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parallel Approaches in Deep Learning: Use parallel computing 23

Parallel Approaches in Deep Learning: Use Parallel Computing

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Proceedings of the 7th international conference on Future networks and distributed Systems

作者： Mekhriddin Rakhimov Shakhzod Javliev Rashid Nasimov Department of Computer Systems Tashkent University of Information Technologies named after Muhammad Al-Khwarizmi Uzbekistan Department of Artificial Intelligence Tashkent University of Information Technologies named after Muhammad Al-Khwarizmi Uzbekistan Department of Artificial Intelligence Tashkent State University of Economics Uzbekistan

ISBN: (纸本)9798400709036

In the present context, the rise of artificial intelligence (AI) has brought to light the importance of expediting processes due to the advancement in AI. This issue holds significance across various domains of machine learning. Consequently, all sectors linked to deep learning, a crucial facet of artificial intelligence, are experiencing continuous advancements. To illustrate, tasks associated with training such as the multiplication of extensive matrices or the manipulation of images to extricate vital features can lead to an increase in time consumption. It is common knowledge that dealing with substantial data quantities demands a considerable amount of time. The primary focus of this research revolves around significantly enhancing the time efficiency of deep learning procedures. While it is a recognized fact that graphics processing units (GPUs) deliver notably quicker outcomes for specific data handling tasks in comparison to a computer's central processing unit (CPU), this study delves into heterogeneous computing systems in cases where GPUs are inaccessible. Herein, we investigate strategies for attaining elevated processing speed through the utilization of advanced technologies. Ultimately, this study culminates by presenting comparative findings derived from di-verse approaches, accompanied by crucial recommendations for future endeavors.

关键词： CUDA technology OpenCL OpenMP artificial intelligence deep learning heterogeneous computing systems parallel computing parallel processing

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PTQ-SO: A Scale Optimization-based Approach for Post-training Quantization of Edge computing

PTQ-SO: A Scale Optimization-based Approach for Post-trainin...

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international conference on Computer Supported Cooperative Work in Design

作者： Kangkang Liu Ningjiang Chen College of Computer and Electronic Information Guangxi University Nanning China Education Department of Guangxi Zhuang Autonomous Region Key Laboratory of Parallel Distributed and Intelligent Computing Guangxi University Nanning China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

With the increasing performance of deep convolutional neural networks, they have been widely used in many computer vision tasks. However, a huge convolutional neural network model requires a lot of memory and computing resources, which makes it difficult to meet the requirements of low latency and reliability of edge computing when the model is deployed locally on resource-limited devices in edge environments. Quantization is a kind of model compression technology, which can effectively reduce model size, calculation cost and inference delay, but the quantization noise will cause the accuracy of the quantization model to decrease. Aiming at the problem of precision loss caused by model quantization, this paper proposes a post-training quantization method based on scale optimization. By reducing the influence of redundant parameters in the model on the quantization parameters in the process of model quantization, the scale factor optimization is realized to reduce the quantization error and thus improve the accuracy of the quantized model, reduce the inference delay and improve the reliability of edge applications. The experimental results show that under different quantization strategies and different quantization bit widths, the proposed method can improve the accuracy of the quantized model, and the absolute accuracy of the optimal quantization model is improved by 1.36%. The improvement effect is obvious, which is conducive to the application of deep neural network in edge environment.

关键词： Quantization (signal) Accuracy Computational modeling Computer network reliability Delays Reliability Convolutional neural networks

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N-Neuron Simulation Using Multiprocessor Cluster 9th

N-Neuron Simulation Using Multiprocessor Cluster

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9th EAI international conference on Advancement of Science and Technology, ICAST 2021

作者： Shanka, Derara Senay College of Engineering and Technology Electrical and Computer Engineering Department Bule Hora University Bule Hora Ethiopia

