检索结果-内蒙古大学图书馆

International Conference on parallel and distributed Systems (ICPADS)

作者： Yutian Lei Qingyong Deng Saiqin Long Shaohui Liu Sangyoon Oh The Key Laboratory of Hunan Province for Internet of Things and Information Security The School of Computer Science Science and Technology on Parallel and Distributed Processing Laboratory Xiangtan University Xiangtan China The Artificial Intelligence Center PengCheng Laboratory shenzhen China Ajou University Suwon-si Republic of Korea

The following topics are dealt with: cloud computing; learning (artificial intelligence); resource allocation; security of data; mobile computing; neural nets; optimisation; parallel processing; scheduling; and storag... 详细信息

ISBN: (数字)9781728190747

ISBN: (纸本)9781728183824

关键词： Deep learning Schedules Multicore processing Logic gates Throughput Acceleration Field programmable gate arrays

来源：评论

学校读者我要写书评

暂无评论

Audio Tagging by Cross Filtering Noisy Labels

arXiv

引用

arXiv 2020年

作者： Zhu, Boqing Xu, Kele Kong, Qiuqiang Wang, Huaimin Peng, Yuxing Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology Changsha410073 China Centre for Vision Speech and Signal Processing University of Surrey GuildfordGU2 7XH United Kingdom

—High quality labeled datasets have allowed deep learning to achieve impressive results on many sound analysis tasks. Yet, it is labor-intensive to accurately annotate large amount of audio data, and the dataset may contain noisy labels in the practical settings. Meanwhile, the deep neural networks are susceptive to those incorrect labeled data because of their outstanding memorization ability. In this paper, we present a novel framework, named CrossFilter, to combat the noisy labels problem for audio tagging. Multiple representations (such as, Logmel and MFCC) are used as the input of our framework for providing more complementary information of the audio. Then, though the cooperation and interaction of two neural networks, we divide the dataset into curated and noisy subsets by incrementally pick out the possibly correctly labeled data from the noisy data. Moreover, our approach leverages the multitask learning on curated and noisy subsets with different loss function to fully utilize the entire dataset. The noisy-robust loss function is employed to alleviate the adverse effects of incorrect labels. On both the audio tagging datasets FSDKaggle2018 and FSDKaggle2019, empirical results demonstrate the performance improvement compared with other competing approaches. On FSDKaggle2018 dataset, our method achieves state-of-the-art performance and even surpasses the ensemble models. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Topology-Aware Deployment of Scientific Applications in Cloud Computing

Topology-Aware Deployment of Scientific Applications in Clou...

引用

IEEE International Conference on Cloud Computing, CLOUD

作者： Pei Fan Zhenbang Chen Ji Wang Zibin Zheng Michael R. Lyu National Laboratory for Parallel & Distributed Processing National University of Defense Technology Changsha P.R.China Dept. of Computer Science & Engineering The Chinese University of Hong Kong Hong Kong China

Nowadays, more and more scientific applications are moving to cloud computing. The optimal deployment of scientific applications is critical for providing good services to users. Scientific applications are usually topology-aware applications. Therefore, considering the topology of a scientific application during the development will benefit the performance of the application. However, it is challenging to automatically discover and make use of the communication pattern of a scientific application while deploying the application on cloud. To attack this challenge, in this paper, we propose a framework to discover the communication topology of a scientific application by pre-execution and multi-scale graph clustering, based on which the deployment can be optimized. Comprehensive experiments are conducted by employing a well-known MPI benchmark and comparing the performance of our method with those of other methods. The experimental results show the effectiveness of our topology-aware deployment method.

关键词： Topology Clustering algorithms Benchmark testing Cloud computing Partitioning algorithms Clustering methods Throughput

来源：评论

学校读者我要写书评

暂无评论

Overlapping community detection via link partition of asymmetric weighted graph

Overlapping community detection via link partition of asymme...

