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Data Aggregation Aware Routing for distributed Training 21st

Data Aggregation Aware Routing for Distributed Training

引用

21st international conference on parallel and distributed computing, Applications, and Technologies, PDCAT 2020

作者： Chen, Zhaohong Long, Xin Wu, Yalan Chen, Long Wu, Jigang Liu, Shuangyin School of Computer Science and Technology Guangdong University of Technology Guangzhou China Guangzhou Key Laboratory of Agricultural Products Quality and Safety Traceability Information Technology Zhongkai University of Agriculture and Engineering Guangzhou China

ISBN: (纸本)9783030692438

For distributed training, the communication overhead for parameter synchronization is heavy in the network. Data aggregation can efficiently alleviate network overheads. However, existing works on data aggregation are based on the streaming message data, which can not well adapt to the discrete communication for parameter synchronization. This paper formulates a data aggregation aware routing problem, with the objective of minimizing training finishing time for global model under the constraint of cache capacity. The problem is formulated as a mixed-integer non-linear programming problem, and it is proved to be NP-Hard. Then we propose a data aggregation aware routing algorithm to solve the formulated problem, by transmitting the data to the closest aggregation node in greedy to reduce the network overhead. Simulation results show that, the proposed algorithm can reduce average training finishing time by 74 %, and it can reduce the network overhead by 33 % on average, compared with the shortest path algorithm. © 2021, Springer Nature Switzerland AG.

关键词： Synchronization

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Big data collaborative artificial intelligence and high-performance computing to drive physical design of fusion

Big data collaborative artificial intelligence and high-perf...

引用

Cloud computing, Big Data Applications and Software Engineering (CBASE), international conference on

作者： Pan Liu Gongjie Liu Jie Liu Chencheng Liu Wenbin Xiong Zhangcun Tang Yinzhao Zhou Yonggang Wang Zhouming Yuan Tianfu Innovation Energy Establishment Chengdu China Beijng PARATERA Technology Corp. Ltd Beijing China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China Laboratory of Software Engineering for Complex Systems National University of Defense Technology Changsha China Central South University of Forestry and Technology Changsha China Nuclear and Radiation Safety Center MEE Beijing China University of Electronic Science and Technology of China Chengdu China

Controlled thermonuclear fusion has always been a dream pursued by mankind. However, the physical processes of controlled thermonuclear fusion are complex, requiring numerical simulations with high performance computing, and the amount of data generated by the physical processes on spatial, temporal and temperature scales is too large to be captured, managed, processed and collated in a reasonable time frame by mainstream software tools to achieve more aggressive fusion physical design. The data are too large to be captured, managed, processed, and collated into more aggressive targets for fusion physical design in a reasonable time by mainstream software tools. At the same time, the failure of fusion ignition can be caused by the distortion of various key physical quantities, and only by decomposing the process step by step and clarifying the changes of key physical quantities in the fusion physics process, can an effective mechanism be formed to prevent the distortion of key physical quantities from causing ignition failure in experimental physics. Big data in collaboration with artificial intelligence and high performance computing to drive the physical design of fusion is a novel avenue. By data acquisition with and pre-processing, this allows the creation of small sample libraries and deep learning. With supervised learning function convergence, incorporating solid/fluid computational methods, further network layering and cell expansion, the parameters of the physical model conforming to Lawson’s criterion will become experimental physical parameters, and we find the new approach in Data capacity, Model combination approach, Type of material, Calculation speed, Optimisation of design iteration times, etc. are superior to the traditional approach.

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A Dynamic Mapping Model for General CNN Accelerator Based on FPGA 17th

A Dynamic Mapping Model for General CNN Accelerator Based on...

引用

17th IFIP WG 10.3 international conference on Network and parallel computing, NPC 2020

作者： Zhao, Xiaoqiang Jiang, Jingfei Han, Zhe Xu, Jinwei Liu, Zhiqiang National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China Artificial Intelligence Research Center National Innovation Institute of Defense Technology Beijing China

ISBN: (纸本)9783030794774

As the application scenarios of convolutional neural network (CNN) become more and more complex, the general CNN accelerator based on matrix multiplication has become a new research focus. The existing mapping methods for converting convolution calculation into matrix multiplication need to be improved. This paper proposes a new dynamic mapping model to improve the flexibility and versatility of matrix multiplication. The dynamic mapping model implements two algorithms: dynamic residue processing mapping algorithm (DRPMA) and dilated convolution mapping algorithm (DCMA). The former can dynamically adjust the mapping method according to the number of output channels of the convolution layer, improve the utilization of the multiply-accumulate (MAC) array. The latter extends the efficient support for Dilated CNNs. For demonstration, we implement an accelerator with Verilog on Xilinx VC709 FPGA board and test some typical CNN models. Experimental results show that the general accelerator achieves high performance and energy efficiency. © 2021, IFIP international Federation for Information Processing.

