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10th E-Health and Bioengineering Conference, EHB 2022

作者： Ivanescu, Renato Constantin Nagy, Rodica Ofiteru, Anca Istrate Comanescu, Cristina Manda, Alexandru Department of Computers and Information Technology University of Craiova Craiova Romania University of Medicine and Pharmacy of Craiova University of Craiova Craiova Romania

ISBN: (纸本)9781665485579

The medical routine of the future is strongly influenced by medical information technology. The quality and efficiency of medicine are at higher standards due to the image-based methods and the increase in computation power. parallel and distributed computing are attractive alternatives to improve processing performance in many areas of study and this paper investigates such solutions for increasing the throughput on large datasets in medical imaging. In this research, different edge detection techniques are used for image processing, as it is the most common and challenging task in the medical domain. The results are relevant and present the effectiveness of the two processing approaches compared to a single CPU for the acceleration of medical image processing. The CUDA processing platform using a hybrid streams-based programming model is the parallel approach. The distributed system is a Linux-based cluster, which uses message passing for communication and efficient load balancing between nodes for high performance. This paper highlights parallel computing as a very good solution for processing large image datasets in the medical domain. Good results were also obtained for the distributed system. © 2022 IEEE.

关键词： Computer operating systems

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An image generator based on neural networks in GPU

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第25期81卷 36353-36374页

作者： Silva, Thiago W. Reis, Halamo Melcher, Elmar U. K. Lima, Antonio M. N. Brito, Alisson, V Fed Univ Campina Grande UFCG Campina Grande Paraiba Brazil Fed Univ Ceara UFC Quixada Brazil Fed Univ Paraiba UFPB Joao Pessoa Paraiba Brazil

Existing image databases contain a few diversity of images. Likewise, there is no specific image base available in other situations, leading to the need to undertake additional efforts in capturing images and creating datasets. Many of these datasets contain only a single object in each image, but often the scenario in which projects must operate in production requires several objects per image. Thus, it is necessary to expand original datasets into more complex ones with specific combinations to achieve the goal of the application. This work proposes a technique for image generation to extend an initial dataset. It has been designed generically to work with various images and create a data set from some initial images. The generated set of images is used in a distributed environment. It is possible to perform image generation in this environment, producing datasets with specific images to work in certain applications. The generation of images consists of two methods: generation by deformation and generation by a neural network. With the proposed methods, this work sought to bring as main contributions the specification and implementation of an image generating component so that it is possible to easily integrate it with possible heterogeneous devices capable of parallel computing, such as General Purpose Graphics processing Unit (GPGPU). In comparison with the existing methods to the proposed one, this one proposes to use the image generator enlarging an initial image bank with the combination of two methods. Some experiments are presented doing generation with handwritten digits to validate the proposed approach. The generator was designed with CUDA and GPU-optimized libraries as TensorFlow-specific modules. The results obtained can optimize the integration process with the simulation of possible stimuli choices, avoiding problems in the generation of image phase tests.

关键词： image generation GPU Neural network Generation by deformation Functional verification

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Efficient Allocation of image Recognition and LLM Tasks on Multi-GPU System 15th

Efficient Allocation of Image Recognition and LLM Tasks on...

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15th International Conference on parallel processing and Applied Mathematics, PPAM 2024

作者： Lawenda, Marcin Samborski, Krzesimir Khloponin, Kyrylo Szustak, Lukasz Poznan Supercomputing and Networking Center Jana Pawla II 10 Poznań61-139 Poland Czestochowa University of Technology Dąbrowskiego 69 Częstochowa42-201 Poland

ISBN: (纸本)9783031857027

This work is concerned with the evaluation of the performance of parallelization of learning and tuning processes for image classification and large language models. For machine learning model in image recognition, various parallelization methods are developed based on different hardware and software scenarios: simple data parallelism, distributed data parallelism, and distributed processing. A detailed description of presented strategies is given, highlighting the challenges and benefits of their application. Furthermore, the impact of different dataset types on the tuning process of large language models is investigated. Experiments show to what extent the task type affects the iteration time in a multi-GPU environment, offering valuable insights into the optimal data utilization strategies to improve model performance. Furthermore, this study leverages the built-in parallelization mechanisms of PyTorch that can facilitate these tasks. Furthermore, performance profiling is incorporated into the study to thoroughly evaluate the impact of memory and communication operations during the training/tuning procedure. Test scenarios are developed and tested with numerous benchmarks on the NVIDIA H100 architecture showing efficiency through selected metrics. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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Multimodal Interaction Fusion Network for Cloth-Changing Person Re-Identification 22

Multimodal Interaction Fusion Network for Cloth-Changing Per...

