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检索条件"任意字段=22nd International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2022"

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22nd international conference on algorithms and architectures for parallel processing, ica3pp 2022

22nd International Conference on Algorithms and Architecture...

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22nd international conference on algorithms and architectures for parallel processing, ica3pp 2022

ISBN: (纸本)9783031226762

The proceedings contain 43 papers. The special focus in this conference is on algorithms and architectures for parallel processing. The topics include: CRFs for Digital Signature and NIZK Proof System in Web Services;SPAC: Scalable Pattern Approximate Counting in Graph Mining;haica: A High Performance Computing & Artificial Intelligence Fused Computing Architecture;AOA: Adaptive Overclocking Algorithm on CPU-GPU Heterogeneous Platforms;GEM: Execution-Aware Cache Management for Graph Analytics;EnergyCIDN: Enhanced Energy-Aware Challenge-Based Collaborative Intrusion Detection in Internet of Things;Federated Learning-Based Intrusion Detection on Non-IID Data;long-Term Fairness Scheduler for Pay-as-You-Use Cache Sharing Systems;MatGraph: An Energy-Efficient and Flexible CGRA Engine for Matrix-Based Graph Analytics;pCOVID: A Privacy-Preserving COVID-19 Inference Framework;D-IOCost: Dynamic Cost-Aware Fair Queueing for Better I/O Proportionality and Performance;automated Binary Analysis: A Survey;LTNoT: Realizing the Trade-Offs Between Latency and Throughput in NVMe over TCP;AS-cast: Lock Down the Traffic of Decentralized Content Indexing at the Edge;Heterogeneous Graph Based Long- And Short-Term Preference Learning Model for Next POI Recommendation;SMTWM: Secure Multiple Types Wildcard Pattern Matching Protocol from Oblivious Transfer;a Label Flipping Attack on Machine Learning Model and Its Defense Mechanism;astute Approach to Handling Memory Layouts of Regular Data Structures;SparG: A Sparse GEMM Accelerator for Deep Learning Applications;An Efficient Transformer Inference Engine on DSP;hierarchical Reinforcement Learning-Based Mobility-Aware Content Caching and Delivery Policy for Vehicle Networks;GCNPart: Interference-Aware Resource Partitioning Framework with Graph Convolutional Neural Networks and Deep Reinforcement Learning;PipeFB: An Optimized Pipeline parallelism Scheme to Reduce the Peak Memory Usage;operator Placement for IoT Data Streaming Applications in Ed

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Hybridization of One- and Two-Point Bandits Convex Optimization in Non-stationary Environments 24th

Hybridization of One- and Two-Point Bandits Convex Optimiz...

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24th international conference on algorithms and architectures for parallel processing, ica3pp 2024

作者： Zeng, Gailun Guo, Jianxiong Advanced Institute of Natural Sciences Beijing Normal University Zhuhai China Hong Kong Baptist University Hong Kong Guangdong Key Lab of AI and Multi-modal Data Processing Department of Computer Science BNU-HKBU United International College Zhuhai China

ISBN: (纸本)9789819615278

Bandit Convex Optimization (BCO) is an imperative analysis framework when dealing with sequential decision-making problems. Considering to balance the computational cost and bounds of regrets, in this paper, we propose a hybridized algorithm of one- and two-point bandit convex models in non-stationary environments and use a more general performance measure dynamic regret, which records the cumulative difference between function loss and a feasible comparator sequence during the time horizon T. The path length of a comparator sequence PT reveals the non-stationarity of environments. Our proposed algorithm builds an upper bound of dynamic regret O((1+PT)1/2[β(λT)1/2+((1-λ)T)3/4]), where the parameter λ can dynamically adjust the bound guarantee to balance the computational cost in real applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Comparators (optical)

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Dynamic Offloading Control for Waste Sorting Based on Deep Q-Network 24th

Dynamic Offloading Control for Waste Sorting Based on Deep...

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24th international conference on algorithms and architectures for parallel processing, ica3pp 2024

作者： Wang, Jing Wang, Xiaoyang Guo, Jianxiong Tang, Zhiqing Ding, Xingjian Wang, Tian Advanced Institute of Natural Sciences Beijing Normal University Zhuhai China Guangdong Key Lab of AI and Multi-modal Data Processing Department of Computer Science BNU-HKBU United International College Zhuhai China Faculty of Information Technology Beijing University of Technology Beijing China

ISBN: (纸本)9789819615278

With the increasing concern for environmental protection and resource optimization, efficient waste sorting has become a serious challenge today. In this paper, we propose a new offloading control problem that aims to solve waste sorting in wireless bin communication networks. Due to limited computational power, bins belonging to embedded devices rely on simple classification models with varying accuracy. In this scenario, consider a network of intelligent bins, each acting as an independent agent capable of deciding to offload an image to the edge server with a more accurate but resource-intensive model when the local classification is deemed inaccurate. Thus, Our goal is to find a lightweight online offloading policy that can achieve the best possible sorting accuracy while balancing transmission traffic. The method utilizes a Deep Q-network algorithm that enables each intelligent bin to make image-processing decisions autonomously. In the experiment, we validate the effectiveness of improving the performance of waste classification compared with existing flow control methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Distributed Resource Allocation Intelligent Waste Management Waste Sorting Wireless Communication Network

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An Efficient Computation Offloading Strategy in Wireless Powered Mobile-Edge Computing Networks 21st

An Efficient Computation Offloading Strategy in Wireless Pow...