引用

International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)

作者： Wenju Zhang Naiyang Guan Xuhui Huang Zhigang Luo Jianwu Li Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha Hunan P.R. China 410073 National University of Defense Technology Changsha Hunan CN Beijing Institute of Technology Beijing Beijing CN

Link partition clusters edges of a complex network to discover its overlapping communities. Due to Its effectiveness, link partition has attracted much attentions from the network science community. However, since link partition assigns each edge of a network to unique community, it cannot detect the disjoint communities. To overcome this deficiency, this paper proposes a link partition on asymmetric weighted graph (LPAWG) method for detecting overlapping communities. Particularly, LPAWG divides each edge into two parts to distinguish the roles of connected nodes. This strategy biases edges to a specific node and helps assigning each node to its affiliated community. Since LPAWG introduces more edges than those in the original network, it cannot efficiently detect communities from some networks with relative large amount of edges. We therefore aggregate the line graph of LPAWG to shrink its scale. Experimental results of community detection on both synthetic datasets and the realworld networks show the effectiveness of LPAWG comparing with the representative methods.

关键词： Communities Image edge detection Partitioning algorithms Benchmark testing Acceleration Educational institutions Complex networks

来源：评论

学校读者我要写书评

暂无评论

Power and performance analysis of the Graph 500 benchmark on the Single-chip Cloud Computer

Power and performance analysis of the Graph 500 benchmark on...

引用

International Conference on Cloud Computing and Internet of Things (CCIOT)

作者： Zhiquan Lai King Tin Lam Cho-Li Wang Jinshu Su College of Computer National University of Defense Technology Changsha China Department of Computer Science The University of Hong Kong Hong Kong China National Key Laboratory of Parallel and Distributed Processing (PDL) Changsha China

ISBN: (纸本)9781479947638

The concerns of data-intensiveness and energy awareness are actively reshaping the design of high-performance computing (HPC) systems nowadays. The Graph500 is a widely adopted benchmark for evaluating the performance of computing systems for data-intensive workloads. In this paper, we introduce a data-parallel implementation of Graph500 on the Intel Single-chip Cloud Computer (SCC). The SCC features a non-coherent many-core architecture and multi-domain on-chip DVFS support for dynamic power management. With our custom-made shared virtual memory programming library, memory sharing among threads is done efficiently via the shared physical memory (SPM) while the library has taken care of the coherence. We conduct an in-depth study on the power and performance characteristics of the Graph500 workloads running on this system with varying system scales and power states. Our experimental results are insightful for the design of energy-efficient many-core systems for data-intensive applications.

关键词： Kernel Benchmark testing Computer architecture Programming Power measurement Computational modeling Runtime

来源：评论

学校读者我要写书评

暂无评论

F3A: Fairness-Aware AI-Workloads Allocation Considering Multidimensional User Demands in JointCloud

F3A: Fairness-Aware AI-Workloads Allocation Considering Mult...

引用

IEEE International Conference on Joint Cloud Computing (JCC)

作者： Jiacheng Yang Guodong Yi Fei Gao Peichang Shi Huaimin Wang National Key Laboratory of Parallel and Distributed Processing College of Computer Science National University of Defense Technology Changsha China Xiangjiang Lab Changsha China School of Advanced Interdisciplinary Studies Hunan University Of Technology and Business Changsha China

