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ICE: Intelligent Caching at the Edge

ICE: Intelligent Caching at the Edge

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2021 IEEE Global Communications conference (GLOBECOM)

作者： Ting Wang Jiawei Mao Mingsong Chen Gang Liu Jieming Di Shui Yu Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China Bell Labs Shanghai China Microsoft Bing Seatle USA School of Computer Science University of Technology Sydney Australia

ISBN: (纸本)9781728181059

The unprecedented growth of mobile data traffic brings unique challenges for network bandwidth and server resources to meet the diverse QoE (Quality of Experience). Caching becomes a promising way to alleviate these issues by storing a subset of data at the network edge, for which caching policy becomes critical. To this end, various caching schemes have been put forward, however, these schemes are either not intelligent lacking the ability of self-learning and self-decision-making, or inefficient with low data hit rate. Based on these observations, in this paper, we propose a novel Intelligent Caching framework at the Edge, named ICE, via deep reinforcement learning to capture certain valued information of the requested data. Notably, in our approach, the popularity of the data to be cached will be explored and considered. A Markov decision model is further developed to determine whether the data should be cached. The evaluation shows that ICE greatly improves the hit rate in comparison with the state-of-the-art approaches, and reduces the energy consumption for data transmission. Furthermore, based on ICE, the users' QoE is greatly improved. In conclusion, both theoretical analysis and experimental results prove the effectiveness and high performance of ICE compared with conventional strategies.

关键词： Energy consumption Q-learning Simulation Markov processes Data models Steady-state Delays

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Design and Implementation of high performance 2D Mesh NoC Based On New Proposed Router Using FPGA

Design and Implementation of High Performance 2D Mesh NoC Ba...

引用

Contemporary Information Technology and Mathematics (ICCITM), International conference on

作者： Noor Dhyaa Majeed Siraj Qays Mahdi Maha Abdulameer Kadhim Computer Engineering Techniques Middle Technical University Baghdad Iraq Middle Technical University Baghdad Iraq

ISBN: (纸本)9781665420334

The classic interconnection among Intellectual Property (IP) cores in a System on Chip (SoC) became ineffective due to the increase the numbers of processors on single chip. These factors lead to the emergence of Network on Chip (NoC) technology. In this paper, 2D Mesh network for 16 node were implemented with a new proposed router to solve packets conflict problem. This network has low resources, where the network utilization ratio of slices is about 24% from the available resources and the maximum frequency 102.093MHz. The total consumption power is about 150 mW for the network, where the consumption static power is 32 mW only. The router in this network has a schedule with a fixed priority, which leads to data flowing without any conflict among packets. Moreover, the crossbar in this router consists of one multiplexer, counter and without additional inputs buffers unlike traditional crossbar, which consists of five multiplexer with multiple input buffers. This router is implemented and tested on the FPGA Spartan 3A (XC3S700A) kit.

关键词： Schedules Time-frequency analysis Mesh networks program processors Intellectual property Generators Topology

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A lightweight CaaS private cloud architecture and models for many task computing 14

A lightweight CaaS private cloud architecture and models for...

引用

14th International conference on computer Science and Education (ICCSE)

作者： Xu, Yiyi Liu, Pengfei Than, Jun Univ Tasmania Australian Maritime Coll Hobart Tas 7248 Australia Guangxi Univ Sci & Technol Liuzhou 545006 Peoples R China Newcastle Univ Sch Engn Newcastle Upon Tyne NE1 7RU Tyne & Wear England Univ Tasmania AMC Hobart Tas 7248 Australia Guangxi Univ Sci & Technol Coll Civil Engn Liuzhou 545006 Peoples R China

ISBN: (纸本)9781728118468

CPU time has long been a remaining problem for large-scale scientific computing. For some less computing-intensive but many-task-computing codes, it may take several weeks or even months to run. With the increase of datasets scale, computing time grows dramatically - this hence can be more and more challenging. To address this problem, most solutions based on ongoing hardware and/or software investment. To address this issue, this research is to make the following contributions to literature: a new "Computing performance first" Computing as a Service (CaaS) model to support scientific computing and contribute as a novel high-performance private cloud computing (HPPCC) method. Efficiency of this approach is demonstrated by experiments with numerical simulation on both with End nodes and without End nodes.

关键词： Computing as a Service high-performance Private Cloud Computing Scientific computing Many-task-computing

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highly efficient breadth-first search on CPU-based single-node system 21

Highly efficient breadth-first search on CPU-based single-no...