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21st international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Zhou, Xiaobao Hu, Jianqiang Liang, Mingfeng Liu, Yang Xiamen Univ Technol Sch Comp & Informat Engn Xiamen 361024 Peoples R China

ISBN: (纸本)9783030953881;9783030953874

The emergence of mobile edge computing (MEC) has improved the data processing capabilities of devices with limited computing resources. However, some tasks that require higher latency and energy consumption are still facing huge challenges. In this paper, for the time-varying wireless channel conditions, we proposed an effective method to perform offloading calculations on the computing tasks of wireless devices, that is, to distribute the tasks to the local of offload to the edge server under the premise of satisfying time delay and energy consumption. Based on this, we adopt the parallel calculation model of Deep Reinforcement Learning Optimal Stopping Theory (DRLOST), which is composed of two parts: offloading decision generation and deep reinforcement learning. The model uses a parallel deep neural network (DNN) to generate offloading decisions, and stores the generated offloading decisions in the memory according to the optimal stopping theory model parameters to further train the model. The simulation results show that the proposed algorithm can minimize delay time, and can respond quickly to tasks even in a fast-fading environment.

关键词： Mobile edge computing Offloading decision parallel computing Optimal stopping theory

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gGMED: Towards GPU Accelerated Geometric Modeling Evaluation and Derivative Processes 23rd

gGMED: Towards GPU Accelerated Geometric Modeling Evaluation...

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23rd international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Xuan, Zhibo Yang, Hailong Wang, Pengbo Sun, Xin Hao, Jiwei Duan, Shenglin Shi, Yongfeng Luan, Zhongzhi Qian, Depei Beihang Univ Sch Comp Sci & Engn Beijing Peoples R China Avic Digital Co Ltd Beijing Peoples R China

ISBN: (纸本)9789819707973;9789819707980

Geometric modeling algorithms serve as the fundamental computation of CAD/CAM software in the field of computer graphics. The evaluation and derivative processes, being an essential component of geometric modeling algorithms, significantly impact their overall performance. However, when dealing with scenarios involving high-precision models or large-scale datasets, the lack of parallel acceleration for geometric modeling computation results in prolonged computation time and low computation efficiency, hindering the satisfactory experience of user interaction. Although the massive parallelism of GPUs has been proved with successful performance acceleration in various application fields, it has not been effectively utilized for accelerating geometric modeling algorithms. In this paper, we propose gGMED, a GPU-based approach specifically designed for accelerating the evaluation and derivative processes in geometric modeling. To leverage the massive parallel capability of GPU, our approach provides several optimizations such as data reuse, bank conflict avoidance, and pipeline execution, for effectively improving the performance of evaluation and derivative processes. The experiment results on representative GPUs and various NURBS models demonstrate that our approach can achieve up to 10.18x and 34.56x performance speedup in end-to-end process and kernel computation respectively, compared to the state-of-the-art geometric modeling libraries.

关键词： Geometric modeling algorithms Evaluation Derivative parallel optimization GPU

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algorithms and architectures for parallel processing 1

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丛书名： Lecture Notes in Computer Science

1000年

作者： Weizhi Meng Rongxing Lu Geyong Min Jaideep Vaidya

ISBN: (数字)9783031226779

ISBN: (纸本)9783031226762

This book constitutes the refereed proceedings of the 22;international conference on algorithms and architectures for parallel processing, ica3pp 2022, which was held in October 2022. Due to COVID-19 pandemic the conference was held virtually.;The 33 full papers and 10 short papers, presented were carefully reviewed and selected from 91 submissions.;The papers cover many dimensions of parallel algorithms and architectures, encompassing fundamental theoretical approaches, practical experimental projects, and commercial components and systems

关键词： Algorithm Analysis and Problem Complexity Information Systems and Communication Service Computer System Implementation Special Purpose and Application-Based Systems Computer Communication Networks

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SW-TRRM: parallel Optimization Research of the Random Ray Method Based on Sunway Bluelight II Supercomputer 23rd

SW-TRRM: Parallel Optimization Research of the Random Ray Me...

