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检索条件"任意字段=10th International Conference on Algorithms and Architectures for Parallel Processing"

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2020 10th international conference on Image processing theory, Tools and Applications, IPTA 2020

2020 10th International Conference on Image Processing Theor...

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10th international conference on Image processing theory, Tools and Applications, IPTA 2020

ISBN: (纸本)9781728187501

the proceedings contain 48 papers. the topics discussed include: localization and correction of corrupted pixel blocks in noisy encrypted images;SegthOR: segmentation of thoracic organs at risk in CT images;4D light field disparity map estimation using Krawtchouk polynomials;fall detection using body geometry in video sequences;CenterlineNet: automatic coronary artery centerline extraction for computed tomographic angiographic images using convolutional neural network architectures;fall detection using body geometry in video sequences;unsupervised segmentation of stents corrupted by artifacts in medical x-ray images;and predicting brain degeneration with a multimodal Siamese neural network.

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Tight Bound of parallel Request Latency for Erasure-Coded Distributed Storage System 20th

Tight Bound of Parallel Request Latency for Erasure-Coded Di...

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20th international conference on algorithms and architectures for parallel processing (ICA3PP)

作者： Zou, Xingshun Li, Wei Shandong Univ Jinan Peoples R China

ISBN: (纸本)9783030602451;9783030602444

With big data analysis and cloud storage improving at an astonishing rate in recent years, more and more parallel computing and data analysis techniques are invented and applied to accessing data from a distributed storage system, which has noticeable differences with accessing data from a separate dedicated storage system. the first and the last processes of a data analysis task in a shared filesystem may have a remarkable gap in latency. the latency of parallel file requests from these processes, however, lacks analytical model especially when erasure code is employed as storage strategy. the main contribution of this work is development of a tight bound of parallel file request latency to give a theoretical upper bound of lantency. Additionally, an exact batch file chunk request analytical model in distributed systems adopting erasure code is studied by the technique of two embedded Markov chains, performance measures such as queue length distribution, waiting time distribution, etc. are derived in closed form. Experimental evaluation verifies accuracy of the theoretical upper bound on latency.

关键词： parallel file requests Distributed storage system Erasure code Queueing theory Latency Embedded Markov chain

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Unsupervised Part Discovery by Unsupervised Disentanglement 42nd

Unsupervised Part Discovery by Unsupervised Disentanglement

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42nd German conference on Pattern Recognition, DAGM GCPR 2020 held in parallel with 25th international Symposium on Vision, Modeling, and Visualization, VMV 2020 and 10th Eurographics Workshop on Visual Computing for Biology and Medicine, VCBM 2020

作者： Braun, Sandro Esser, Patrick Ommer, Björn Heidelberg Collaboratory for Image Processing/IWR Heidelberg University Heidelberg Germany

ISBN: (纸本)9783030712778

We address the problem of discovering part segmentations of articulated objects without supervision. In contrast to keypoints, part segmentations provide information about part localizations on the level of individual pixels. Capturing both locations and semantics, they are an attractive target for supervised learning approaches. However, large annotation costs limit the scalability of supervised algorithms to other object categories than humans. Unsupervised approaches potentially allow to use much more data at a lower cost. Most existing unsupervised approaches focus on learning abstract representations to be refined with supervision into the final representation. Our approach leverages a generative model consisting of two disentangled representations for an object’s shape and appearance and a latent variable for the part segmentation. From a single image, the trained model infers a semantic part segmentation map. In experiments, we compare our approach to previous state-of-the-art approaches and observe significant gains in segmentation accuracy and shape consistency (Code available at https://***/unsupervised-part-segmentation ). Our work demonstrates the feasibility to discover semantic part segmentations without supervision. © 2021, Springer Nature Switzerland AG.

关键词： Semantics

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parallel Belief Propagation Optimized by Coloring on GPUs 20th

Parallel Belief Propagation Optimized by Coloring on GPUs

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20th international conference on algorithms and architectures for parallel processing (ICA3PP)

作者： Hou, Junteng Si, Chengxiang Wang, Shupeng Wu, Guangjun Zhang, Lei Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China Coordinat Ctr China Natl Comp Network Emergency Response Tech Team Beijing Peoples R China

ISBN: (纸本)9783030602451;9783030602444

Belief propagation (BP) is a message passing algorithm that infers over probabilistic graphical models. Its main computational workload, messages update, is suitable for GPU's massively parallel architecture. However, the efficiency of fully parallel BP is low, and traditional algorithms implemented on GPUs occupy amount of computing and memory resources. In this paper, we propose several GPU-friendly BP algorithms optimized by coloring. Color Wave (CW) algorithm performs multi-step coloring on residuals of non-convergent vertices to quickly obtain multiple disjoint partitions and vertices with the largest residuals in each partition, and then updates batches of messages in a fixed order. these operations are all suitable for parallelization and require little additional memory. To save time in each iteration, the Color Extract (CE) algorithm only update messages on edges with the largest residuals among all adjacent edges. the Random Drop (RD) algorithm steadily increases the convergence degree by progressively reducing the messages update ratio of non-convergent edges. the experiments on different GPUs show that our algorithms perform well throughout the calculation process. Compared with state-of-the-art algorithms, CW algorithm converged most of the messages in previous iterations. the convergence degree of CE is higher than all other algorithms in most calculation processes. RD converges fast and always has a high degree of convergence.

