检索结果-内蒙古大学图书馆

Proceedings - 2022 IEEE 36th international parallel and distributed Processing symposium Workshops, IPDPSW 2022 2022年 1235-1236页

作者： Podobas, Artur Drozd, Aleksandr Drozd, Aleksandr Devereux, Barry KTH Royal Institute of Technology Sweden Riken Center for Computational Science Japan University of Tennessee United States Queen's University Belfast United Kingdom

来源：评论

学校读者我要写书评

暂无评论

DistrEdge: Speeding up Convolutional Neural Network Inference on distributed Edge Devices

DistrEdge: Speeding up Convolutional Neural Network Inferenc...

引用

international symposium on parallel and distributed Processing (IPDPS)

作者： Xueyu Hou Yongjie Guan Tao Han Ning Zhang New Jersey Institute of Technology USA Windsor University Canada

As the number of edge devices with computing resources (e.g., embedded GPUs, mobile phones, and laptops) in-creases, recent studies demonstrate that it can be beneficial to col-laboratively run convolutional neural network (CNN) inference on more than one edge device. However, these studies make strong assumptions on the devices' conditions, and their application is far from practical. In this work, we propose a general method, called DistrEdge, to provide CNN inference distribution strategies in environments with multiple IoT edge devices. By addressing heterogeneity in devices, network conditions, and nonlinear characters of CNN computation, DistrEdge is adaptive to a wide range of cases (e.g., with different network conditions, various device types) using deep reinforcement learning technology. We utilize the latest embedded AI computing devices (e.g., NVIDIA Jetson products) to construct cases of heterogeneous devices' types in the experiment. Based on our evaluations, DistrEdge can properly adjust the distribution strategy according to the devices' computing characters and the network conditions. It achieves 1.1 to 3 x speedup compared to state-of-the-art methods.

关键词： distributed processing Adaptive systems Portable computers Distribution strategy Computational modeling Laboratories Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Intelligent judgment of rotating machinery based on multi-scale parallel network and attention mechanism

Intelligent judgment of rotating machinery based on multi-sc...

引用

international symposium on distributed computing and Applications to Business, Engineering and Science (DCABES)

作者： Zhixiang Fan Pengjiang Qian Jiangnan University School of Artificial Intelligence and Computer Science Wuxi China

ISBN: (纸本)9781665454636

The primary problem solved in rotating machinery fault diagnosis is how to effectively extract fault features from the vibration signals with noise. To extract fault features accurately, this study proposes a multi-scale parallel convolutional neural network fault recognition algorithm, which can carry out feature fusion. The above method combines empirical feature extraction (e.g., fast Fourier transform) to enrich feature information, which can effectively implement deep learning. The effectiveness and reliability of the method are verified through example studies on JNU, SEU and PU rolling bearing experimental data sets. The algorithm has the higher classification capability and diagnostic accuracy compared with four common deep learning algorithms.

关键词： Deep learning Vibrations Fault diagnosis Fast Fourier transforms Rolling bearings distributed databases Feature extraction

来源：评论

学校读者我要写书评

暂无评论

[Full] Deep Heuristic for Broadcasting in Arbitrary Networks

[Full] Deep Heuristic for Broadcasting in Arbitrary Networks

引用

international symposium on parallel and distributed computing

作者： Hovhannes A. Harutyunyan Narek Hovhannisyan Rakshit Magithiya Department of Computer Science and Software Engineering Concordia University Montreal Canada

ISBN: (数字)9781665488020

ISBN: (纸本)9781665488037

Broadcasting is an information dissemination problem in a connected graph in which one vertex, called the originator, must distribute a message to all other vertices by placing a series of calls along the edges of the graph. Every time the informed vertices aid the originator in distributing the message. Finding the broadcast time of any vertex in an arbitrary graph is NP-complete. We designed an efficient heuristic, which improves the results of existing heuristics in most cases. Extensive simulations show that our new heuristic outperforms the existing ones for most of the commonly used interconnection networks in some network models generated by network simulator ns-2.

关键词： Multiprocessor interconnection Computational modeling Broadcasting distributed computing

来源：评论

学校读者我要写书评

暂无评论

SW-LCM: A Scalable and Weakly-supervised Land Cover Mapping Method on a New Sunway Supercomputer

SW-LCM: A Scalable and Weakly-supervised Land Cover Mapping ...

