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24th International conference on Algorithms and Architectures for Parallel processing, ICA3PP 2024

24th International Conference on Algorithms and Architecture...

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24th International conference on Algorithms and Architectures for Parallel processing, ICA3PP 2024

ISBN: (纸本)9789819615247

The proceedings contain 131 papers. The special focus in this conference is on Algorithms and Architectures for Parallel processing. The topics include: MARO: Enabling Full MPI Automatic Refactoring in DSL-Based Programming Framework;SSC: An SRAM-Based Silence Computing Design for On-chip Memory;TP-BFT: A Faster Asynchronous BFT Consensus with Parallel Structure;LTP: A Lightweight On-Chip Temporary Prefetcher for Data-Dependent Memory Accesses;A neural network-Based PUF Protection Method Against Machine Learning Attack;Compression Format and Systolic Array Structure Co-design for Accelerating Sparse Matrix Multiplication in DNNs;multidimensional Intrinsic Identity Construction and Dynamic Seamless Authentication Schemes in IoT Environments;invisible Backdoor Attack with Image Contours Triggers;finestra: Multi-aggregator Swarm Learning for Gradient Leakage Defense;DIsFU: Protecting Innocent Clients in Federated Unlearning;multiple-Round Aggregation of Abstract Semantics for Secure Heterogeneous Federated Learning;dynamic Privacy Protection with Large Language Model in Social networks;a Dynamic Symmetric Searchable Encryption Scheme for Rapid Conjunctive Queries;a Data Watermark Scheme Base on Data Converted Bitmap for Data Trading;distributed Incentive Algorithm for Fine-Grained Offloading in Vehicular Ad Hoc networks;mitigating Over-Unlearning in Machine Unlearning with Synthetic Data Augmentation;AW-YOLOv9: Adverse Weather Conditions Adaptation for UAV Detection;efficient and Privacy-Preserving Ranking-Based Federated Learning;on-Chain Dynamic Policy Evaluation for Decentralized Access Control;DPG-FairFL: A Dual-Phase GAN-Based Defense Framework Against Image-Based Fairness Data Poisoning Attacks in Federated Learning.

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24th International conference on Algorithms and Architectures for Parallel processing, ICA3PP 2024

24th International Conference on Algorithms and Architecture...

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24th International conference on Algorithms and Architectures for Parallel processing, ICA3PP 2024

ISBN: (纸本)9789819615414

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24th International conference on Algorithms and Architectures for Parallel processing, ICA3PP 2024

24th International Conference on Algorithms and Architecture...

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24th International conference on Algorithms and Architectures for Parallel processing, ICA3PP 2024

ISBN: (纸本)9789819615278

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Asynchronous Wireless network for Transmission of neural Data from Thousands of Autonomous Microimplants

Asynchronous Wireless Network for Transmission of Neural Dat...

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2023 IEEE Biomedical Circuits and Systems conference, BioCAS 2023

作者： Lee, Jihun Lee, Ahhyoung Leung, Vincent Laiwalla, Farah Lopez-Gordo, Miguel Larson, Lawrence Nurmikko, Arto Brown University School of Engineering ProvidenceRI United States Baylor University Electrical and Computer Engineering WacoTX United States University of Granada Department of Signal Theory Granada Spain

ISBN: (纸本)9798350300260

We introduce a large-scale wireless RF network concept for capturing neural data from spatially distributed autonomous microchip implants. Each sensor chip is assumed to be capable of event detection by transforming time-varying input signals to spike trains. Inspired by the brain's information processing, we have developed a spectrally efficient, low-error rate asynchronous networking protocol based on a code-division multiple access method. We test the network performance with dozens of sub-millimeter-size silicon microchips in situ on laboratory testbench, complemented by large scale in silico simulations. Through an application example relevant to brain-computer interfaces, we simulate the wireless transmission of spiking data from eight thousand neurons in the primate cortex for accurate hand movement prediction in a cursor control task. © 2023 IEEE.

关键词： Brain-computer interface Spike event communication spiking neural network Wireless neural sensor

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24th International conference on Algorithms and Architectures for Parallel processing, ICA3PP 2024

24th International Conference on Algorithms and Architecture...

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24th International conference on Algorithms and Architectures for Parallel processing, ICA3PP 2024

ISBN: (纸本)9789819615506

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14th International conference on Parallel processing and Applied Mathematics, PPAM 2022

14th International Conference on Parallel Processing and App...

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14th International conference on Parallel processing and Applied Mathematics, PPAM 2022