关键词： Belief propagation GPUs parallel optimization Coloring operations Progressively reducing update ratio

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A Binary Code Sequence Based Tracking Algorithm in Wireless Sensor Networks 19th

A Binary Code Sequence Based Tracking Algorithm in Wireless ...

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19th international conference on algorithms and architectures for parallel processing (ICA3PP)

作者： Zhang, Yang Ren, Qianqian Pan, Yu Li, Jinbao Heilongjiang Univ Key Lab Database & Parallel Comp Heilongjiang Pro Harbin Peoples R China Heilongjiang Univ Key Lab Elect Engn Coll Heilongjiang Prov Harbin Peoples R China Heilongjiang Univ Sch Comp Sci & Technol Harbin Peoples R China

ISBN: (纸本)9783030389611;9783030389604

this paper proposes a binary code sequence based tracking algorithm in wireless sensor network. the proposed algorithm can release the influence of sensed data on localization results via building the map between target's occurrence region and a binary code sequence. To solve the ambiguity problem existing in occurrence region determination, the paper further gives a Voronoi diagram based location refinement algorithm. the simulation results show the tracking results under difference trajectories.

关键词： Target localization RSSI Voronoi

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Efficient parallel algorithms for betweenness- and closeness-centrality in dynamic graphs 34

Efficient parallel algorithms for betweenness- and closeness...

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34th ACM international conference on Supercomputing, ICS 2020

作者： Shukla, Kshitij Regunta, Sai Charan Tondomker, Sai Harsh Kothapalli, Kishore International Institute of Information Technology Hyderabad India

ISBN: (纸本)9781450379830

Finding the centrality measures of nodes in a graph is a problem of fundamental importance due to various applications from social networks, biological networks, and transportation networks. Given the large size of such graphs, it is natural to use parallelism as a recourse. there have been several studies that show how to compute the various centrality measures of nodes in a graph on parallel architectures, including multi-core systems and GPUs. However, as these graphs evolve and change, it is pertinent to study how to update the centrality measures on changes to the underlying graph. In this paper, we show novel parallel algorithms for updating the betweenness- and closeness-centrality values of nodes in a dynamic graph. Our algorithms process a batch of updates in parallel by extending the approach of handling a single update for betweenness-and closeness-centrality by Jamour et al. [16] and Sariyüce et al. [27], respectively. Besides, our algorithms incorporate mechanisms to exploit the structural properties of graphs for enhanced performance. We implement our algorithms on two parallel architectures: an Intel 24-core CPU and an Nvidia Tesla V100 GPU. To the best of our knowledge, we are the first to show GPU algorithms for the above two problems. We conduct detailed experiments to study the impact of various parameters associated with our algorithms and their implementation. Our results on a collection of real-world graphs indicate that our algorithms achieve a significant speedup over corresponding state-of-the-art algorithms. © 2020 ACM.

关键词： Graphics processing unit

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CC-MOEA: A parallel Multi-objective Evolutionary Algorithm for Recommendation Systems 20th

CC-MOEA: A Parallel Multi-objective Evolutionary Algorithm f...

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20th international conference on algorithms and architectures for parallel processing (ICA3PP)

作者： Wei, Guoshuai Wu, Quanwang Chongqing Univ Coll Comp Sci Chongqing Peoples R China

ISBN: (纸本)9783030602390;9783030602383

Recommender systems have nowadays been widely used in a variety of applications such as Amazon and Ebay. Traditional recommendation techniques mainly focus on recommendation accuracy only. In reality, other metrics such as diversity and novelty also play a key role for modern recommendation systems. Although some works based on multi-objective evolutionary algorithm have been proposed for multi-objective recommendation, they are usually very time-consuming because of the large data size of the RSs and the long-term evolution iterations and hence it greatly limits their application in practice. To address these shortcomings, this paper first designs a multi-objective recommendation system, taking into account diversity and novelty as well as accuracy. then, a novel parallel multi-objective evolutionary algorithm called CC-MOEA is proposed to optimize these conflicting metrics. CC-MOEA is devised grounded on NSGA-II and a cooperative coevolutionary island model, and a parallel global non-dominated selection method is introduced to reduce the runtime of finding the global optimal individuals. Furthermore, a new initialization method and a crossover operator are specifically designed. the experimental results reveal that CC-MOEA outperforms some state-of-the-art algorithms in terms of hypervolume and runtime.