ISBN: (纸本)9783030937089

A growing number of research effort has been made to make the Simulation of the astonishing biophysical activities of the massively interconnected organic neurons on various computational platforms such as Field programmable Gate Arrays (FPGAs) and General Purpose Graphics Processing Unit (GP-GPU). Nonetheless, to make efficient and realistic simulation of this biophysical activities, a considerable amount of processing power and memory space is incurred. Although computational platforms come with an advanced solutions such as high performance cluster, and super computers, they come with high establishment cost. Hence, biologically realistic and computationally efficient simulation on consumer level cluster of microprocessor nodes would be helpful, as they give a room for a better performance of compute intensive applications in a relatively cheaper cost as compared to dedicated High performance computing facilities. Accordingly, in this work, large scale simulation of spiking neural network (SNN) on a cluster of 8 physical cores enabled with hyper threading (16 logical cores) is presented. The neural network is composed of the biologically plausible and computationally efficient Izhikevich single neuron model. To improve the performance of the simulation and effectively exploit the computational capacity of the cluster, we have used two parallel programming techniques: distributed parallel programming using Message Passing Interface (MPI) library and distributed shard (hybrid) parallel programming using MPI in tandem with Open Multi-Processing (OpenMP) library. Moreover, to harness the combined memory and computation power of the cluster the neurons were distributed across the nodes using static load balancing mechanism. Hence, we were able to simulate up to 160,000 neurons and 3.2M synapses connection per neuron. Performance evaluation for different configuration of the SNN with a purely MPI and Hybrid parallelization method was presented. Our performance result s

关键词： Neural networks

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TBNDG: Trust-Based Malicious Node Detection for Grey-Hole Attacks in VANET’s

TBNDG: Trust-Based Malicious Node Detection for Grey-Hole At...

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international conference on parallel distributed and Grid computing (PDGC)

作者： Soumya Ranjan Mohapata Rekha Jain Srinivas Koushik K Sandeep Varma Kalidindi Umashankar Ghugar Govt College of Engineering Keonjhar Odisha Department of Computer Application Manipal University Jaipur Department of Computer Science & Engineering GITAM University Visakhapatnam Department of Computer Science & Engineering OP Jindal University Raigarh Odisha

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

Vehicular advertising networks (VANETs) are vulnerable to various security threats due to their dynamic and decentralized nature. One of the most critical threats is the grey hole attack, where malicious nodes selectively drop packets, which poses a significant threat to communication reliability and security in VANETs. This paper proposes a robust approach to detect malicious nodes involved in grey-hole attacks. Our method uses the concept of trust to distinguish between trusted and untrusted nodes in the network. Each node maintains a trust score for neighbouring nodes based on their behaviour and influence. Trust is calculated through direct observation, such as packet forwarding and message integrity checks, and indirect feedback from other nodes in the network. To effectively detect grey-hole attackers, we use a multi-dimensional trust evaluation mechanism that considers several factors, such as packet delivery consistency, message integrity, and node behaviour history. By summing up these factors, we calculate a comprehensive confidence score for each point that allows us to identify potential malicious actors. In addition, we introduced updates to the trust and consensus mechanisms between neighbouring nodes to improve the strength of our approach against reconciliation and false accusations. Nodes periodically exchange trust information and check the consistency of their trust estimates, thus reducing the impact of false positives and negatives. We evaluate the performance of our proposed trust-based detection scheme through extensive simulations using real-world VANET scenarios. The results show that it detects grey-hole attackers with high accuracy, low computational complexity, and effective communication. In summary, our proposed model TBNDG calculates the performance in terms of PDR, End-To-End Delay, Routing Overhead, Packet Loss Ratio and Throughput than the existing model

关键词： Computational modeling Vehicular ad hoc networks Packet loss Throughput Routing Routing protocols Delays Security Reliability Vehicle dynamics

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Enhancing the Scalability of Knowledge Discovery Services for Massive Data Mining on Clouds

Enhancing the Scalability of Knowledge Discovery Services fo...

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IoT Based Control networks and Intelligent Systems (ICICNIS), international conference on

作者： Vijayakumar Sajjan Jayam Pradeep Kumar Vikram Kalvala Narsaiah Domala B. Kiran Kumar C P Pavan Kumar Hota Department Of CSE Malla Reddy University Hyderabad Department Of ECE Chaitanya Deemed to be University Hyderabad Department Of ECE MLR Institute Of Technology Hyderabad Department Of Artificial Intelligence Shri Vishnu Engineering College for Women Bhimavaram

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

The traditional data mining techniques do not work well when it comes to the big data and the tremendous growth rate of the digital data also becomes a major challenge to the discovery of knowledge. Cloud computing offers an ideal solution to computationally and storage intensive tasks with scalable and on-demand services that can process big data at short notice. This paper reviews the current models that employ cloud structure for distributed data mining as well as models like CosDic and the Data Mining Cloud Framework (DMCF). The way of processing large datasets is also represented well by the Map Reduce model where it is possible to implement the data processing in parallel on several nodes. Despite the fact that cloud-based systems are very useful when it comes to the management of large amounts of data, challenges such as privacy, data security and data coordination still persist. These innovative methodologies include Edge computing and Federated Learning that are anticipated to solve the mentioned issues by enhancing the speed of data processing and its safety. Thus, scalable cloud data mining to meet growing needs of knowledge discovery in big data contexts will have to develop new privacy protection mechanisms, integration of cloud models, and complex algorithms.

