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Dynamic DNN Decomposition for Lossless Synergistic Inference 41

Dynamic DNN Decomposition for Lossless Synergistic Inference

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IEEE 41st international conference on distributed Computing Systems (ICDCS)

作者： Zhang, Beibei Xiang, Tian Zhang, Hongxuan Li, Te Zhu, Shigiang Gu, Jianjun Zhejiang Lab Intelligent Robot Res Ctr Hangzhou Peoples R China

ISBN: (纸本)9781665449328

Deep neural networks (DNNs) sustain high performance in today's data processing applications. DNN inference is resource-intensive thus is difficult to fit into a mobile device. To solve this problem, recent advances unleash DNN services using the edge computing paradigm. The existing approaches split a DNN into two parts and deploy the two partitions to computation nodes at two edge computing tiers. Nonetheless, these methods overlook collaborative device-edge-cloud computation resources. Besides, previous algorithms demand the whole DNN re-partitioning to adapt to computation resource changes and network dynamics. Moreover, for resource-demanding convolutional layers, prior works do not give a parallel processing strategy without loss of accuracy at the edge side. To tackle these issues, we propose D-3, a dynamic DNN decomposition system for synergistic inference without precision loss. The proposed system introduces a heuristic algorithm named horizontal partition algorithm to split a DNN into three parts. The algorithm partially adjust the partitions at run time according to processing time and network conditions. At the edge side, a vertical separation module separates feature maps into tiles that can be independently run on different edge nodes in parallel. Extensive quantitative evaluation of five popular DNNs illustrates that D-3 outperforms the stateof-the-art counterparts up to 3.4X in end-to-end DNN inference time and reduces backbone network communication overhead up to 3.68X.

关键词： distributed computing Edge computing DNN inference acceleration

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Proceedings - 29th Euromicro international conference on parallel, distributed and Network-Based processing, PDP 2021

Proceedings - 29th Euromicro International Conference on Par...

引用

29th Euromicro international conference on parallel, distributed and Network-Based processing, PDP 2021

ISBN: (纸本)9781665414555

The proceedings contain 43 papers. The topics discussed include: building representative and balanced datasets of OpenMP parallel regions;oneAPI for GPUs and FPGAs: portability, yes!, performance portability, not quite;attack surface assessment for cybersecurity engineering in the automotive domain;a compact encoding of security logs for high performance activity detection;a federated content distribution system to build health data synchronization services;nonblocking data structures for distributed-memory machines: stacks as an example;bucket MapReduce: relieving the disk I/O intensity of data-intensive applications in MapReduce frameworks;an efficient practical non-blocking pagerank algorithm for large scale graphs;parallel asynchronous stochastic dual coordinate descent algorithm s for high efficiency and stable convergence;and a synchronized and dynamic distributed graph structure to allow the native distribution of multi-agent system simulations.

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A Hands-On Approach to Teaching parallel and Heterogeneous Computing

A Hands-On Approach to Teaching Parallel and Heterogeneous C...

引用

IEEE international conference on High Performance Computing Workshops (HiPCW)

作者： Abubeker Abdurhaman Arihant Singh Abrar Hossain Kishwar Ahmed Department of Electrical Engineering and Computer Science The University of Toledo Toledo OH USA

ISBN: (数字)9798331509118

ISBN: (纸本)9798331509125

In this paper, we present our experiences teaching heterogeneous and parallel computing to high-school students through hands-on activities leveraging the Nvidia Jetson Nano devices and Google Colab platform. We designed activity modules and developed software applications to improve students’ understanding of heterogeneous and parallel computing concepts. We also present the feedback we received from the students, and analyze the effectiveness of our proposed modules and techniques. The findings highlight the importance of using a hands-on and activity-based approach to introduce students to the practical aspects of parallel and heterogeneous computing. Furthermore, we discuss key insights we gained from the two summer workshop experiences and present potential improvements for future iterations of the proposed activities.

关键词： Surveys conferences High performance computing Education Refining parallel processing Heterogeneous networks Nanoscale devices Software Internet

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5th international Symposium on Dynamic Response and Failure of Composite Materials, DRAF 2024

5th International Symposium on Dynamic Response and Failure ...