关键词： Accuracy Cooperative coevolutionary Multi-objective evolutionary algorithm parallel Recommendation system

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NPV: Fast Network Policy Verification for Cloud-Native Networking

NPV: Fast Network Policy Verification for Cloud-Native Netwo...

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international conference on Distributed Computing Systems

作者： Shunbin Dong Yumin Xie Jin Zhao Kun Qiu School of Computer Science Fudan University Shanghai China Shanghai Key Laboratory of Intelligent Information Processing Shanghai China

ISBN: (数字)9798350386059

ISBN: (纸本)9798350386066

Network policy plays a crucial role in cloud-native networking, especially in multi-tenant scenarios. It provides precise control over connectivity by specifying source and destination endpoints, traffic types, and other criteria to allow or deny traffic. However, manual configuration of these policies introduces the risk of errors, leading to isolation violations or network service unavailability. therefore, network policy verification is essential for maintaining security and quality of service in cloud-native networking. Currently, a naïve approach involves individually checking each policy within the cluster, which can take over 100s for verification in a cluster size of over 100k. Existing verification frameworks, like Kano and Verikube, improve performance by leveraging pre-filtering and Satisfiability Modulo theories (SMT) solvers, achieving a 3.12x to 12.99x performance boost over the naïve baseline. However, as network policy changes rapidly within 100ms in real cloud-native networks, both frameworks need over 10s to perform verification for cluster sizes over 100k, which is far from satisfying. To overcome these issues, we propose and implement a novel network policy verification framework NPV, which utilizes the policy-label pre-filter process with bitwise compression. We further enhance the policy verification algorithm with a policy-namespace divide-and-conquer strategy to improve the data-level parallelism. We implement NPV on commodity servers and evaluate its performance using real network policy datasets. Our experiments indicate that, compared with the state-of-the-art methods, NPV can achieve up to 139.00x to 651.06x improvement in verification time compared to Kano and Verikube, with 65% less memory usage.

关键词： Memory management Clustering algorithms Quality of service Manuals parallel processing Servers Security

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Optimizing B⁺-Tree Searches on Coupled CPU-GPU architectures 20th

Optimizing B<SUP>+</SUP>-Tree Searches on Coupled CPU-GPU Ar...

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20th international conference on algorithms and architectures for parallel processing (ICA3PP)

作者： Huang, Han Luan, Hua Beijing Normal Univ Beijing Peoples R China

ISBN: (纸本)9783030602451;9783030602444

the B+-tree is an important index in the fields of data warehousing and database management systems. With the development of new hardware technologies, the B+-tree needs to be revisited to fully take advantage of hardware resources. In this paper, we focus on optimization techniques to increase the searching performance of B+-trees on the coupled CPU-GPU architecture. First, we propose a hierarchical searching approach on the single coupled GPU to efficiently deal with leaf nodes of B+-trees. It adopts a flexible strategy to determine the number of work items in a work group to search one key in order to reduce irregular memory accesses and divergent branches in the work group. Second, we present a co-processing pipeline method on the coupled architecture. the CPU and the integrated GPU process the sorting and searching tasks simultaneously to hide sorting and partial searching latencies. A distribution model is designed to support the workload balance strategy based on real-time performance. Our performance study shows that the hierarchical searching scheme provides an improvement up to 36% on the GPU compared to the baseline algorithm with fixed number of work items and the co-processing pipeline method further increases the throughput by a factor of 1.8. To the best of our knowledge, this paper is the first study to consider both the CPU and the coupled GPU to optimize B+-trees searches.

关键词： B+-trees the coupled architecture Integrated GPU Co-processing

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Voltage Island-Aware Energy-Efficient Scheduling of parallel Streaming Tasks on Many-Core CPUs 28

Voltage Island-Aware Energy-Efficient Scheduling of Parallel...

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28th Euromicro international conference on parallel, Distributed and Network-Based processing (PDP)

作者： Melot, Nicolas Kessler, Christoph Keller, Joerg Linkoping Univ S-58183 Linkoping Sweden Fernuniv D-58084 Hagen Germany

ISBN: (纸本)9781728165820

For multi- and many-core CPUs, dynamic voltage and frequency scaling (DVPS) for individual cores provides an effective way for energy-efficient execution of applications. However, this requires additional hardware within the chip that regulates voltage and frequency for each hardware sub-component that can be scaled separately. Because of the significant cost of this control hardware, it is often not realistic to provide such a regulator for each individual core. Instead, chip manufacturers group cores into islands consisting of multiple cores with a common regulator, and energy optimizing solutions must lake this constraint into account when assigning frequencies 10 jobs and cores. Crown Scheduling is a technique for the combined resource allocation, mapping and discrete DVFS-level selection for actor networks consisting of moldable parallel streaming tasks for energy efficient execution given a throughput constraint. We extend crown scheduling to compute correct schedules also in the presence of DVFS islands constraints. We find that, for most task sets, the crown scheduler computes almost equally good schedules for target architectures with and without island constraints.

关键词： static scheduling energy-efficient execution optimization algorithm

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