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23rd international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Ren, Zenghui Liu, Tao Liu, Zhaoyuan Guo, Ying Pan, Jingshan Zhao, Dawei Wu, Xiaoming Yang, Meihong Qilu Univ Technol Shandong Comp Sci Ctr Natl Supercomp Ctr Jinan Shandong Acad Sci Jinan Peoples R China

ISBN: (纸本)9789819708079;9789819708086

The Random Ray Method (TRRM) is a new approach to solving partial differential equations (PDEs) based on the method of characteristics (MOC). It employs stochastic rather than deterministic discretization of characteristic tracks and can be used for the numerical simulation of nuclear reactors. In this paper, we propose SW-TRRM, a parallel optimization program for TRRM based on the Sunway Bluelight II Supercomputer for the first time. We present a two-level parallelization scheme that consists of thread-level and process-level optimization. At the thread-level, we introduce three schemes for speeding up within a single core group, including direct parallelization, parallelization by energy groups, and loop structure optimization. At the process-level, we implement task parallelization among multiple processes using domain replication. Moreover, we devise an algorithm to optimize the MPI collective communication across super-nodes. Experimental results show that SW-TRRM achieves a 17.40x speedup within a single core group compared to the original TRRM program. When scaled up to 2,048 processes and 133,120 cores, SW-TRRM maintains good strong and weak scalability.

关键词： High performance computing parallel optimization Sunway supercomputer The random ray method

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Optimizing Yinyang K-Means Algorithm on ARMv8 Many-Core CPUs 22nd

Optimizing Yinyang K-Means Algorithm on ARMv8 Many-Core CPU...

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22nd international conference on algorithms and architectures for parallel processing, ica3pp 2022

作者： Zhou, Tianyang Wang, Qinglin Yin, Shangfei Hao, Ruochen Liu, Jie Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China School of Computer Science National University of Defense Technology Changsha410073 China

ISBN: (纸本)9783031226762

K-Means algorithm is one of the most common clustering algorithms widely applied in various data analysis applications. Yinyang K-Means algorithm is a popular enhanced K-Means algorithm that avoids most unnecessary calculations using triangle inequality. However, Yinyang K-Means algorithm is time-consuming when the problem size is large. Due to the influence of performance and energy-efficiency, ARM CPUs have appeared in high performance computing. Therefore, it is very interesting to accelerate Yinyang K-Means algorithm on ARM CPUs. In this paper, we propose an efficient parallel implementation of Yinyang K-Means algorithm on ARMv8 many-core CPUs by means of vectorization, NUMA affinity memory optimization and data layout optimization. The experiment on two ARMv8 many-core CPUs has shown that our implementation can achieve up to 5.6 times faster than the open-source multi-threaded one of Yinyang K-Means algorithm. To the best of our knowledge, this is the first work that studies the optimization of Yinyang K-Means algorithms on ARMv8 CPUs. © 2023, Springer Nature Switzerland AG.

关键词： K-means clustering

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Key-Based Transaction Reordering: An Optimized Approach for Concurrency Control in Hyperledger Fabric 1

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23rd international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Ma, Haoliang Shi, Peichang Fu, Xiang Yi, Guodong Natl Univ Def Technol Coll Comp Sci Natl Key Lab Parallel & Distributed Comp Changsha 410073 Peoples R China Natl Univ Def Technol Coll Comp Sci Key Lab Software Engn Complex Syst Changsha 410073 Peoples R China Xiangjiang Lab Changsha 410073 Peoples R China

ISBN: (数字)9789819708628

ISBN: (纸本)9789819708611;9789819708628

As blockchain technology garners increased adoption, permissioned blockchains like Hyperledger Fabric emerge as a popular blockchain system for developing scalable decentralized applications. Nonetheless, parallel execution in Fabric leads to concurrent conflicting transactions attempting to read and write the same key in the ledger simultaneously. Such conflicts necessitate the abortion of transactions, thereby impacting performance. The mainstream solution involves constructing a conflict graph to reorder the transactions, thereby reducing the abort rate. However, it experiences considerable overhead during scenarios with a large volume of transactions or high data contention due to capture dependencies between each transaction. Therefore, one critical problem is how to efficiently order conflicting transactions during the ordering phase. In this paper, we introduce an optimized reordering algorithm designed for efficient concurrency control. Initially, we leverage key dependency instead of transaction dependency to build a conflict graph that considers read/write units as vertices and intra-transaction dependency as edges. Subsequently, a key sorting algorithm generates a serializable transaction order for validation. Our empirical results indicate that the proposed key-based reordering method diminishes transaction latency by 36.3% and considerably reduces system memory costs while maintaining a low abort rate compared to benchmark methods.

关键词： Hyperledger Fabric Reordering Algorithm Concurrency Control Transaction Conflicts

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