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5th international Symposium on Dynamic Response and Failure of Composite Materials, DRAF 2024

ISBN: (纸本)9783031776960

The proceedings contain 38 papers. The special focus in this conference is on Dynamic Response and Failure of Composite Materials. The topics include: Detection of Impact Damages on Full Scale Wing Using distributed Fiber Optics Sensors Network;evolution of Unstable Skin-Stringer Debonding Propagation in Composite Aircraft Structures: Implications on Damage Tolerant Design;a Benchmark Between Conventional and Custom Heat Treaments for Inconel 718 Alloy Processed Through Cold Metal Transfer Technology;real-Time Shearography Measurements of 3D Printed Auxetic Specimens During Flexural Tests;analysis of the Crushing Behavior of Flat Composite Plates Produced by Sheet Molding Compound;effect of Tightening Torque on Bearing Performance of Kevlar Fiber Composite Bolted Joints Produced by Additive Manufacturing;general Damage Detections on Composite Panels Using Computer Vision Algorithms;Crashworthiness of C-Shaped CFRP Composites: A Numerical and Experimental Study;a Numerical-Experimental Study on the Impact Behaviour of Additive Manufactured bcc Lattice Structures;development of a Numerical Methodology Able to Simulate the Unstable Mixed Mode Delamination Growth in Stiffened Composite Panels Under Cyclic Loading Conditions;Numerical Investigation on the Static and Dynamic Behaviour of an Additive Manufactured UAV’s Horizontal Tail;Low Velocity Impact (LVI) Simulation of a CFRP Ply-Drop Laminates: A Numerical Analysis;structural Sandwich Panels for Roofing Systems in Civil applications;Comparison of Various processing Treatments on Uniaxial Compression Performance of Large Scale Additively Manufactured Thermoplastic ABS Chopped Fiber Composite Structures;layer Height Treatments on Uniaxial Compression Performance of Industrial Large-Scale Additively Manufactured Polymer Composite Structures;homogenization techniques for Nanocomposites: A Comprehensive Review.

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Digital Signature techniques for IoT Privacy and Trust Management

Digital Signature Techniques for IoT Privacy and Trust Manag...

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Emerging Technologies for Dependable Internet of Things (ICETI), international conference on

作者： Qais Abd Al-Salam Mohammed Mohammed Joudah Zaiter Hamid Mohammed Farhan Department of Computer and Communication Engineering Electrical Engineering Technical College Middle Technical University Baghdad Iraq

ISBN: (数字)9798331533557

ISBN: (纸本)9798331533564

In this article, a live solution for data acquisition from sensors and its representation on a web interface is discussed with the help of a Raspberry Pi in which MQTT is used for communication the Flask as the framework. Particularly emphasized is the improvement of the efficiency and the time needed for generating Digital signature using the ECDSA algorithm which is traditionally slow and requires much resources to be applied for real time IoT applications. For this, the authors try the approach of parallel processing on the Raspberry Pi which becomes many times shorter in generation of the signatures without any loss of the data integrity. It involves input data including temperature, humidity, motion, distance, and light intensity, processes them and then sends the input data over a secure MQTT connection. They also showed that the FCMP algorithm incurs less computation time, and the speed attained is almost constant and stable when parallel processing is incorporated in the generation of some of the ECDSA algorithm’s signatures. As presented in the experimental results, the processing time is reduced in comparison with sequential processing, which emphasizes the possibility of parallelization. Altogether we can state that the usage of Raspberry Pi and Flask as the core of the system along with MQTT as a communication protocol provides the necessary level of security and reliability for monitoring the sensor data in the IoT system. They show that parallel processing can indeed help enhance the cryptographic operations making this approach beneficial for performance optimization across multiple IoT applications.

关键词： Temperature sensors Temperature distribution Protocols Humidity Sensor systems Real-time systems Sensors Internet of Things Monitoring Digital signatures

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Top-k Dominating Queries on Incremental Datasets

Top-k Dominating Queries on Incremental Datasets

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27th international conference on Database Systems for Advanced applications (DASFAA)

作者： Wu, Jimmy Ming-Tai Wang, Ke Lin, Jerry Chun-Wei Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao Peoples R China Western Norway Univ Appl Sci Dept Comp Sci Elect Engn & Math Sci Bergen Norway