关键词： Field programmable gate arrays (FPGA)

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DistStream: An Order-Aware distributed Framework for Online-Offline Stream Clustering Algorithms 40

DistStream: An Order-Aware Distributed Framework for Online-...

引用

40th IEEE international conference on distributed computing Systems (ICDCS)

作者： Xu, Lijie Ye, Xingtong Kang, Kai Guo, Tian Dou, Wensheng Wang, Wei Wei, Jun Chinese Acad Sci Inst Software State Key Lab Comp Sci Guangzhou Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Inst Software Technol Nanjing Peoples R China Worcester Polytech Inst Worcester MA 01609 USA

ISBN: (纸本)9781728170022

Stream clustering is an important data mining technique to capture the evolving patterns in real-time data streams. Today's data streams, e.g., IoT events and Web clicks, are usually high-speed and contain dynamically-changing patterns. Existing stream clustering algorithms usually follow an online-offline paradigm with a one-record-at-a-time update model, which was designed for running in a single machine. These stream clustering algorithms, with this sequential update model, cannot be efficiently parallelized and fail to deliver the required high throughput for stream clustering. In this paper, we present DistStream, a distributed framework that can effectively scale out online-offline stream clustering algorithms. To parallelize these algorithms for high throughput, we develop a mini-batch update model with efficient parallelization approaches. To maintain high clustering quality, DistStream's mini-batch update model preserves the update order in all the computation steps during parallel execution, which can reflect the recent changes for dynamically-changing streaming data. We implement DistStream atop Spark Streaming, as well as four representative stream clustering algorithms based on DistStream. Our evaluation on three real-world datasets shows that DistStream-based stream clustering algorithms can achieve sublinear throughput gain and comparable (99%) clustering quality with their single-machine counterparts.

关键词： Stream clustering data stream scalability

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Blockchain-Based distributed Reputation for a Cap-and-Trade Carbon Emission System

Blockchain-Based Distributed Reputation for a Cap-and-Trade ...

引用

IEEE international conference on Energy Internet (ICEI)

作者： Dong Wang Da Li Junwei Ma Zhenhua Yan Yongliang Li Tonghe Wang Songpu Ai Junwei Cao State Grid Blockchain Technology (Beijing) Co. Ltd. Beijing P.R. China State Grid Shanxi Electric Power Co. Ltd. Taiyuan Shanxi P.R. China State Grid Ningxia Electric Power Company Yinchuan Ningxia P.R. China Tsinghua University Beijing P.R. China

ISBN: (纸本)9781665407359

Reputation has been widely used in the energy field in recent years. However, their reputation mechanisms are usually centralized even if they are designed for distributed energy systems, which could cause vulnerability to single point failures. This paper explores the design of blockchain-based distributed reputation for a cap-and-trade carbon emission system. The blockchain technology is adopted to achieve distributed management of reputation scores and realize a peer-to-peer carbon trading market. Simulation experiments are carried out to demonstrate the influence of the proposed reputation rules on reputation scores. In addition, a case study shows how reputation affects the results of carbon trading. As far as we know, this paper is one of the few works that incorporate distributed reputation in a carbon emission system.

关键词： conferences Carbon dioxide Emissions trading Blockchains Peer-to-peer computing Internet distributed management

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Proceedings - 2020 IEEE 26th international conference on parallel and distributed Systems, ICPADS 2020

Proceedings - 2020 IEEE 26th International Conference on Par...

引用

26th IEEE international conference on parallel and distributed Systems, ICPADS 2020

ISBN: (纸本)9781728190747

The proceedings contain 91 papers. The topics discussed include: a personal distributed real-time collaborative system;a customized reinforcement learning based binary offloading in edge cloud;efficient post-quantum identity-based encryption with equality test;D2D-enabled reliable data collection for mobile crowd sensing;deep spatio-temporal attention model for grain storage temperature forecasting;secure and verifiable data access control scheme with policy update and computation outsourcing for edge computing;proactive content caching for internet-of-vehicles based on peer-to-peer federated learning;a similarity clustering-based deduplication strategy in cloud storage systems;and DyRAC: cost-aware resource assignment and provider selection for dynamic cloud workloads.

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Collar Data Synthesis and Its Application for Generating Virtual Collar 3D Novel View from a Single-View

Collar Data Synthesis and Its Application for Generating Vir...