关键词： Cloud computing Federated learning Reviews Scalability Computational modeling Big Data Knowledge discovery Data processing Data models Edge computing

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CAPTURE: Memory-Centric Partitioning for distributed DNN Training with Hybrid parallelism

CAPTURE: Memory-Centric Partitioning for Distributed DNN Tra...

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international conference on High Performance computing

作者： Henk Dreuning Kees Verstoep Henri E. Bal Rob V. van Nieuwpoort University of Amsterdam Amsterdam The Netherlands Vrije Universiteit Amsterdam Amsterdam The Netherlands Leiden University Leiden The Netherlands

Deep Learning (DL) model sizes are increasing at a rapid pace, as larger models typically offer better statistical performance. Modern Large Language Models (LLMs) and image processing models contain billions of trainable parameters. Training such massive neural networks incurs significant memory requirements and financial cost. Hybrid-parallel training approaches have emerged that combine pipelining with data and tensor parallelism to facilitate the training of large DL models on distributed hardware setups. However, existing approaches to design a hybrid-parallel partitioning and parallelization plan for DL models focus on achieving high throughput and not on minimizing memory usage and financial cost. We introduce CAPTURE, a partitioning and parallelization approach for hybrid parallelism that minimizes peak memory usage. CAPTURE combines a profiling-based approach with statistical modeling to recommend a partitioning and parallelization plan that minimizes the peak memory usage across all the Graphics Processing Units (GPUs) in the hardware setup. Our results show a reduction in memory usage of up to 43.9% compared to partitioners in state-of-the-art hybrid-parallel training systems. The reduced memory footprint enables the training of larger DL models on the same hardware resources and training with larger batch sizes. CAPTURE can also train a given model on a smaller hardware setup than other approaches, reducing the financial cost of training massive DL models.

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Machine Learning-empowered Network Measurement: A Critical Path to Traffic Anomaly Detection in IoT-enabled Smart Grid

Machine Learning-empowered Network Measurement: A Critical P...

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international conference on parallel and distributed Systems (ICPADS)

作者： Hongrui Zang Hongbo Liu Wei Sun Guangyuan Zhang Xiangyu Kong State Grid Jilin Electric Power Company Limited Communication Company Branch Changchun China School of Computer Science and Technology Dalian University of Technology Dalian China

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

Large-scale deployment of Internet of Things (IoT) devices provides efficient data collection and control capabilities in the smart grid, while edge computing plays a key role in increasing the speed of data processing and reducing latency. The growth of edge computing in the smart grid is inevitably increasing the stability requirement in network transmission and the necessity of adequate network measurement to detect traffic anomalies. There are two challenges for measurement in IoT-enabled networks. The first challenge is the high processing speed and limited memory space. The second challenge is the varying network traffic. This paper studies how to use sketch techniques to fulfill the required capabilities of network measurement. Sketches have been considered as the most promising solution for network measurement in recent years, because they greatly optimize the speed and memory usage at the cost of small error. However, most sketches do not work well for varying network traffic. These sketches require to adjust their internal memory usage while the optimal point is sensitive to specified flow size distribution and memory size. To address this problem, we propose a machine learning empowered sketch framework. The proposed sketch framework trains a neural network model that online adjusts the memory usage based on a small sample of flow. We conduct data-driven simulations to evaluate the proposed framework. By comparing measurement results on public dataset, we can see that the proposed framework significantly improves the accuracy of measurement and anomaly detection without sacrificing the line-rate processing capability.

关键词： Accuracy Power measurement Memory management Estimation Telecommunication traffic Smart grids Resource management Internet of Things Anomaly detection Edge computing

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Order, Unite, and Conquer: A Group Formulation for Multi-Armed Bandits in Microservice Provisioning 33

Order, Unite, and Conquer: A Group Formulation for Multi-Arm...