ISBN: (数字)9798350387339

ISBN: (纸本)9798350387346

With the rapid growth of large language models, cloud computing has become an indispensable component of the AI industry. Cloud service providers(CSPs) are establishing AI data centers to service AI workloads. In the face of this surging need for AI computing power, building a connected computing environment across various clouds and forming a JointCloud presents an attractive solution. However, scheduling AI tasks across multiple AI data centers within a JointCloud environment presents a significant challenge: how to balance users’ demands while ensuring CSPs’ fairness in scheduling. Existing research primarily focuses on optimizing scheduling quality with limited consideration for fairness. Therefore, this paper proposes a Fairness-Aware AI-Workloads Allocation method (F3A), a fair cross-cloud allocation technique for AI tasks. F3A utilizes Point and Token to reflect both the resource status and historical task allocations of AI data centers, enabling the consideration of users’ multidimensional demands and facilitating fair task allocation across multiple centers. In order to better assess the fairness of scheduling, we also devised a fairness indicator(FI), based on the Gini coefficient to measure the fairness of task allocation. The experimental results demonstrate that F3A consistently maintains FI within 0.1 across various cluster sizes and different task quantities, representing an improvement of 76.45% compared to classical fair scheduling algorithms round-robin. F3A exhibits commendable performance in ensuring fairness in task allocation while also demonstrating effectiveness in cost reduction and enhancing user satisfaction.

关键词： Industries Data centers Cloud computing Job shop scheduling Costs Scheduling algorithms Large language models

来源：评论

学校读者我要写书评

暂无评论

RAFlow: Read Ahead Accelerated I/O Flow through Multiple Virtual Layers

RAFlow: Read Ahead Accelerated I/O Flow through Multiple Vir...

引用

International Conference on Networking, Architecture, and Storage (NAS)

作者： Zhaoning Zhang Kui Wu Huiba Li Jinghua Feng Yuxing Peng Xicheng Lu National Key Laboratory for Parallel and Distributed Processing National University of Defense Technology China Computer Science Department University of Victoria B.C. Canada National Super Computing Center Tianjin China

Virtualization is the foundation for cloud computing, and the virtualization can not be achieved without software defined, elastic, flexible and scalable virtual layers. Unfortunately, if multiple virtual storage devices are chained together, the system may be subject to severe performance degradation. While the read-ahead (RA) mechanism in storage devices plays a very important role to improve I/O performance, RA may not be effective as expected for multiple virtualization layers, since it is originally designed for one layer only. When I/O requests are passed through a long I/O path, they may trigger a chain reaction and lead to unnecessary data transmission and thus bandwidth waste. In this paper, we study the dynamic behavior of RA through multiple I/O layers and demonstrate that if controlled well, RA can greatly accelerate I/O speed. We present RAFlow, a RA control mechanism, to effectively improve I/O performance by strategically expanding RA window at each layer. Our real-world experiments show that it can achieve 20% to 50% performance improvement in I/O paths with up to 8 virtualized storage devices.

关键词： Radio access networks Acceleration distributed databases Performance evaluation Synchronization Benchmark testing Degradation

来源：评论

学校读者我要写书评

暂无评论

Approximate Maximum Common Sub-graph Isomorphism Based on Discrete-Time Quantum Walk

Approximate Maximum Common Sub-graph Isomorphism Based on Di...

引用

International Conference on Pattern Recognition

作者： Kai Lu Yi Zhang Kai Xu Yinghui Gao Richard C. Wilson Science and Technology on Parallel and Distributed Processing Laboratory and College of Computer National University of Defense Technology Changsha P.R. China College of Electronic Science and Engineering National University of Defense Technology Changsha P.R. China Department of Computer Science University of York York UK

Maximum common sub-graph isomorphism (MCS) is a famous NP-hard problem in graph processing. The problem has found application in many areas where the similarity of graphs is important, for example in scene matching, video indexing, chemical similarity and shape analysis. In this paper, a novel algorithm Qwalk is proposed for approximate MCS, utilizing the discrete-time quantum walk. Based on the new observation that isomorphic neighborhood group matches can be detected quickly and conveniently by the destructive interference of a quantum walk, the new algorithm locates an approximate solution via merging neighborhood groups. Experiments show that Qwalk has better accuracy, universality and robustness compared with the state-of-the-art approximate MCS methods. Meanwhile, Qwalk is a general algorithm to solve the MCS problem approximately while having modest time complexity.

关键词： Approximation algorithms Interference Algorithm design and analysis Accuracy Time complexity Quantum computing Noise

来源：评论

学校读者我要写书评

暂无评论

DCSA: The Deployment Mechanism of Chained Serverless Applications in JointCloud Environment

DCSA: The Deployment Mechanism of Chained Serverless Applica...