引用

21st IEEE International conference on high performance Computing and Communications, 17th IEEE International conference on Smart City and 5th IEEE International conference on Data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Zhang, Chenglong Cao, Huawei Ye, Xiaochun Wang, Guobo Hao, Qinfen Fan, Dongrui State Key' Laboratoiy of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9781728120584

With the rapid development of computer technology, the scale of graph increases explosively and large-scale graph computing has been the focus in recent years. Breadth-First Search (BFS) is one of the most important kernels in graph computing. It is the main kernel of the Graph500 benchmark that evaluates the performance of supercomputers and servers in terms of data-intensive applications. In this paper, we implement a highly efficient parallel BFS on CPU-based single-node systems. We propose two algorithm optimization techniques: round-robin vertex shuffle and jumping access. The former can improve the load balance for parallel implementation, while the latter can reduce unnecessary data accesses in the bottom-up algorithm. Our new implementation demonstrates strong scalability and achieves 65.2 GTEPS for Kronecker graph with 2 30 vertices and 2 34 edges on a 4-way Xeon server. The speedup is nearly 12.8 times faster than the original direction-optimizing algorithm. In terms of energy efficiency, the result on our self-designed ARM server is 254.6 MTEPS/W and is superior to conventional x86 server. To our best knowledge, this is the first work that evaluates BFS performance on ARM server and ARM server is suitable for data-intensive applications such as large-scale graph processing. © 2019 IEEE.

关键词： Supercomputers

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Explicit Residual Descent for 3D Human Pose Estimation from 2D Joint Locations 31

Explicit Residual Descent for 3D Human Pose Estimation from ...

引用

31st British Machine Vision conference, BMVC 2020

作者： Kang, Yangyuxuan Yao, Anbang Wang, Shandong Lu, Ming Chen, Yurong Wu, Enhua State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences China University of Chinese Academy of Sciences China Intel Labs China Faculty of Science and Technology University of Macau China

Recent studies show that the end-to-end learning paradigm based on well-designed lifting networks merely using 2D joint locations as the input can achieve impressive performance in handling 3D human pose estimation problem. However, in the viewpoint of optimization design, existing methods of this category have two drawbacks: (1) The inherent feature relation between the 2D pose input and the corresponding 3D pose estimate is not sufficiently explored. (2) The regression procedure is usually performed in a one-step manner. To address these two issues, this paper proposes an efficient yet accurate method called Explicit Residual Descent (ERD). Given an arbitrary lifting network which takes 2D joint locations in a single image as the input and generates an initial 3D pose estimate, our ERD learns a sequence of descent directions encoded with a shared lightweight differentiable structure, progressively refining the previous 3D pose estimate via adding in a 3D increment obtained from projecting the reconstructed 2D pose features onto each learnt descent direction. Extensive experiments on public benchmarks including Human3.6M dataset validate the superior performance of the proposed method against state-of-the-art methods. Code will be made publicly available. © 2020. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.

关键词： Benchmarking

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Mongolian emotional speech synthesis based on transfer learning and emotional embedding

Mongolian emotional speech synthesis based on transfer learn...

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International conference on Asian Language Processing (IALP)

作者： Aihong Huang Feilong Bao Guanglai Gao Yu Shan Rui Liu National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Provincial Key Laboratory of Mongolian Information Processing Technology School of Computer Science Inner Mongolia University Hohhot China

ISBN: (纸本)9781665483124

In recent years, end-to-end speech synthesis based on attention has achieved better performance than traditional speech synthesis models, and the technology of end-to-end Mongolian speech synthesis has reached the application standard. However, due to the sparse training corpus, the research on Mongolian emotional speech synthesis is still far from perfect. In response to these problems, we established a Mongolian emotional corpus and constructed an emotionally controllable Mongolian speech synthesis system for the first time. Through combining transfer learning and emotional embedding, the Mongolian emotional speech synthesis system with 8 kinds of emotions (happy, angry, sadness, surprise, fear, disgust, boredom and neutral) has been achieved. We proposed the method that emotional labels are used as the input of the emotional embedding layer to generate emotional vectors, which are spliced with the output vectors of the bidirectional LSTM layer, so that the text representation vectors contain information about emotional category, thereby synthesize a variety of different emotional voices. Experiments show that our method can synthesize high-quality Mongolian emotional speech.

关键词： Training Transfer learning Speech enhancement Controllability Speech synthesis Standards

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Towards large-scale sparse matrix-vector multiplication on the SW26010 manycore architecture 21

Towards large-scale sparse matrix-vector multiplication on t...

引用

作者： Chen, Yuedan Xiao, Guoqing Wu, Fan Tang, Zhuo College of Computer Science and Electronic Engineering Hunan University Changsha Hunan410082 China National Supercomputing Center in Changsha Changsha Hunan410082 China David R. Cheriton School of Computer Science University of Waterloo WaterlooONN2L 3G1 Canada

ISBN: (纸本)9781728120584

Sparse matrix-vector multiplication (SpMV) is one of the important subroutines in numerical linear algebra widely used in plenty of large-scale applications. This paper focuses on scaling and optimizing SpMV for large-scale applications based on the memory structure and computing architecture of SW26010 CPU of the Sunway TaihuLight supercomputer. We propose the large-scale SpMV on the Sunway TaihuLight that includes two parts, i.e., the parallel partial (Compressed Sparse Row) CSR-based SpMV part and the parallel accumulation part. We respectively propose the adaptive partitioning methods and parallelization designs for the two parts of the large-scale SpMV based on the SW26010 architecture. The experimental results prove that the large-scale SpMV achieves high efficiency and good scalability on the Sunway TaihuLight. © 2019 IEEE.