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22nd IEEE International Symposium on parallel and distributed processing with Applications, ISPA 2024

作者： Yuan, Caihong Xu, Yuanchen Guan, Zhijie Chen, Xiaopan Zhu, Xiaoke Liang, Wenjuan Henan University School of Computer and Information Engineering Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan Province Spatial Information Processing Engineering Technology Research Center Kaifeng China

ISBN: (纸本)9798331509712

Cloth-Changing Person Re-Identification (CC-ReID) refers to the technology that can identify the same individual in surveillance scenarios, even after changing their clothes. The core of CC-ReID is to exact cloth-irrelevant identity features. Existing methods usually extract them from body contours, skeletons, human faces, etc. However, information extracted from a single modality is often insufficient. Therefore, we propose a novel Multimodal Interaction Fusion (MIF) framework that could facilitate the deep integration of multimodal information, thereby enhancing the generalization ability of CC-ReID. Specifically, we design a triple-interaction collaborative module that strategically integrates features captured from body posture, original RGB images, and clothes-erased images, maximizing the advantages of each modality and overcoming their limitations. Additionally, we develop an Adapt-Blend Pool (ABP) module that effectively improves pooling methods to enhance the model's ability to process images with complex backgrounds and varied features. Following training, our framework effectively harnesses a variety of modal inputs to capture identity representations independent of clothing accurately. Extensive experimental results on PRCC, LTCC, and VC-Clothes datasets demonstrate the effectiveness of the proposed method. © 2024 IEEE.

关键词： image enhancement

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Deep Neural Network Training With distributed K-FAC

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IEEE TRANSACTIONS ON parallel AND distributed SYSTEMS 2022年第12期33卷 3616-3627页

作者： Pauloski, J. Gregory Huang, Lei Xu, Weijia Chard, Kyle Foster, Ian T. Zhang, Zhao Univ Chicago Dept Comp Sci Chicago IL 60637 USA Texas Adv Comp Ctr Austin TX 78758 USA

Scaling deep neural network training to more processors and larger batch sizes is key to reducing end-to-end training time;yet, maintaining comparable convergence and hardware utilization at larger scales is challenging. Increases in training scales have enabled natural gradient optimization methods as a reasonable alternative to stochastic gradient descent and variants thereof. Kronecker-factored Approximate Curvature (K-FAC), a natural gradient method, preconditions gradients with an efficient approximation of the Fisher Information Matrix to improve per-iteration progress when optimizing an objective function. Here we propose a scalable K-FAC algorithm and investigate K-FAC's applicability in large-scale deep neural network training. Specifically, we explore layer-wise distribution strategies, inverse-free second-order gradient evaluation, and dynamic K-FAC update decoupling, with the goal of preserving convergence while minimizing training time. We evaluate the convergence and scaling properties of our K-FAC gradient preconditioner, for image classification, object detection, and language modeling applications. In all applications, our implementation converges to baseline performance targets in 9-25% less time than the standard first-order optimizers on GPU clusters across a variety of scales.