引用

international symposium on parallel and distributed Processing (IPDPS)

作者： Yi Zhao Juepeng Zheng Haohuan Fu Wenzhao Wu Jie Gao Mengxuan Chen Jinxiao Zhang Lixian Zhang Runmin Dong Zhenrong Du Sha Liu Xin Liu Shaoqing Zhang Le Yu Tsinghua University Beijing China Xi’an Institute of Surveying and Mapping Joint Research Center for Next-Generation Smart Mapping Tsinghua University Beijing China National Supercomputing Center in Wuxi Wuxi China National Research Center of Parallel Computer Engineering and Technology Beijing China Qingdao Pilot National Laboratory for Marine Science and Technology Qingdao China

High-resolution land cover mapping (LCM) is an important application for studying and understanding the change of the earth surface. While deep learning (DL) methods demonstrate great potential in analyzing satellite images, they largely depend on massive high-quality labels. This paper proposes SW-LCM, a Scalable and Weakly-supervised two-stage Land Cover Mapping method on a new Sunway Supercomputer. Our method consists of a k-means clustering module as a first stage, and an iterative deep learning module as a second stage. With the k-means module providing a good enough starting point (taking inaccurate results as noisy labels), the deep learning module improves the classification results in an iterative way, without any labelling efforts required for processing large scenarios. To achieve efficiency for country-level land cover mapping, we design a customized data partition scheme and an on-the-fly assembly for k-means. Through careful parallelization and optimization, our k-means module scales to 98,304 computing nodes (over 38 million cores), and provides a sustained performance of 437.56 PFLOPS, in a real LCM task of the entire region of China; the iterative updating part scales to 24,576 nodes, with a performance of 11 PFLOPS. We produce a 10-m resolution land cover map of China, with an accuracy of 83.5% (10-class) or 73.2% (25-class), 7% to 8% higher than best existing products, paving ways for finer land surveys to support sustainability-related applications.

关键词：

来源：评论

学校读者我要写书评

暂无评论

An Efficient Vectorization Scheme for Stencil Computation

An Efficient Vectorization Scheme for Stencil Computation

引用

international symposium on parallel and distributed Processing (IPDPS)

作者： Kun Li Liang Yuan Yunquan Zhang Yue Yue Hang Cao State Key Laboratory of Computer Architecture Chinese Academy of Sciences Institute of Computing Technology Beijing School of Computer Science and Technology University of Chinese Academy of Sciences Beijing

Stencil computation is one of the most important kernels in various scientific and engineering applications. A variety of work has focused on vectorization and tiling techniques, aiming at exploiting the in-core data parallelism and data locality respectively. In this paper, the downsides of existing vectorization schemes are analyzed. Briefly, they either incur data alignment conflicts or hurt the data locality when integrated with tiling. Then we propose a novel transpose layout to preserve the data locality for tiling and reduce the data reorganization overhead for vectorization simultaneously. To further improve the data reuse at the register level, a time loop unroll-and-jam strategy is designed to perform multistep stencil computation along the time dimension. Experimental results on the AVX2 and AVX-S12 CPUs show that our approach obtains a competitive performance with the classic vectorization methods (Auto Vectorization and Data Reorganization), state-of-the-art compilers (Pluto and SDSL), and highly-optimized work (DLT and Tessellation).

关键词： Concurrent computing distributed processing Layout parallel processing Registers Computational efficiency Kernel

来源：评论

学校读者我要写书评

暂无评论

IPDPS 2022 PhD Forum

IPDPS 2022 PhD Forum

引用

IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Sanjukta Bhowmick Anne-Cecile Orgerie

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

The IEEE Computer Society has created the Technical Consortium on High Performance computing (TCHPC) to advance and coordinate work in the field of high performance computing networking, storage, and analysis concepts, technologies and applications, and to expand the IEEE's role in this interdisciplinary and pervasive field. The Consortium has launched an Education and Outreach Initiative to coordinate activities, information, and best practices around HPC education/outreach across its member technical committees and the broader community. This includes the coordination of student activities across conferences, and the lead chair for this initiative is also chair the IPDPS 2022 PhD Forum.

关键词： Kernel Task analysis distributed processing Conferences Computational efficiency Spatiotemporal phenomena Schedules

来源：评论

学校读者我要写书评

暂无评论

Proof of Clustering: an efficient and reliable blockchain-based clustering framework

Proof of Clustering: an efficient and reliable blockchain-ba...