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33rd Euromicro international conference on parallel, distributed, and Network-Based Processing, PDP 2025

作者： Labres, Joao Vitor Bruniera De Souza, Paulo Silas Severo Moreira, Deborah Victoria Lima Da Silva, Paulo Ricardo Branco De Paiva, Michel Bernardo Alves, Erika Costa Azevedo, Layane Menezes Campinas Brazil Alegrete Brazil

ISBN: (纸本)9798331524937

Bringing intelligent decision-making to industrial facilities involves coordinating a complex ecosystem of applications with different performance requirements that often depend on each other. A promising approach to managing this complexity is leveraging loosely coupled software architectures, such as microservices, with distributed infrastructures, such as the Edge-to-Cloud Continuum. The order in which microservices are provisioned in the Edge-to-Cloud Continuum can significantly impact outcomes when distributing tasks to agents. Poor provisioning orderings can lead to non-critical microservices overburdening servers with lower latency while leaving latencysensitive microservices with no option but to run on more distant servers. To address this challenge, we introduce a novel provisioning ordering mechanism within a Multi-Armed Bandit (MAB) algorithm, helping to conquer the problem. Simulations based on real-world specifications indicate a $\mathbf{3 8 \%}$ reduction in latency violations and up to 6% decrease in power consumption compared to an unordered approach. © 2025 IEEE.

关键词： Edge computing

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Privacy-Preserving Edge Assistance for Solving Matrix Eigenvalue Problem

Privacy-Preserving Edge Assistance for Solving Matrix Eigenv...

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international conference on Computer Communications and networks (ICCCN)

作者： Xiaotong Zhao Hanlin Zhang Jie Lin Fanyu Kong Leyun Yu College of Computer Science and Technology Qingdao University Qingdao China School of Computer Science and Technology Xian Jiaotong University Xian China School of Computer Science and Technology Shandong University Jinan China JIC IOT CO. LTD Nanchang China

ISBN: (数字)9798350384611

ISBN: (纸本)9798350348439

The large-scale matrix eigenvalue computation, as a basic mathematical tool, has been widely used in many fields such as face recognition and data analysis. However, local terminal devices lack sufficient resources to undertake heavy computational tasks, which poses a challenge to the applications of eigenvalue computation. In this paper, we propose the first privacy-preserving edge-assisted computation scheme for solving the largest eigenvalue and corresponding eigenvector. We propose a privacy-preserving transformation method to protect data privacy and prevent edge servers from retrieving sensitive information. Mean-while, we design a verification scheme to ensure the correctness of the results returned by the edge servers. In addition, we design a distributed parallel computing scheme to ensure the efficiency of edge computation. Through theoretical analysis and simulation experiments, we verify the feasibility and efficiency of our proposed scheme.

关键词： Performance evaluation Data privacy Protocols Face recognition parallel processing Eigenvalues and eigenfunctions Computational efficiency

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27th international European conference on parallel and distributed computing, Euro-Par 2021

27th International European Conference on Parallel and Distr...

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27th international European conference on parallel and distributed computing, Euro-Par 2021

ISBN: (纸本)9783030856649

The proceedings contain 38 papers. The special focus in this conference is on parallel and distributed computing. The topics include: An MPI-based Algorithm for Mapping Complex networks onto Hierarchical Architectures;pipelined Model parallelism: Complexity Results and Memory Considerations;efficient and Systematic Partitioning of Large and Deep Neural networks for parallelization;A GPU Architecture Aware Fine-Grain Pruning Technique for Deep Neural networks;Towards Flexible and Compiler-Friendly Layer Fusion for CNNs on Multicore CPUs;smart distributed DataSets for Stream Processing;colony: parallel Functions as a Service on the Cloud-Edge Continuum;horizontal Scaling in Cloud Using Contextual Bandits;geo-distribute Cloud Applications at the Edge;Automatic Low-Overhead Load-Imbalance Detection in MPI Applications;A Fault Tolerant and Deadline Constrained Sequence Alignment Application on Cloud-Based Spot GPU Instances;sustaining Performance While Reducing Energy Consumption: A Control Theory Approach;algorithm Design for Tensor Units;a Scalable Approximation Algorithm for Weighted Longest Common Subsequence;TSLQueue: An Efficient Lock-Free Design for Priority Queues;G-Morph: Induced Subgraph Isomorphism Search of Labeled Graphs on a GPU;accelerating Graph Applications Using Phased Transactional Memory;Efficient GPU Computation Using Task Graph parallelism;towards High Performance Resilience Using Performance Portable Abstractions;Enhancing Load-Balancing of MPI Applications with Workshare;trace-Based Workload Generation and Execution;particle-In-Cell Simulation Using Asynchronous Tasking;Exploiting Co-execution with OneAPI: Heterogeneity from a Modern Perspective;designing a 3D parallel Memory-Aware Lattice Boltzmann Algorithm on Manycore Systems;Fault-Tolerant LU Factorization Is Low Cost;Mixed Precision Incomplete and Factorized Sparse Approximate Inverse Preconditioning on GPUs;GPU-Accelerated Mahalanobis-Average Hierarchical Clustering Analysis.

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