关键词： Training parallel processing Program processors Convergence Computational modeling Data models Deep learning Optimization methods neural networks scalability high-performance computing

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Speed Up Federated Learning in Heterogeneous Environments: A Dynamic Tiering Approach

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IEEE INTERNET OF THINGS JOURNAL 2025年第5期12卷 5026-5035页

作者： Mahmoud Sajjadi Mohammadabadi, Seyed Zawad, Syed Yan, Feng Yang, Lei Univ Nevada Reno Dept Comp Sci & Engn Reno NV 89557 USA IBM Res Almaden Res Dept San Jose CA 95120 USA Univ Houston Comp Sci Dept Houston TX 77004 USA Univ Houston Elect & Comp Engn Dept Houston TX 77004 USA

Federated learning (FL) enables collaborative training of a model while keeping the training data decentralized and private. However, in Internet of Things systems, inherent heterogeneity in processing power, communication bandwidth, and task size can significantly hinder the efficient training of large models. Such heterogeneity would render vast variations in the training time of clients, lengthening overall training and wasting resources of faster clients. To tackle these heterogeneity challenges, we propose dynamic tiering-based FL (DTFL), a novel system that leverages distributed optimization principles to improve the edge learning performance. Based on clients' resources, DTFL dynamically offloads part of the global model to the server, alleviating resource constraints on slower clients and speeding up training. By leveraging split learning, DTFL offloads different portions of the global model to clients in different tiers and enables each client to update the models in parallel via local-loss-based training. This helps reduce the computation and communication demand on resource-constrained devices, mitigating the straggler problem. DTFL introduces a dynamic tier scheduler that uses tier profiling to estimate the expected training time of each client based on their historical training time, communication speed, and dataset size. The dynamic tier scheduler assigns clients to suitable tiers to minimize the overall training time in each round. We theoretically prove the convergence properties of DTFL and validate its effectiveness by training large models (ResNet-56 and ResNet-110) across varying numbers of clients (from 10 to 200) using the popular image datasets (CIFAR-10, CIFAR-100, CINIC-10, and HAM10000) under both I.I.D and non-I.I.D systems. DTFL seamlessly integrates various privacy measures without sacrificing performance. Extensive experimental results show that compared with state-of-the-art FL methods, DTFL can significantly reduce the training time b

关键词： Training Computational modeling Internet of Things Servers Federated learning Accuracy Dynamic scheduling Performance evaluation Optimization Load modeling distributed optimization edge computing federated learning (FL) heterogeneous devices split learning (SL)

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Review of parallel processing methods for Big image Data Applications

Review of Parallel Processing Methods for Big Image Data App...

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International Conference on Communication, Computing and Electronics Systems, ICCCES 2019

作者： Vigneshwari, K. Kalaiselvi, K. Department of Computer Science VELS Institute of Science Technology and Advanced Studies Chennai600117 India Formerly Vels University Chennai600117 India

ISBN: (纸本)9789811526114

The coexistence of technologies, like big data application, cloud computing, and the numerous images in the Web has paved the need for new image processing algorithms that exploit the processed image for diverse applications. There arises a need for new image processing algorithms to utilize the processed image for diverse applications though many techniques with variations exist. Ultimately, the enduring issue is to enhance the effectiveness of huge image processing and to maintain the combination of the same with recent works. This paper presents a review of the newest progress in researches on parallel processing methods for the processing of big data. Initially, the reviews about the parallel processing methods were carried out by highlighting some promising parallel processing methods in recent studies, such as the representation of MapReduce (MR) framework, distributed, parallel methods, and Hadoop framework. Subsequently, focus on analysis and deliberations about the challenges and promising solutions of parallel computing methods on big data in various applications and on image processing applications were made and concluded with a summary of number of open problems and research areas. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Big data

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Generative Enhancement-based Similarity Prediction Hashing for image Retrieval 29

Generative Enhancement-based Similarity Prediction Hashing f...

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29th IEEE International Conference on parallel and distributed Systems, ICPADS 2023

作者： Cao, Yuan Meng, Fanlei Wu, Xiangyu Wang, Zijie Ocean University of China School of Computer Science and Technology Qingdao China

ISBN: (纸本)9798350330717

Hashing is frequently used in approximate nearest neighbor search due to its storage and search efficiency. On account of the bottleneck of traditional learning to hash methods, deep-based learning to hash has gained quite a popularity among researchers recently. While such methods show a promising performance gain by utilizing deep neural networks into its end-to-end training process to generate compact binary codes, the intrinsic connections between components make it unfeasible to optimize the architecture significantly. Subject to noise interference and absence of similarity labels of training data, normal unsupervised deep models even carry a noticeable deviation at representation learning stage. By integrating Generative Adversarial Network, this paper presents a novel architecture for generating compact hash codes from an extended set derived from original images while using the benefit of similarity prediction. Extensive experiments conducted on three benchmarks, NUS-WIDE, CIFAR-10, and MS-COCO illustrate that our method outperforms state-of-the-art image retrieval models and generates high-quality binary hash codes seamlessly. © 2023 IEEE.