ISBN: (纸本)9783031112171;9783031112164

Top-k dominance (TKD) query for incomplete datasets is a popular preference query for incomplete data, which analyzes the dominance relationships among objects in a dataset by a dominance method to reveal the top-k most valuable information in the dataset. At present, in-depth research has been conducted on this topic, and efficient query algorithms based on various pruning strategies have been proposed, as well as optimization algorithms based on a distributed computing framework for processing large-scale datasets. With the advent of the information age, data update iterations are accelerated, and in the face of dynamically updated data, the traditional TKD query algorithm based on static data can no longer meet our needs, and an efficient algorithm based on the dynamically updated data set environment is needed. In this paper, we conduct an in-depth study on the TKD query problem for dynamically updated incomplete datasets, and propose a dynamic update parallel algorithm based on MapReduce framework. The algorithm utilizes the query results of historical datasets, avoids the repeated analysis of the dominant relationships between historical objects, optimizes the computation process, reduces the space occupation, and proves through experiments that the dynamic update algorithm has more obvious advantages compared with the traditional algorithm.

关键词： Incomplete datasets Top-k dominance query Static datasets Dynamic update parallel compute

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Zamna: a tool for the secure and reliable storage, sharing, and usage of large data sets in data science applications

Zamna: a tool for the secure and reliable storage, sharing, ...

引用

IEEE Mexican international conference on Computer Science (ENC)

作者： Carrizales-Espinoza, Diana Gonzalez-Compean, J. L. Morales-Sandoval, Miguel Cinvestav Tamaulipas Victoria Mexico

ISBN: (数字)9781665473477

ISBN: (纸本)9781665473477

The exponential data growth (volume), continuously produced from different sources (variety) such as sensors, end-users, and IoT devices, has motivated the creation of efficient data science applications to obtain useful information (value and veracity) in critical decision-making processes (velocity). In health scenarios, the building of data science systems not only must include efficient modules for the acquisition, processing, and visualization of the data, but also transversal layers are required for the management, delivery and storage of data in a secure, reliable, and efficient manner in accordance with different norms or regulations for the handling of sensitive data. This is even more complex when the data science systems are distributed in environments of multiple infrastructures or in current and future computing models (for example, edge, fog, cloud or any combination of them). This article presents Zamna, a computational tool that allows end-users to support data science services by providing the resources for the secure and reliable data management, delivery and storage. Zamna manages the exchange and preparation of data in automatic and transparent manners, from acquisition until consumption by decision makers, which is a fundamental part in practically any data science application. Through its services, Zamna allows the automatic and transparent meeting of standards and regulations of data management, guaranteeing privacy, confidentiality, integrity, and availability of content, as well as tolerance to service failures and traceability. In a case study in the e-health domain, Zamna, allows achieving 70% compliance with international standards for the secure handling of sensitive data at the time that exhibited high performance due to the parallel patterns used in its construction.

关键词： Data science Reliability Computer science Standards Regulation NIST Big Data

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Combining Federated Learning and Blockchain for Enhanced Traceability in Agricultural Supply Chains 2

Combining Federated Learning and Blockchain for Enhanced Tra...

引用

2nd IEEE international conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Bhaskar, Nuthanakanti Margarat, G.Simi Al-Farouni, Mohammed Nagaraju, Krishna Chythanya Kumar, Rakesh Krishnakumar, K. Department of CSE CMR Technical Campus Telangana Hyderabad India New Prince Shri Bhavani College of Engineering and Technology Department of CSE Tamil Nadu Chennai600073 India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq GRIET Bachupally Department of CSE Telangana Hyderabad India GLA University Department of Computer Engineering & Applications Mathura India Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350368109

To address these challenges, this research will adopt a federated learning method and implement the use of a blockchain system to implement a traceability system on the agricultural chain. Based on the voluntary collaboration of decentralized datasets and blockchain-based ledgers, the technology is poised to enhance the transparency, effectiveness, and reliability of the agricultural chain. TensorFlow Federated utilized in the development of the federated learning model exhibits cifar-10 acuracy of 0. 92, precision of 0. 88, recall of 0. 94 and F1 score of 0, indicating that the classifier significantly outperforms the baseline random forest classifier. 91. The model is tested for joint training from the distributed data sources and shows relatively reasonable training time (12 hrs) and communication complexity (8. 5 GB). The state of affairs of blockchain transactions show that successful transaction records up to 1800 are registered with various activities such as product registration, quality certification of the product, record of transportation of the products, and storage records of the products etc. The datasets included here include various types of agricultural data that cover activities taking place on the farming fields and in processing plants, distributor's centers, and retail stores amongst other areas of concern and may cover data such as crop yields, weather updates, and transportation routes among others. Some of the software tools used in the development process include Python that can be used for data preprocessing and analysis;TensorFlow Federated which can be used in the creation of federated learning models;Ethereum can be used in the implementation of blockchain. This approach provides an invaluable roadmap for successfully tackling important demands in agricultural SCM systems and optimizing the success of decision-makers and entities along the food supply chain. © 2024 IEEE.

