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Accelerating Massively distributed Deep Learning Through Efficient Pseudo-Synchronous Update Method

INTERNATIONAL JOURNAL OF parallel PROGRAMMING 2024年第3期52卷 125-146页

作者： Wen, Yingpeng Qiu, Zhilin Zhang, Dongyu Huang, Dan Xiao, Nong Lin, Liang Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou Guangdong Peoples R China

In recent years, deep learning models have been successfully applied to large-scale data analysis, including image classification, video caption, natural language processing, etc. Large-scale data analyses take advantage of parallel computing to accelerate the speed of model training, in which data parallelism has become the dominant method for deep learning model training due to its high throughput rate. Synchronous stochastic gradient descent optimization becomes a well-recognized optimization method to ensure model convergence, but the overhead of gradients synchronization increases linearly as the number of workers increases, causing a huge waste of time. Although some efficiency-first asynchronous methods have been proposed, these methods cannot guarantee their convergence in large-scale distributed training. To solve this problem, we propose an efficient pseudo-synchronous approach that updates the network with the previous gradient, performing the synchronization of a new gradient to overlap computation and synchronization. This idea will obviously affect the normal convergence of the model, so we propose a novel adaptive exponential smoothing predicted gradient algorithm for model optimization, which can adaptively adjust the confidence coefficient of the history gradient to ensure the normal convergence of the training process. Experiments prove that our method can speed up the training process and achieve a comparable accuracy rate with standard synchronous SGD. Besides, our method has more efficient weak scalability compared to the traditional synchronous SGD and those in previous related work. We apply our methods to image recognition and video caption applications at most 12288 cores with strong scalability on Tianhe II. Evaluations show that, when configured appropriately, our method attains near-linear scalability using 128 nodes. We get 93.4% weak scaling efficiency on 64 nodes, 90.5% on 128 nodes.

关键词： Deep learning (DL) Stochastic gradient descent (SGD) Optimization distributed computing Tianhe-2 supercomputer Data parallel

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Self-supervised Adversarial Hashing for Large-scale image Retrieval 30

Self-supervised Adversarial Hashing for Large-scale Image Re...

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30th IEEE International Conference on parallel and distributed Systems, ICPADS 2024

作者： Cao, Yuan Xu, Xue Liu, Junwei Chen, Xiangru Ocean University of China School of Computer Science and Technology Qingdao China

ISBN: (纸本)9798331515966

Hashing has been widely used in large-scale image retrieval due to its high storage and search efficiency. Unsupervised learning saves a significant amount of labor costs compared to supervised learning. Existing unsupervised methods mostly convert unsupervised problems into supervised problems by reconstructing semantic information. In this paper, we propose a novel Self-supervised Adversarial Hashing (SAH) method which utilizes unsupervised semantic reconstruction methods along with self-supervised generative adversarial and contrastive learning methods to improve the model's accuracy and robustness. In addition, we propose a novel method for semantic relationship reconstruction, taking into account the similarity between different categories. Based on this, we have designed a multi-joint loss to further achieve reasonable intra-class aggregation and inter-class differentiation. The experimental results show that the proposed method outperforms state-of-the-art hashing methods for large-scale image retrieval. © 2024 IEEE.

关键词： Self-supervised learning

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Inverse Quantum Fourier Transform Inspired Algorithm for Unsupervised image Segmentation

Inverse Quantum Fourier Transform Inspired Algorithm for Uns...

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37th IEEE International parallel and distributed processing Symposium (IPDPS)

作者： Akinola, Taoreed Li, Xiangfang Wilkins, Richard Obiomon, Pamela Qian, Lijun Prairie View A&M Univ Dept Elect & Comp Engn Prairie View TX 77446 USA

ISBN: (纸本)9798350311990

image segmentation is a very popular and important task in computer vision. In this paper, inverse quantum Fourier transform (IQFT) for image segmentation has been explored and a novel IQFT-inspired algorithm is proposed and implemented by leveraging the underlying mathematical structure of the IQFT. Specifically, the proposed method takes advantage of the phase information of the pixels in the image by encoding the pixels' intensity into qubit relative phases and applying IQFT to classify the pixels into different segments automatically and efficiently. To the best of our knowledge, this is the first attempt of using IQFT for unsupervised image segmentation. The proposed method has low computational cost comparing to the deep learning based methods and more importantly it does not require training, thus make it suitable for real-time applications. The performance of the proposed method is compared with K-means and Otsuthresholding. The proposed method outperforms both of them on the PASCAL VOC 2012 segmentation benchmark and the xVIEW2 challenge dataset by as much as 50% in terms of mean Intersection-Over-Union (mIOU).

关键词： Inverse Quantum Fourier Transform Computer Vision image Segmentation

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ER-SFM: Efficient and Robust Cluster-Based Structure from Motion 7th

ER-SFM: Efficient and Robust Cluster-Based Structure from Mo...