引用

IEEE/ACM international symposium on Big Data computing (BDC)

作者： Yamina Khenfouci Yacine Challal Laboratoire des Méthodes de Conception de Systèmes (LMCS) Ecole Nationale Supérieure d’Informatique (ESI) Algiers Algeria College of Computing and IT University of Doha for Science and Technology Doha Qatar

ISBN: (数字)9798350367300

ISBN: (纸本)9798350367317

We propose a novel blockchain-based collaborative clustering framework, designed to enhance the efficiency and reliability of data clustering by decentralizing the clustering process across multiple nodes within the blockchain network. The system incorporates a consensus mechanism called Proof of Clustering to achieve the accuracy and consistency of clustering. This results thereby in ensuring confidence and reliability of the final clusters and prevents any breaches or corruption. To improve the performance of clustering process in the decentralized blockchain environment, data parallelism is employed. Through extensive experimentation and performance evaluation, we demonstrate the effectiveness and efficiency of proof of clustering consensus in various scenarios. Comparative studies with traditional centralized and distributed clustering methods showcase the superiority of our proposed solution by in terms of computational speed and resource utilization. PoC improves the Silhouette Score by 5.05% and reduces the Davies-Bouldin Index by 22.93%, indicating better cluster cohesion and separation in the PoC framework.

关键词： Performance evaluation Data analysis Accuracy Collaboration Machine learning parallel processing Reliability engineering Consensus protocol Indexes Resource management

来源：评论

学校读者我要写书评

暂无评论

parallel Accelerating Ultra-Long Read Alignment by Vertical Partitioning Data

Parallel Accelerating Ultra-Long Read Alignment by Vertical ...

引用

international symposium on parallel Architectures, Algorithms and Programming (PAAP)

作者： Deng Pan Cheng Zhong Danyang Chen Jinxiong Zhang Feng Yang School of Computer Electronics and Information Key Laboratory of Parallel Distributed Computing Technology in Guangxi Universities Guangxi University Nanning Guangxi China School of Computer Electronics and Information Key Laboratory of Parallel Distributed Computing Guangxi University Nanning Guangxi China

ISBN: (纸本)9781665452199

The alignment between sequencing reads and genome is a basic work in biological big data analysis. Each read of the third generation sequencing data is getting longer, and the data size is getting larger. To effectively solve the ultra-long read alignment problem with high requirements for computing and memory capacity, a strategy for vertical partitioning ultra-long reads on hybrid CPU/GPU cluster is proposed, and a heap data structure is used to filter the local aligned results in all computing nodes of the parallel cluster system according to the alignment score to reduce the data transmission size. The methods for early termination and parallel merging-splicing are used to accelerate splicing local aligned results. The local aligned results among all computing nodes are collected and extended to obtain the final alignment results. The experimental results on datasets of simulated and real ultra-long reads show that the proposed parallel alignment algorithm can obtain high alignment accuracy, sensitivity and base-level sensitivity as a whole, and accelerate completing alignment between ultra-long reads and reference genome.

关键词： Sequential analysis Sensitivity Splicing Merging Memory management Genomics Programming

来源：评论

学校读者我要写书评

暂无评论

Scalable Multi-Versioning Ordered Key-Value Stores with Persistent Memory Support

Scalable Multi-Versioning Ordered Key-Value Stores with Pers...

引用

international symposium on parallel and distributed Processing (IPDPS)

作者： Bogdan Nicolae Argonne National Laboratory USA

Ordered key-value stores (or sorted maps/dictionaries) are a fundamental building block in a large variety of both sequential and parallel/distributed algorithms. However, most state-of-art approaches are either based on ephemeral in-memory representations that are difficult to persist and/or not scalable enough under concurrent access (e.g., red-black trees, skip lists), and/or not lightweight enough (e.g. database engines). Furthermore, there is an increasing need to provide versioning support, which is needed in a variety of scenarios: introspection, provenance tracking, revisiting previous intermediate results. To address these challenges, we propose a new lightweight dictionary data structure that simultaneously provides support for multi-versioning, persistency and scalability under concurrent access. We demonstrate its effectiveness through a series of experiments, in which it outperforms several state-of-art approaches, both in terms of vertical and horizontal scalability.

关键词： Concurrent computing distributed processing Dictionaries Databases Scalability Data structures Planning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：