关键词： Software testing

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parallel Approximations for High-Dimensional Multivariate Normal Probability Computation in Confidence Region Detection applications

Parallel Approximations for High-Dimensional Multivariate No...

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international Symposium on parallel and distributed processing (IPDPS)

作者： Xiran Zhang Sameh Abdulah Jian Cao Hatem Ltaief Ying Sun Marc G. Genton David E. Keyes Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Statistics Program King Abdullah University of Science and Technology Extreme Computing Research Center King Abdullah University of Science and Technology Thuwal Saudi Arabia Department of Mathematics The University of Houston Houston Texas USA

ISBN: (数字)9798350387117

ISBN: (纸本)9798350387124

Addressing the statistical challenge of computing the multivariate normal (MVN) probability in high dimensions holds significant potential for enhancing various applications. For example, the critical task of detecting confidence regions where a process probability surpasses a specific threshold is essential in diverse applications, such as pinpointing tumor locations in magnetic resonance imaging (MRI) scan images, determining hydraulic parameters in groundwater flow issues, and forecasting regional wind power to optimize wind turbine placement, among numerous others. One common way to compute high-dimensional MVN probabilities is the Separation-of-Variables (SOV) algorithm. This algorithm is known for its high computational complexity of O(n 3 ) and space complexity of O(n 2 ), mainly due to a Cholesky factorization operation for an n×n covariance matrix, where n represents the dimensionality of the MVN problem. This work proposes a high-performance computing framework that allows scaling the SOV algorithm and, subsequently, the confidence region detection algorithm. The framework leverages parallel linear algebra algorithms with a task-based programming model to achieve performance scalability in computing process probabilities, especially on large-scale systems. In addition, we enhance our implementation by incorporating Tile Low-Rank (TLR) approximation techniques to reduce algorithmic complexity without compromising the necessary accuracy. To evaluate the performance and accuracy of our framework, we conduct assessments using simulated data and a wind speed dataset. Our proposed implementation effectively handles high-dimensional multivariate normal (MVN) probability computations on shared and distributed-memory systems using finite precision arithmetics and TLR approximation computation. Performance results show a significant speedup of up to 20X in solving the MVN problem using TLR approximation compared to the reference dense solution without sacrificin

关键词： Accuracy Tensors Wind speed Magnetic resonance imaging Linear algebra Wind power generation Approximation algorithms

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Accelerating Quantum Subcircuit Reconstruction Utilizing Multi-Node Computation

Accelerating Quantum Subcircuit Reconstruction Utilizing Mul...

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Quantum Computing and Engineering (QCE), IEEE international conference on

作者： Chuck Garcia Ellie Vogel Wei Tang Margaret Martonosi University of Texas at Austin Duke University Princeton University

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

Today's Quantum Computers (QCs) face significant engineering challenges that limit their size and fidelity. To execute realistic applications, researchers must develop ways to move past these constraints. CutQC enables small QCs to run larger quantum circuits by cutting circuits into smaller subcircuits and then utilizing classical resources and Kronecker products to reproduce the desired output. In this work, we enhance the reconstruction process. In particular, we develop a distributed PyTorch implementation of CutQC's classical post-processing step, which can run across multiple nodes on either GPU or CPU devices. Our results show that our PyTorch implementation executes circuits of up to 35 qubits on 10- and 15-qubit QCs with efficient reconstruction. We use single-node computation as a baseline and then compare the results to the runtimes of our PyTorch workflows. By utilizing parallelism and specified devices, the reconstruction step of hybrid quantum-classical circuit execution can be improved. The original CutQC paper demonstrated a 60X to 8600X runtime speedup over classical simulation, and our work increases this speedup. These results show that large quantum circuits can be run on smaller QCs by leveraging circuit cutting with data parallelism techniques to maintain a reasonable runtime. This allows researchers to maintain the high fidelity of smaller machines while executing larger circuits.

关键词： Computers Runtime Qubit Graphics processing units parallel processing Central processing Unit Quantum circuit Faces

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