ISBN: (纸本)9783031304415

The proceedings contain 77 papers. The special focus in this conference is on Parallel processing and Applied Mathematics. The topics include: neural Nets with a Newton Conjugate Gradient Method on Multiple GPUs;Exploring Techniques for the Analysis of Spontaneous Asynchronicity in MPI-Parallel Applications;Cost and Performance Analysis of MPI-Based SaaS on the Private Cloud Infrastructure;building a Fine-Grained Analytical Performance Model for Complex Scientific Simulations;evaluation of Machine Learning Techniques for Predicting Run Times of Scientific Workflow Jobs;Smart Clustering of HPC Applications Using Similar Job Detection Methods;distributed Work Stealing in a Task-Based Dataflow Runtime;task Scheduler for Heterogeneous Data Centres Based on Deep Reinforcement Learning;Shisha: Online Scheduling of CNN Pipelines on Heterogeneous Architectures;General Framework for Deriving Reproducible Krylov Subspace Algorithms: BiCGStab Case;proactive Task Offloading for Load Balancing in Iterative Applications;language Agnostic Approach for Unification of Implementation Variants for Different Computing Devices;high Performance Dataframes from Parallel processing Patterns;global Access to Legacy Data-Sets in Multi-cloud Applications with Onedata;MD-Bench: A Generic Proxy-App Toolbox for State-of-the-Art Molecular Dynamics Algorithms;Breaking Down the Parallel Performance of GROMACS, a High-Performance Molecular Dynamics Software;GPU-Based Molecular Dynamics of Turbulent Liquid Flows with OpenMM;a Novel Parallel Approach for Modeling the Dynamics of Aerodynamically Interacting Particles in Turbulent Flows;reliable Energy Measurement on Heterogeneous Systems–on–Chip Based Environments;distributed Objective Function Evaluation for Optimization of Radiation Therapy Treatment Plans;a Generalized Parallel Prefix Sums Algorithm for Arbitrary Size Arrays;GPU4SNN: GPU-Based Acceleration for Spiking neural network Simulations;Ant System Inspired Heuristic Optimization of UAVs Depl

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24th International conference on Algorithms and Architectures for Parallel processing, ICA3PP 2024

24th International Conference on Algorithms and Architecture...

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24th International conference on Algorithms and Architectures for Parallel processing, ICA3PP 2024

ISBN: (纸本)9789819615445

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14th International conference on Parallel processing and Applied Mathematics, PPAM 2022

14th International Conference on Parallel Processing and App...

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14th International conference on Parallel processing and Applied Mathematics, PPAM 2022

ISBN: (纸本)9783031304446

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Multi-agent Deep Reinforcement Learning Aided Computing Offloading in LEO Satellite networks

Multi-agent Deep Reinforcement Learning Aided Computing Offl...

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IEEE International conference on Communications (IEEE ICC)

作者： Lai, Junyu Liu, Huashuo Sun, Yusong Tan, Huidong Gan, Lianqiang Chen, Zhiyong Univ Elect Sci & Technol China Sch Aeronaut & Astronaut Chengdu Peoples R China

ISBN: (纸本)9781538674628

Legacy computing offloading approaches are originally designed for the terrestrial networks with rather static topologies, and may not be appropriate for the next-generation LEO satellite broadband networks (LSBNs) featured with high dynamicity. This paper presents a multi-agent deep reinforcement learning (MADRL) algorithm for making edge computing multi-level offloading decisions in the LSBNs. Particularly, computing offloading is formulated as a partially observable Markov decision process (POMDP) based multi-agent decision problem. Each LEO satellite is an intelligent agent, either conducting a received edge computing task or forwarding it to its four neighboring satellite or the nearest cloud node on the ground. These agents are fully cooperative and their deep neural network models used to make offloading decisions share the same parameter values and are trained by the same replay buffer. A centralized training and distributed execution framework is utilized to ensure that globally optimized offloading decisions can be achieved based on local observations. Comparative simulation experiments for six representative offloading approaches show that the proposed MADRL aided approach outperforms the others regarding to decreasing edge computing task processing delay and increasing onboard compute resource utilization ratio. In addition, the convergence of this MADRL aided approach is also the best among the three DRL-based approaches.

关键词： LEO satellite broadband network edge computing multi-level offloading multi-agent deep reinforcement learning partially observable Markov decision process

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A Learning-Based Scheduler for High Volume processing in Data Warehouse Using Graph neural networks 22nd

A Learning-Based Scheduler for High Volume Processing in Dat...

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22nd International conference on Parallel and distributed Computing, Applications and Technologies (PDCAT 2021)

作者： Bengre, Vivek HoseinyFarahabady, M. Reza Pivezhandi, Mohammad Zomaya, Albert Y. Jannesari, Ali Iowa State Univ Dept Comp Sci Lab Software Analyt & Pervas Parallelism SwAPP Ames IA USA Univ Sydney Ctr Distributed & High Performance Comp Sch Comp Sci Camperdown NSW Australia

ISBN: (纸本)9783030967727;9783030967710

The process of extracting, transforming, and loading (also known as ETL) of a high volume of data plays an essential role in data integration strategies in data warehouse systems in recent years. In almost all distributed ETL systems currently use in both industrial and academia context, a simple heuristic-based scheduling policy is employed. Such a heuristic policy tries to process a stream of jobs in the best-effort fashion, however, it can result in under-utilization of computing resources in most practical scenarios. On the other hand, such inefficient resource allocation strategy can result in an unwanted increase in the total completion time of data processing jobs. In this paper, we develop an efficient reinforcement learning technique that uses a Graph neural network (GNN) model to combine all submitted tasks graphs into a single graph to simplify the representation of the states within the environment and efficiently make a parallel application for processing of the submitted jobs. Besides, to positively augment the embedding features in each leaf node, we pass messages from leaf to root so the nodes can collaboratively represent actions within the environment. The performance results show up to 15% improvement in job completion time compared to the state-of-the-art machine learning scheduler and up to 20% enhancement compared to a tuned heuristic-based scheduler.

关键词： Extract Transform Load (ETL) operations Scheduling policy Data streaming processing system Graph neural networks Job completion time Reinforcement learning

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