 1.5x to 2.5x on average. Experiments also demonstrate the scalability of our framework.

关键词： Knowledge engineering distributed processing Filtering Social networking (online) Scalability Source coding Heuristic algorithms

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Classification Assistance Based image Inpainting for BR code

Classification Assistance Based Image Inpainting for BR code

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Information Science, parallel and distributed Systems (ISPDS), International conference on

作者： Yuting Yang Jinfeng Li Naifeng Liang Man Li Xiangyun Cai Ziyao Zheng City College of Huizhou

ISBN: (数字)9798350352719

ISBN: (纸本)9798350352726

Recently, many image inpainting methods have achieved promising performance in recovering damaged natural images in various scenes. Different from natural images, blur-readable (BR) 2D barcode images have distinct structural characteristics, such as finder patterns (represent the version information) and data bits. Version information is crucial for decoding BR code. The problem of missing data bits can be solved by image inpainting methods and error correction codes. However, when the finder patterns are damaged, they may not be correctly repaired through the inpainting technology, resulting in the failure of barcode recognition. The limitation of existing image inpainting algorithms is that the version information of BR code cannot be obtained while generating repaired result. In this paper, we innovatively introduce the classification task into the inpainting network and propose a classification assistance based inpainting model for BR code (CAII). Experiment results show that our method can efficiently enhance the readability of damaged BR code images.

关键词： Information science Codes image recognition Pattern recognition Error correction codes Decoding Classification algorithms

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A parallel Change Detection Method for Spatiotemporally Multi-Temporal SAR image Based on Q-Learning and Wavelet 14

A Parallel Change Detection Method for Spatiotemporally Mult...

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14th International conference on Digital image processing, ICDIP 2022

作者： Peng, Jinxi Su, Yuanqi Xue, Xiaorong Song, Donghong Xie, Zhengbo Xue, Xiaoyong Fan, Wenlong Department of Computer Science and Technology Xían JiaoTong University Shaanxi Xi'an710049 China Department of software Engineering South China institute of software Engineering Guangdong Guangzhou510990 China Liaonin Jinzhou121001 China The Middle Training School of Zhenan County Zhen'an Shaanxi 711500 China The Middle School of Luonan County Luonan Shaanxi 726100 China

ISBN: (纸本)9781510657564

In order to make full use of multi-source remote sensing image resources in the application of remote sensing image change detection, a parallel computing for coarse-grained computing data is a very important choice. For the large-scale explosive growth of remote sensing image change which is contained coherent speckles detection sensor data, it’s the amount of data calculation is large and time-consuming. With the development of modern remote sensing big data and Artificial Intelligence (AI), however, it is difficult for traditional methods to solve an efficient algorithm for change detection in synthetic aperture radar application. In connection with acquiring SAR (Polarimetric Synthetic Aperture Radar) image to accurately obtain an effectively computing power. Looking for data processing and task computing as the basis for parallelization, we propose a parallel change detection method for multi-temporal SAR images based on wavelet transform, and comprehensively apply the Q-learning model of parallel computing with intelligent processing. Firstly, according to the statistical features of SAR images and the semantics of Convolutional Neural Network Pixel (CNN-Pixel) analysis for an efficient change detection method based on Q-Learning semantic analysis and wavelet transform is proposed. Secondly, on the basis of Pixel-Data is achieved the accuracy of change detection, probabilistic and statistical conjugate multi-distribution function features, which is pixels and tasks are pre-processed, pixel-space data calculation of Gaussian mixture model and conjugate gradient transform task model for parallel calculation. Thirdly, the change-sequence data with obstacles at the time series model of Q-learning in the changing target object data on which the surrounding path is an obstacle to achieving that image-pixel sequence data of conjugate gradient calculation data and threshold iteratively optimizes the feature extraction of the changing area, the multi-sample training se

关键词： Change detection

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