关键词： generative model hashing image retrieval unsupervised learning

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Satellite image Classification using Convolutional Neural Network with Huber Loss Function for Land Cover Mapping in Forest Ecosystem

Satellite Image Classification using Convolutional Neural Ne...

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2024 International Conference on distributed Systems, Computer Networks and Cybersecurity, ICDSCNC 2024

作者： Al-Fatlawy, Ramy Riad Raviprakash, P. Vybhavi, Gummagatta Yajaman Jagadish, Sripelli Sundaram, S Meenakshi College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Hyderabad India Gokaraju Rangaraju Institute of Engineering and Technology Bachupally Department of Cse Hyderabad India School of Computer Science & Artificial Intelligence Sr University Warangal India Nitte Meenakshi Institute of Technology Department of Cse Bengaluru India

ISBN: (纸本)9798350375442

Satellite image classification for land cover involves determining high-resolution imagery to recognize and classify various types of land covers. This process assists in monitoring forest health, managing resources, and assessing biodiversity effectively. However, accurately classifying images for land cover is difficult because of diverse and complex nature of forest environments and varying image qualities. In this research, the Convolutional Neural Network with Huber Loss Function (CNN-HLF) is proposed to classify the satellite images accurately for landcover mapping in forest ecosystem. The CNN-HLF effectively manage the variability and complexity of forest environments which improves the performance of land cover. Initially, the Eurosat dataset is used to determine the CNN-HLF performance. Resizing and image sharpening is applied to adjust the size and enhance the clarity of features. DenseNet201 is employed for extracting features effectively from pre-processed images. The proposed CNN-HLF achieves a high accuracy of 99.87% using Eurosat dataset compared to existing methods like GoogleNet with pre-processing and Lightweight parallel CNN (LPCNN). © 2024 IEEE.

关键词： Convolutional neural networks

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Slim-MY: A Lightweight Network for Power Line Defect Detection with FPGA Acceleration 29

Slim-MY: A Lightweight Network for Power Line Defect Detecti...

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29th IEEE International Conference on parallel and distributed Systems, ICPADS 2023

作者： Zhang, Xiaoyang Chen, Wenjie Zhou, Zihao Duan, Zhuowen East China Normal University MoE Engineering Research Center for Software/Hardware Co-Design Technology and Application Shanghai China

ISBN: (纸本)9798350330717

The power line is a crucial component of the power system. However, long-term exposure to the natural environment can lead to various defects, such as burrs, corrosion, and corona. These defects seriously threaten the safe operation of the power system. Traditional manual inspections and image processing methods are time-consuming, labor-intensive, and have lower accuracy. In light of these, we propose a lightweight network called Slim-MY. It utilizes lightweight feature extraction and fusion modules, significantly reducing computational and parameter overhead while ensuring defect detection accuracy. Furthermore, to accelerate the post-processing process, we propose the FP-NMS algorithm. It parallelizes the sorting and overlapping box removal parts of the NMS algorithm, making it more suitable for deployment on devices that support parallel computing, such as FPGA. By testing on our dataset of power line defects, the Slim-MY network achieves a detection accuracy and computation of 92.7% and 1.1 GFLOPs respectively, which outperforms most other networks. By accelerating on the KV260 FPGA, the Slim-MY network achieves an inference speed of 94 fps, and the FP-NMS algorithm's execution speed is 14.8 times that of the GTX 1080Ti. It indicates the feasibility and efficiency of the method we propose in power line defect detection tasks. © 2023 IEEE.

关键词： Deep Learning Defect Detection FPGA parallel Computing

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