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7th Chinese Conference on Pattern Recognition and Computer Vision

作者： Ye, Zongxin Li, Wenyu Liu, Sidun Qiao, Peng Dou, Yong Natl Univ Def Technol Coll Comp Natl Key Lab Parallel & Distributed Comp Changsha Peoples R China

ISBN: (纸本)9789819785070;9789819785087

Structure from Motion (SfM) is a fundamental computer vision technique that recovers scene structure and camera motion from multi-view images. When facing large-scale scenarios, cluster-based methods are commonly employed to improve reconstruction efficiency. However, these methods currently face challenges regarding their limited robustness, redundant computation, and drift. To address these issues, we propose a unified pipeline called ER-SfM, which enhances the three key aspects of cluster-based SfM: image clustering, local reconstruction, and merging. In terms of image clustering, we propose a three-stage image clustering method to ensure adequate and reliable connections between clusters. In the local reconstruction stage, we expedite the reconstruction process by eliminating duplicate point cloud computation. In the final merging stage, we introduce a global merging algorithm without scale ambiguity to address the drift problem. Extensive experimental results demonstrate the superior performance of our method in terms of both robustness and efficiency compared to state-of-the-art methods.

关键词： parallel structure from motion 3D Reconstruction image clustering Global averaging

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A large-scale lychee image parallel classification algorithm based on spark and deep learning

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2025年 230卷

作者： Xiao, Yiming Wang, Jianhua Xiong, Hongyi Xiao, Fangjun Huang, Renhuan Hong, Licong Wu, Bofei Zhou, Jinfeng Long, Yongbin Lan, Yubin South China Agr Univ Coll Elect Engn Coll Artificial Intelligence Guangzhou Peoples R China South China Agr Univ Guangdong Lab Lingnan Modern Agr Guangzhou Peoples R China South China Agr Univ Natl Ctr Int collaborat Res precis Agr Aviat Pesti Guangzhou Peoples R China

Accurate and rapid classification of large-scale lychee images is crucial for collecting germplasm resources and studying the characteristics of different lychee varieties, and it requires the construction of accurate classification models and the design of rapid classification algorithms. However, the current deep learning-based classification methods for lychee images are unable to simultaneously meet the processing requirements of accuracy and timeliness in large-scale lychee image classification. To address the problem above, this paper proposes a largescale parallel classification algorithm for lychee images based on Spark and deep learning. Specifically, first, the T_ECBAM_ResNetS-34 model architecture was designed and trained using a self-built dataset covering ten types of lychee images and the PyTorch deep learning framework, which improved the accuracy of model classification;Second, the model inference algorithm trained by PyTorch was restructured, utilizing Apache Spark RDD and broadcast variables and data structures to implement data partitioning and model parallel computation across nodes. The experimental results show that the method proposed in this paper surpasses existing technologies in both classification accuracy and the speed of large-scale lychee image classification.

关键词： Litchi Classification Apache Spark ResNet-34 distributed Model Inference

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QGIP: A Framework Bridging Quantum Grayscale image processing and Applications 22

QGIP: A Framework Bridging Quantum Grayscale Image Processin...

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

作者： Che, Xilong Zhang, Jiale Chen, Shuo Peng, Shun Hu, Juncheng Jilin University College of Computer Science and Technology Changchun China

ISBN: (纸本)9798331509712

Quantum computing offers parallel processing capabilities and resource-saving advantages, particularly useful for managing expansive datasets and complex image processing tasks. Grayscale images, being the simplest single-channel image mode, are frequently employed in artificial intelligence training. Before actual image applications, various image processing operations are typically required. However, the restoration of a grayscale image of dimensions 2n × 2n after a series of linear transformations poses a challenge. Existing methods typically involve finding the inverse of the most recent linear transformation or re-encoding the image followed by repeated operations until the final transformation, resulting in excessive computational overhead and disconnection from subsequent quantum grayscale image applications. To address this issue, we propose a universal quantum linear restoration algorithm for grayscale image, denoted as QLR, which effectively bridges the stages of linear transformation and subsequent image applications. QLR reduces the time complexity from O(2n) to O(n) compared to classical counterpart. Building upon the QLR algorithm, we further propose two quantum resource-optimized compression methods for optional lossless image storage. Combining with other quantum algorithms and techniques, we design a framework (QGIP) aimed at bridging the processes of quantum grayscale image processing and applications. Experiments simulated on the IBM Quantum platform validate the correctness and efficiency of our proposal. © 2024 IEEE.

关键词： image reconstruction

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parallel COREGISTRATION ALGORITHM FOR SAR imageS BASED ON HADOOP

PARALLEL COREGISTRATION ALGORITHM FOR SAR IMAGES BASED ON HA...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Li, Jiawei Zeng, Guobing Xu, Huaping Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China

ISBN: (纸本)9798350320107

As the availability of SAR images continues to grow, efficient coregistration of massive SAR images presents a greater challenge. Traditional serial coregistration methods impose an unbearable time overhead. To reduce this overhead and make full use of computing resources, a parallel coregistration strategy based on Hadoop is proposed for SAR images. The Hadoop distributed File System (HDFS) is used to store SAR image data in chunks, and Hadoop's distributed computing strategy MapReduce is used to realize distributed parallel processing of SAR images. Two distributed parallel coregistration methods are presented with the proposed parallel strategy: one based on the maximum correlation method and the other on the DEM-assisted coregistration method. These methods are evaluated through coregistration experiments on the same dataset, and they are verified by comparing the coregistration results and processing time.

