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14 篇 会议

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21st international conference on algorithms and architectures for parallel processing, ica3pp 2021

21st International Conference on Algorithms and Architecture...

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21st international conference on algorithms and architectures for parallel processing, ica3pp 2021

ISBN: (纸本)9783030953874

The proceedings contain 145 papers. The special focus in this conference is on algorithms and architectures for parallel processing. The topics include: TSAEns: Ensemble Learning for KPI Anomaly Detection;towards Transferable Adversarial Examples Using Meta Learning;temporal Convolution Network Based on Attention for Intelligent Anomaly Detection of Wind Turbine Blades;Error Serial Episodes Discovery from Mobile Payment Log in Distributed ETC;parallel Cache Prefetching for LSM-Tree Based store: From Algorithm to Evaluation;A Hybrid TLBO-TS Algorithm Based Mobile Service Selection for Composite Services;UPM-DMA: An Efficient Userspace DMA-Pinned Memory Management strategy for NVMe SSDs;AHOA: Adaptively Hybrid Optimization Algorithm for Flexible Job-shop Scheduling Problem;trace-Navi: A High-Accuracy Indoor Navigation System Based on Real-Time Activity Recognition and Discrete Trajectory Calibration;BFR-RetinaNet: An Improved RetinaNet Model for Vehicle Detection in Aerial Images;iterative Filling Incomplete Fingerprint Map Based on Multi-directional Signal Propagation in Large-Scale Scene;dynamic Adjustment Policy of Search Driver Matching Distance via Markov Decision Process;a Multi-precision Quantized Super-Resolution Model Framework;An Optimized GPU Implementation of Weakly-Compressible SPH Using CUDA-Based strategies;a Heterogeneous Multi-core Network-on-Chip Mapping Optimization Algorithm;a Novel 3D Intelligent Cluster Method for Malicious Traffic Fine-Grained Classification;Predicting students’ Academic Performance Based on Improved PSO-Xgboost: A Campus Behavior Perspective;motion-Sequence Authentication System: Guard for Smart Phones;joint Optimization Scheme of Multi-service Replication and Request Offloading in Mobile Edge Computing;Flying MEC: Online Task Offloading, Trajectory Planning and Charging Scheduling for UAV-Assisted MEC;learning Knowledge Graph Embeddings by Multi-Attention Mechanism for Link Prediction;multiple Workflows Offloading Based on Dee

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21st international conference on algorithms and architectures for parallel processing, ica3pp 2021

21st International Conference on Algorithms and Architecture...

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21st international conference on algorithms and architectures for parallel processing, ica3pp 2021

ISBN: (纸本)9783030953836

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21st international conference on algorithms and architectures for parallel processing, ica3pp 2021

21st International Conference on Algorithms and Architecture...

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21st international conference on algorithms and architectures for parallel processing, ica3pp 2021

ISBN: (纸本)9783030953904

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parallel Cache Prefetching for LSM-Tree Based store: From Algorithm to Evaluation 1

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

作者： Zhang, Shuo Xu, Guangping Jia, YuLei Xue, Yanbing Zheng, Wenguang Tianjin Key Lab Intelligence Comp & New Software Tianjin 300384 Peoples R China Tianjin Univ Technol Comp & Commun Engn Sch Tianjin 300384 Peoples R China

ISBN: (数字)9783030953843

ISBN: (纸本)9783030953843;9783030953836

The Log-structured Merge-Tree has efficient writing performance and performs well in big data scenarios. An LSM-tree transforms random writes into batch sequential writes through the design of a multilayer storage structure. However, as the core operation, the compaction inevitably results in degrading periodically in the read performance. Regular but irregular data compaction operations make the cache challenging to track the access information of data blocks. This work studies how to address the cache invalidation problem. We propose a two-phase parallel prefetching approach, which can effectively improve the cache invalidation when the compaction occurs. Our experimental results show our method can effectively improve read performance.

关键词： LSM-tree Key-value store Caches Minor cache Cache invalidation

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Evolving Deep parallel Neural Networks for Multi-Task Learning 21st

Evolving Deep Parallel Neural Networks for Multi-Task Learni...

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

作者： Wu, Jie Sun, Yanan Sichuan Univ Coll Comp Sci Chengdu Peoples R China

ISBN: (纸本)9783030953881;9783030953874

Multi-Task Learning (MTL) can perform multiple tasks simultaneously with a single model, and can achieve competitive performance for each individual task. In recent years, the Deep Neural Networks (DNNs) based models have demonstrated their advantages in the field of MTL. Yet, most of such models are commonly manually designed with expertise through performing trial and error experiments, which is prohibitively ineffective. In view of this, we design a method based on evolutionary algorithm in this paper, named EVO-MTL, to automate the parallel DNN architectures for effectively addressing the MTL problems. Specifically, our main idea is to evolve the connections between the parallel task-specific backbone networks, and then leverage the useful information contained in the tasks by fusing the task-specific features. In order to verify the effectiveness of the proposed algorithm, the experiments are designed to compare with recent MTL methods including the manually designed and automatically designed. The experimental results demonstrate that the proposed algorithm can outperform the carefully hand-designed methods. In addition, the proposed algorithm can also attain promising competitive performance in balancing multi-task conflicts compared with the DNN architecture searched by state-of-the-art automated MTL method.