关键词： Supercomputers

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Brazilian Midwest Native Vegetation Mapping Based on Google Earth Engine

Brazilian Midwest Native Vegetation Mapping Based on Google ...

引用

2020 IEEE Latin American GRSS and ISPRS Remote Sensing conference, LAGIRS 2020

作者： Estrabis, N.V. Osco, L. Ramos, A.P. Goncalves, W.N. Liesenberg, V. Pistori, H. Junior, J. Marcato Federal University of Mato Grosso do Sul Mato Grosso do Sul Faculty of Engineering Architecture and Urbanism and Geography Brazil University of Westem São Paulo Presidente Prudente Environmental and Regional Development Brazil Santa Catarina State University Department of Forest Engineering Santa Catarina Brazil Dom Bosco Catholic University Department of Computer Engineering Brazil

ISBN: (纸本)9781728143507

Google Earth Engine (GEE) platform is an online tool, which generates fast solutions in terms of image classification and does not require high performance computers locally. We investigate several data input scenarios for mapping native-vegetation and nonnative-vegetation in the Atlantic Forest region encompassed in a Landsat scene (224/076) acquired on November 28, 2019. The data input scenarios were: I- spectral bands (blue to shortwave infrared);II- NDVI (Normalized Difference Vegetation Index);IIImNDWI (modified Normalized Difference Water Index);IV- scenarios I and II;and V- scenarios I to III. Our results showed that the use of spectral bands added NDVI and mNDWI (scenario V) provided the best performance for the native-vegetation mapping, with accuracy of 96.64% and kappa index of 0.91. © 2020 IEEE.

关键词： Landsat

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Auto-tuning MPI collective operations on large-scale parallel systems 21

Auto-tuning MPI collective operations on large-scale paralle...

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作者： Zheng, Wenxu Fang, Jianbin Juan, Chen Wu, Feihao Pan, Xiaodong Wang, Hao Sun, Xiaole Yuan, Yuan Xie, Min Huang, Chun Tang, Tao Wang, Zheng College of Computer National University of Defense Technology China School of Computing and Communications Lancaster University United Kingdom Xi'an University of Posts AND Telecommunications China

ISBN: (纸本)9781728120584

MPI libraries are widely used in applications of high performance computing. Yet, effective tuning of MPI colletives on large parallel systems is an outstanding challenge. This process often follows a trial-and-error approach and requires expert insights into the subtle interactions between software and the underlying hardware. This paper presents an empirical approach to choose and switch MPI communication algorithms at runtime to optimize the application performance. We achieve this by first modeling offline, through microbenchmarks, to find how the runtime parameters with different message sizes affect the choice of MPI communication algorithms. We then apply the knowledge to automatically optimize new unseen MPI programs. We evaluate our approach by applying it to NPB and HPCC benchmarks on a 384-node computer cluster of the Tianhe-2 supercomputer. Experimental results show that our approach achieves, on average, 22.7% (up to 40.7%) improvement over the default setting. © 2019 IEEE.

关键词： Supercomputers

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Multi-graph fusion for functional neuroimaging biomarker detection 29

Multi-graph fusion for functional neuroimaging biomarker det...

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29th International Joint conference on Artificial Intelligence, IJCAI 2020

作者： Gan, Jiangzhang Zhu, Xiaofeng Hu, Rongyao Zhu, Yonghua Ma, Junbo Peng, Ziwen Wu, Guorong Center for Future Media School of Computer Science and Technology University of Electronic Science and Technology of China Chengdu611731 China School of Natural and Computational Science Massey University Auckland Campus New Zealand School of Medicine Department of Computer Science University of North Carolina Chapel HillNC27599 United States College of Psychology and Sociology Shenzhen University Shenzhen518060 China Sichuan Artificial Intelligence Research Institute Yibin644000 China

ISBN: (纸本)9780999241165

Brain functional connectivity analysis on fMRI data could improve the understanding of human brain function. However, due to the influence of the inter-subject variability and the heterogeneity across subjects, previous methods of functional connectivity analysis are often insufficient in capturing disease-related representation so that decreasing disease diagnosis performance. In this paper, we first propose a new multi-graph fusion framework to fine-tune the original representation derived from Pearson correlation analysis, and then employ `1-SVM on fine-tuned representations to conduct joint brain region selection and disease diagnosis for avoiding the issue of the curse of dimensionality on high-dimensional data. The multi-graph fusion framework automatically learns the connectivity number for every node (i.e., brain region) and integrates all subjects in a unified framework to output homogenous and discriminative representations of all subjects. Experimental results on two real data sets, i.e., fronto-temporal dementia (FTD) and obsessive-compulsive disorder (OCD), verified the effectiveness of our proposed framework, compared to state-of-the-art methods. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Brain

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