关键词： SAR Hadoop parallel Coregistration

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image denoising based on Swin Transformer Residual Conv U-Net 27

Image denoising based on Swin Transformer Residual Conv U-Ne...

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27th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and parallel/distributed Computing (SNPD)

作者： Gan, Yong Zhou, Shaohui Chen, Haonan Wang, Yuefeng Zhengzhou Univ Technol Zhengzhou Peoples R China Zhengzhou Univ Light Ind Coll Comp Sci & Technol Zhengzhou Peoples R China

ISBN: (纸本)9798350391961;9798350391954

In the field of computer vision, image denoising remains a fundamental and challenging problem, playing a crucial role in the preprocessing of various image processing tasks. The introduction of Convolutional Neural Networks (CNNs) into the image denoising domain has yielded significant improvements across different levels of visual tasks. In recent years, models based on the Swin Transformer have also been applied to the image denoising field, demonstrating superior denoising performance that surpasses CNN-based methods, thus becoming advanced techniques in current image denoising research. This paper proposes a Swin-Conv module that combines the local modeling capabilities of residual convolutional layers with the non-local modeling capabilities of the Swin Transformer and integrates this module into the UNet architecture for image denoising. For the dataset used in the model training process, data augmentation techniques were employed to randomly enhance the dataset, thereby improving overall robustness. The results indicate that the proposed Swin Transformer Residual Conv U-Net model shows improvement over current advanced networks, achieving PSNR and SSIM values of 36.09 and 0.963 at sigma = 15, 33.87 and 0.915 at sigma = 25, and 28.96 and 0.810 at sigma = 50.

关键词： image Denoising Deep Learning Transformer Convolutional Neural Networks U-Net

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Theoretical Analysis of an Adaptive Periodic Multi Installment Scheduling With Result Retrieval for SAR image processing

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IEEE TRANSACTIONS ON parallel AND distributed SYSTEMS 2022年第12期33卷 4672-4683页

作者： Chinnappan, Gokul Madathupalyam Veeravalli, Bharadwaj Natl Univ Singapore Dept Elect & Comp Engn Singapore 119077 Singapore

processing a large-scale Synthetic Aperture Radar (SAR) image dataset on a distributed computing infrastructure poses a challenging problem. Large-scale load distribution strategies like multi-installment scheduling (MIS) assume that the size of the result is negligible compared to the input workloads and hence ignore it in their design. Similarly, numerical methods like particle swarm optimization and their variants are not practical for real-time applications, given their run-time complexities. As both the results retrieval and completion time are crucial for SAR image data processing, in this article, we attempt to provide a thorough theoretical analysis of an adaptive MIS that includes the result retrieval phase. We use the periodic nature of the internal installments to keep the strategy simple and fine-tune the last installment to avoid any idle times in the processors. We derive a closed-form solution for the load fractions and hence, the overall processing time, schedule feasibility criteria, and certain other properties that lead to adaptive scheduling. Finally, we validate our theoretical findings through rigorous simulation studies using a loosely connected virtual machines (VMs) topology for the SAR dataset.

关键词： Program processors Radar polarimetry Processor scheduling Load modeling distributed databases Computational modeling Schedules SAR image multi-installment scheduling load distribution front-end processors heterogeneous cluster

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A Multi-staining Digital Pathology image Registration Method Based on Global and Local Computing 27

A Multi-staining Digital Pathology Image Registration Method...

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27th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and parallel/distributed Computing (SNPD)

作者： Ding, Yi Zhang, Xin Lu, Cheng Commun Univ China Sch Comp & Cyber Sci Beijing Peoples R China Guangdong Prov Peoples Hosp Dept Radiol Guangzhou Peoples R China

ISBN: (纸本)9798350391961;9798350391954

To accurately evaluate the patient's condition, medical workers usually need to register multiple pathological images of the lesion site samples. Using computer technology to assist in registration work can effectively improve the efficiency of doctors analyzing pathological images. One of the most advanced methods currently is the Virtual Alignment of Pathology image Series method, which is a multi-staining digital pathology image registration method that combines global and local calculations. However, this method may encounter certain biases when processing images with significant angle differences. Through a detailed analysis of this method, this article proposes an improvement plan which optimizes the acquisition of non-rigid registration mask images, enabling the method to obtain mask images more reasonably and achieve better registration results for images with significant angle differences. This provides more accurate judgment basis and helps doctors diagnose and develop treatment plans more accurately.

关键词： digital pathological images global rigid registration local non-rigid registration feature point detection

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