关键词： Evolutionary computation Multi-Task Learning Neural architecture search Computer vision Deep Neural Networks

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Evaluating the parallel Execution Schemes of Smart Contract Transactions in Different Blockchains: An Empirical study 21st

Evaluating the Parallel Execution Schemes of Smart Contract ...

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

作者： Shi, Jianfeng Li, Chengzhi Wu, Heng Gao, Heran Jin, Songchang Huang, Tao Zhang, Wenbo Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Inst Software Beijing Peoples R China Chinese Acad Sci Inst Software State Key Lab Comp Sci Beijing Peoples R China Def Innovat Inst Beijing Peoples R China Tianjin Artificial Intelligence Innovat Ctr Tianjin Peoples R China

ISBN: (纸本)9783030953911;9783030953904

In order to increase throughput, more and more blockchains begin to provide the ability to execute smart contract transactions in parallel. However, there is currently no research work on evaluating parallel execution schemes in different blockchains, which makes it difficult for developers to find a blockchain technology suitable for their application scenarios. In this paper, we firstly summarize existing parallel execution schemes of smart contract transactions. Then, based on their characteristics, we propose a comprehensive evaluation framework for parallel execution schemes of smart contract transactions and implement a benchmark tool, which can compare different parallel execution schemes and discover their limitations. Finally, we utilize the evaluation framework and the tool to evaluate several excellent blockchains in China and obtain some useful findings.

关键词： Blockchain Smart contract parallel execution Benchmark Empirical study

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The Case for Disjoint Job Mapping on High-Radix Networked parallel Computers 21st

The Case for Disjoint Job Mapping on High-Radix Networked Pa...

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

作者： Hu, Yao Koibuchi, Michihiro Natl Inst Informat Informat Syst Architecture Sci Res Div Chiyoda Ku 2-1-2 Hitotsubashi Tokyo 10184030 Japan

ISBN: (纸本)9783030953881;9783030953874

Onboard optics and co-packaged optics (CPO) will enable to build an ultra high-radix switching ASIC. Ultra high-radix interconnection networks, which take a low diameter, lead to a marginal impact of intra-job network topology on the performance of job mapping, i.e., placement of message passing interface (MPI) ranks onto compute nodes. In this context, we investigate the impact of job mapping algorithms on job scheduling performance, which have different trade-offs between the resource utilization and the constraint of intra-job network topology. Our simulation results show that a simple disjoint job mapping policy (e.g., a topology-oblivious job mapping algorithm) surprisingly outperforms a complicated joint one (e.g., a topology-aware job mapping algorithm) for its substantially better job scheduling performance at the cost of a larger network diameter, especially when dealing with an exceedingly large workload on high-radix networked parallel computers.

关键词： Interconnection network parallel computer High-radix switch Job mapping

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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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SGP: A parallel Computing Framework for Supporting Distributed structural Graph Clustering 21st

SGP: A Parallel Computing Framework for Supporting Distribut...

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

作者： Xia, Xiufeng Fang, Peng An, Yunzhe Zhu, Rui Zong, Chuanyu Shenyang Aerosp Univ Sch Comp Sci Shenyang Peoples R China

ISBN: (纸本)9783030953911;9783030953904

structural graph clustering is an important problem in the domain of graph data management. Given a large graph G, structural graph clustering is to assign vertices to clusters where vertices in the same cluster are densely connected to each other and vertices in different clusters are loosely connected to each other. Due to its importance, many algorithms have been proposed to study this problem. However, no effort focuses on the distributed graph environment. In this paper, we propose a parallel computing framework named SGP (short for statistics-based Graph Partition) to support large graph clustering under distributed environment. We first use historical clustering information to partition graph into a group of clusters. Based on the partition result, we can properly assign vertexes to different nodes based on connection relationship among vertex. When a clustering request is submitted, we can use properties leading by the partition for efficiently clustering. Finally, we conduct extensive performance studies on large real and synthetic graphs, which demonstrate that our new approach could efficiently support large graph clustering under distributed environment.

关键词： structural graph Cluster Distributed Core-vertex

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EdgeSP: Scalable Multi-device parallel DNN Inference on Heterogeneous Edge Clusters 21st

EdgeSP: Scalable Multi-device Parallel DNN Inference on Hete...

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

作者： Gao, Zhipeng Sun, Shan Zhang, Yinghan Mo, Zijia Zhao, Chen Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China China Int Engn Consulting Corp Ltd Beijing Peoples R China

ISBN: (纸本)9783030953881;9783030953874

Edge computing has emerged as a promising line of research for processing large-scale data and providing low-latency services. Unfortunately, deploying deep neural networks (DNNs) on resource-limited edge devices presents unacceptable latency, hindering artificial intelligence from empowering edge devices. Prior solutions attempted to address this issue by offloading workload to the remote cloud. However, the cloud-assisted approach ignores that devices in the edge environment tend to exist as clusters. In this paper, we propose EdgeSP, a scalable multi-device parallel DNN inference framework that maximizes resource utilization of heterogeneous edge device clusters. We design a multiple fused-layer blocks parallelization strategy to reduce inter-device communication during parallel inference. Further, we add early exit branches to DNNs, empowering the device to trade-off latency and accuracy for a variety of sophisticated tasks. Experimental results show that EdgeSP enables inference latency acceleration of 2.3 x -3.7x for DNN inference tasks of various scales and outperforms the existing naive parallel inference method. Additionally, EdgeSP can provide high accuracy inference services under various latency requirements.

关键词： Edge computing Edge intelligence parallel inference Deep neural networks Early-exit Internet of Things

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