引用

IEEE International Conference on Joint Cloud Computing (JCC)

作者： Yaojie Li Peichang Shi Jianfei Liu Rui Li Fei Gao Penghui Ma Dong Xie Guodong Yi National Key Laboratory of Parallel and Distributed Processing College of Computer Science National University of Defense Technology Changsha China Xiangjiang Lab Changsha China School of Advanced Interdisciplinary Studies Hunan University Of Technology and Business Changsha China

ISBN: (数字)9798350387339

ISBN: (纸本)9798350387346

Serverless computing, comprised of Function as a Service (FaaS) and Backend as a Service (BaaS), has garnered widespread attention owning to its features such as maintenance-free operations, pay-per-use pricing, and automatic scalability. However, practical usage encounters several challenges: 1) The diversity of user applications makes comprehensive performance evaluation difficult, as benchmark and application tests only reflect performance under specific conditions and cannot fully capture users’ actual experiences across different serverless platforms. 2) Disparities in performance and costs across different serverless platforms make it challenging to achieve optimal performance and cost efficiency through single-cloud deployment, thereby underutilizing the advantages of each platform. 3) Vendor lock-in issues restrict the migration of user applications and exacerbate dependence on a single cloud *** address these challenges, this paper proposes a collaborative mechanism, referred to as DCSA, which integrates FaaS and storage services to achieve automatic cross-cloud deployment of user applications while considering both performance and cost comprehensively. Firstly, we adapt the interfaces of different serverless platforms, effectively reducing the complexity of cross-cloud deployment. Secondly, we develop cost and latency models for the cross-cloud deployment of chained serverless applications and propose a deployment scheduling algorithm that simultaneously considers both latency and cost. Finally, we conduct experiments to evaluate the performance of the proposed algorithm. Results demonstrate that our method can effectively reduce latency (up to 2.3%) and lower costs (up to 9.9%).

关键词： Performance evaluation Adaptation models Costs Scheduling algorithms Scalability Serverless computing Collaboration

来源：评论

学校读者我要写书评

暂无评论

Audio Visual Speech Recognition with Multimodal Recurrent Neural Networks

Audio Visual Speech Recognition with Multimodal Recurrent Ne...

引用

International Joint Conference on Neural Networks

作者： Weijiang Feng Naiyang Guan Yuan Li Xiang Zhang Zhigang Luo Institute of Software College of Computer National University of Defense Technology Changsha Hunan P.R. China 410073 Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology Changsha Hunan P.R. China 410073

ISBN: (纸本)9781509061839

Studies on nowadays human-machine interface have demonstrated that visual information can enhance speech recognition accuracy especially in noisy environments. Deep learning has been widely used to tackle such audio visual speech recognition (AVSR) problem due to its astonishing achievements in both speech recognition and image recognition. Although existing deep learning models succeed to incorporate visual information into speech recognition, none of them simultaneously considers sequential characteristics of both audio and visual modalities. To overcome this deficiency, we proposed a multimodal recurrent neural network (multimodal RNN) model to take into account the sequential characteristics of both audio and visual modalities for AVSR. In particular, multimodal RNN includes three components, i.e., audio part, visual part, and fusion part, where the audio part and visual part capture the sequential characteristics of audio and visual modalities, respectively, and the fusion part combines the outputs of both modalities. Here we modelled the audio modality by using a LSTM RNN, and modelled the visual modality by using a convolutional neural network (CNN) plus a LSTM RNN, and combined both models by a multimodal layer in the fusion part. We validated the effectiveness of the proposed multimodal RNN model on a multi-speaker AVSR benchmark dataset termed AVletters. The experimental results show the performance improvements comparing to the known highest audio visual recognition accuracies on AVletters, and confirm the robustness of our multimodal RNN model.

关键词： Visualization Hidden Markov models Speech recognition Recurrent neural networks Feature extraction Logic gates Data models

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：