The proceedings contain 65 papers. The topics discussed include: an energy efficient clustering protocol using minimum spanning tree for wireless sensor networks;DOA estimation for rectangular linear array antenna in ...
ISBN:
(纸本)9783642240362
The proceedings contain 65 papers. The topics discussed include: an energy efficient clustering protocol using minimum spanning tree for wireless sensor networks;DOA estimation for rectangular linear array antenna in frequency non selective slow fading MIMO channels;vector quantization based face recognition using integrated adaptive fuzzy clustering;transformation of active reference graph into passive reference graph for distributed garbage collection;face recognition using fuzzy neural network classifier;impulse noise removal from grayscale images using fuzzy genetic algorithm;reliability estimation of mobile agents for service discovery in MANET;a state-of-the-art survey on ids for mobile ad-hoc networks and wireless mesh networks;manipulating objects through hand gesture recognition in virtual environment;efficient ID-based signature scheme from bilinear map;and sleep scheduler protocol for network reliability in wireless sensor networks.
The proceedings contain 55 papers. The special focus in this conference is on parallel and distributedcomputing: Applications and Technologies. The topics include: A Meta-reinforcement Learning Framework for Ada...
ISBN:
(纸本)9789819642069
The proceedings contain 55 papers. The special focus in this conference is on parallel and distributedcomputing: Applications and Technologies. The topics include: A Meta-reinforcement Learning Framework for Adaptive Quadrotor UAV Attitude Control;Securing Energy Transactions for Electric Vehicles: The Blockchain Approach and Encrypted NFTs;optimizing Task Allocation in Heterogeneous Agent Manufacturing Systems;MPG: Multi-modal Personal Health Graph for Alzheimer’s Disease Diagnosis;SMAC: A Secure Multi-authority Access Control Scheme with Attribute Unification for Fog Enabled IoT in E-Health;Convolutional Neural Networks Parameter Training for SCM Algorithm Based on Hausdorff Difference;handling Non-stationarity with Distribution Shifts and Data Dependency in Time Series Forecasting;the Two-Stage Stochastic Facility Location Game;regularized Non-monotone γ-weakly Submodular Maximization;fed-MoE: Efficient Federated Learning for Mixture-of-Experts Models via Empirical Pruning;WaitIO-Hybrid: Communication for Coupling MPI Programs Among Heterogeneous Systems;the Material Delivery Route Prediction Method Based on Deep Reinforcement Learning;privacy-Preserving in Medical Image Analysis: A Review of Methods and Applications;research on Task Migration Problem Based on Link Uncertainty in Adversarial Scenarios;optimizing Production Component Scheduling in Multivariate Industrial Networks with Dynamic Changes in Production Costs;multi-agent Collaboration for Time-Sensitive Tasks in Multiple Networked Adversarial Scenarios;containerized Data-Flow Processing for Scalable Real-Time Analytics on Edge Devices;fast Approximation for Scheduling Malleable Jobs on parallel Batch Machines with Rejection;real-Time and In-Situ Temperature Profiling for Determining Detonation of White Dwarf Mergers;accparser: A Standalone OpenACC Parser and Its Usage on Mapping OpenACC to OpenMP Directives;Out-of-Memory GPU Sorting Using Asynchronous CUDA Streams;long-Term and Periodicity-Aware Spatio
This special issue is dedicated to examining the rapidly evolving fields of artificial intelligence, mathematical modeling, and optimization, with particular emphasis on their growing importance in computational scien...
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This special issue is dedicated to examining the rapidly evolving fields of artificial intelligence, mathematical modeling, and optimization, with particular emphasis on their growing importance in computational science. It features the most notable papers from the "Mathematical Modeling and Problem Solving" workshop at PDPTA'24, the 30th internationalconference on parallel and distributed Processing Techniques and Applications. The issue showcases pioneering research in areas such as natural language processing, system optimization, and high-performance computing. The nine selected studies include novel AI-driven methods for chemical compound generation, historical text recognition, and music recommendation, along with advancements in hardware optimization through reconfigurable accelerators and vector register sharing. Additionally, evolutionary and hyper-heuristic algorithms are explored for sophisticated problem-solving in engineering design, and innovative techniques are introduced for high-speed numerical methods in large-scale systems. Collectively, these contributions demonstrate the significance of AI, supercomputing, and advanced algorithms in driving the next generation of scientific discovery.
The proceedings contain 24 papers. The special focus in this conference is on parallel and distributed Processing Techniques. The topics include: parallel N-Body Performance Comparison: Julia, Rust, and More;REFT...
ISBN:
(纸本)9783031856372
The proceedings contain 24 papers. The special focus in this conference is on parallel and distributed Processing Techniques. The topics include: parallel N-Body Performance Comparison: Julia, Rust, and More;REFT: Resource-Efficient Federated Training Framework for Heterogeneous and Resource-Constrained Environments;An Efficient Data Provenance Collection Framework for HPC I/O Workloads;using Minicasts for Efficient Asynchronous Causal Unicast and Byzantine Tolerance;a Comparative Study of Two Matrix Multiplication Algorithms Under Current Hardware Architectures;Is Manual Code Optimization Still Required to Mitigate GPU Thread Divergence? Applying a Flattening Technique to Observe Performance;towards Automatic, Predictable and High-Performance parallel Code Generation;Attack Graph Generation on HPC Clusters;analyzing the Influence of File Formats on I/O Patterns in Deep Learning;inference of Cell–Cell Interactions Through Spatial Transcriptomics Data Using Graph Convolutional Neural Networks;natural Product-Like Compound Generation with Chemical Language Models;improved Early–Modern Japanese Printed Character Recognition Rate with Generated Characters;Improved Method for Similar Music Recommendation Using Spotify API;Reconfigurable Virtual Accelerator (ReVA) for Large-Scale Acceleration Circuits;Building Simulation Environment of Reconfigurable Virtual Accelerator (ReVA);vector Register Sharing Mechanism for High Performance Hardware Acceleration;Efficient Compute Resource Sharing of RISC-V Packed-SIMD Using Simultaneous Multi-threading;introducing Competitive Mechanism to Differential Evolution for Numerical Optimization;hyper-heuristic Differential Evolution with Novel Boundary Repair for Numerical Optimization;jump Like a Frog: Optimization of Renewable Energy Prediction in Smart Gird Based on Ultra Long Term Network;vision Transformer-Based Meta Loss Landscape Exploration with Actor-Critic Method;Fast Computation Method for Stopping Condition of Range Restricted
Blockchain technology is characterized by its distributed, decentralized, and immutable ledger system which serves as a fundamental platform for managing smart contract transactions (SCTs). However, these SCTs undergo...
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ISBN:
(纸本)9783031814037;9783031814044
Blockchain technology is characterized by its distributed, decentralized, and immutable ledger system which serves as a fundamental platform for managing smart contract transactions (SCTs). However, these SCTs undergo sequential validation within a block which introduces performance bottlenecks in blockchain. In response, this paper introduces a framework called the Multi-Bin parallel Scheduler (MBPS) designed for parallelizing blockchain smart contract transactions to leverage the capabilities of multicore systems. Our proposed framework facilitates concurrent execution of SCTs, enhancing performance by allowing non-conflicting transactions to be processed simultaneously while preserving deterministic order. The framework comprises of three vital stages: conflict detection, bin creation, and execution. We conducted an evaluation of our MBPS framework in Hyperledger Sawtooth v1.2.6, revealing substantial performance enhancements compared to existing parallel SCT execution frameworks across various smart contract applications. This research contributes to the ongoing optimization efforts in blockchain technology demonstrating its potential for scalability and efficiency in real-world scenarios.
Optical satellites with infrared, visible light, multi-spectral and hyper-spectral cameras, are effective means to achieve multi-targets surveillance. In the scenarios of ultra-high data rate and high temporal sensiti...
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Linear algebra algorithms, such as the Householder QR decomposition, are pivotal in various applications including signal processing, optimization, and numerical solutions to systems of linear equations. Traditional s...
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ISBN:
(纸本)9783031814037;9783031814044
Linear algebra algorithms, such as the Householder QR decomposition, are pivotal in various applications including signal processing, optimization, and numerical solutions to systems of linear equations. Traditional sequential implementations of the Householder algorithm face significant limitations in terms of performance and scalability when applied to large matrices. To overcome these constraints, this paper explores the parallelization of the Householder QR algorithm on Graphics Processing Units (GPUs) using CUDA, a parallelcomputing platform and programming model developed by NVIDIA. Our method ensures the availability of critical intermediate data, distinguishing it from standard libraries like cuSOLVER, which modify the processing order and often discard important intermediate computations. By leveraging CUDA streams, we achieve enhanced parallelism without compromising the integrity of the algorithm's sequence or the accessibility of intermediate data. Our performance analysis reveals that our implementation achieves efficiency comparable to cuSOLVER, making it a viable option. This study not only presents a novel implementation but also extends the potential for GPU-accelerated linear algebra procedures to benefit a wider range of scientific and engineering applications.
This work analyzes the computing performance of distributed control systems in IEC 61499 on two hardware devices, Dell XPS workstation and Raspi 4B. Based on the test IEC 61499 application, three different system conf...
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The growing popularity of data-intensive applications in cloud computing necessitates a cost-effective approach to harnessing distributed processing capabilities. However, the wide variety of instance types and config...
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