引用

IEEE international conference on Big Data and Cloud computing (BdCloud)

作者： Na Guo Pei-Wei Tsai Hongwang Xiao Chang Wu Wenjuan Duan Department of Computing Technologies Swinburne University of Technology Melbourne Australia School of Mechanical Electrical &Information Engineering Shandong University Weihai Weihai China Beijing Academy Artificial Intelligence Beijing China Department of Computing Technologies Beijing Institute of Technology Beijing China

In recent years, virtual reality, big data and cloud computing technologies have all made rapid development, and they are also more and more closely combined with the apparel industry. The apparel digital design and virtual fitting techniques are playing increasingly important roles in the field of apparel design, manufacturing and sales. The three-dimensional digital design and virtual fitting make the designers or the users have multi-view and more real impressions and experiences of the apparel. But 3D display is difficult to be popularized in apparel industry because of its high cost. The cost of two-dimensional display is low and easy to popularize. However, 2D system can only provide images of a specific perspective, lacking a multi-perspective experience. If the supplier is required to provide 2D images of several specific perspectives, the personalized needs of users cannot be satisfied. Therefore, how to make 2D system according to an object image and users' needs to provide the object images of other perspectives is an urgent problem to be solved. In this work, we propose a novel view synthesis pipeline based on an enhanced Pix2Pix neural network for a novel view (Pix2Pix-V). It consists of four parts: 1) a new method of generating the labeled virtual collar image data based on its 3D model, and the 2D images can be obtained on any perspective; 2) Pix2Pix-V neural network; 3) a Pix2Pix-V training workflow for predicting the novel view; 4) transfer learning is used in predicting similar views of virtual collars.

关键词： Industries Solid modeling Three-dimensional displays Costs Pipelines Transfer learning Neural networks

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distributed Manufacturing in Knitting Industry

Distributed Manufacturing in Knitting Industry

引用

Digital Twins and parallel Intelligence (DTPI), IEEE international conference on

作者： Lijun Li Pingfan Sun Jixiang Lu School of Automation Southeast University Nanjing China Institute of Knitting Ningbo Cixing Co. Ltd. Ningbo China College of Science and Technology Ningbo University Ningbo China

Knitwear is the second largest product category in the traditional clothing industry. The traditional clothing product supply chain is difficult to meet consumer's demand of fast fashion. AI and industrial Internet technology, together with intelligent knitting equipments make C2M flexible supply chain possible through digitization. distributed cloud factory is built as regional node of production supported by digital twin and parallel intelligence. Personalized needs of end consumers can be quickly responded, and the cost is close to traditional large-scale assembly line. parallel manufacturing applies industrial robots, Internet of things, cloud computing, AR and AI technologies comprehensively to build virtual factory in Cloud that carries out decision-making and management simultaneously, with highly automated production equipments in workshop. Meanwhile, it combines with smart knitting factories to build a global distributed collaborative production management platform based on parallel manufacturing architecture system through virtual reality interaction, distributed manufacturing, managed and controlled through Cloud. Intelligent upgrading of traditional knitting industry can effectively improve labor productivity, reduce production cost and number of employees.

关键词： Productivity Cloud computing Costs Digital twin conferences Supply chains Collaboration

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Improvement of load balancing in shared-memory multiprocessor systems 1st

Improvement of load balancing in shared-memory multiprocesso...

引用

1st international conference on Intelligent and Cloud computing, ICICC 2019

作者： Deeb, Hasan Sarangi, Archana Sarangi, Shubhendu Kumar Department of Computer Science and Engineering ITER Siksha ‘O’ Anusandhan Deemed to be University BhubaneswarOdisha India Department of Electronics and Instrumentation Engineering ITER Siksha ‘O’ Anusandhan Deemed to be University BhubaneswarOdisha India

ISBN: (纸本)9789811562013

parallel programming is one of the most effective approaches to handle complex problems regarding time complexity by reducing computation time, by getting the most of the capacity of the processors and shared-memory or distributed systems. One of the main ingredients of parallel programming is ‘Loops’ and especially DOALL loops. All loop scheduling algorithms try to achieve load balancing by using chunk resizing techniques (decreasing, increasing…). In this paper, prior loop scheduling algorithms will be evaluated on Mandelbrot. A new algorithm is obtained by merging the increasing and decreasing chunk size techniques in order to acquire the advantages of both approaches. The experimental results show that in the decreasing approach, a large number of small chunks in the last stages will increase the scheduling overhead because of the increasing of inter-processor communication. Also, the large chunk size of the initial stages will increase load imbalance. On the other hand, for the increasing approach, the large chunks assigned to processors at the last stages might increase load imbalance especially when iterations of the last stages are more time consuming than others. This work will introduce a new approach. This approach will try to minimize the load imbalance and communication overhead that are caused by using a decreasing chunk size approach and to minimize the load imbalance and scheduling overhead caused by the use of increase chunk size approach, which is going to provide better performance for many workload patterns. © Springer Nature Singapore Pte Ltd 2021.

关键词： parallel programming

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OmpMemOpt: Optimized Memory Movement for Heterogeneous computing 26th

OmpMemOpt: Optimized Memory Movement for Heterogeneous Compu...

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26th international conference on parallel and distributed computing (Euro-Par)

作者： Barua, Prithayan Zhao, Jisheng Sarkar, Vivek Georgia Inst Technol Atlanta GA 30332 USA

ISBN: (纸本)9783030576752;9783030576745

The fast development of acceleration architectures and applications has made heterogeneous computing the norm for high-performance computing. The cost of high volume data movement to the accelerators is an important bottleneck both in terms of application performance and developer productivity. Memory management is still a manual task performed tediously by expert programmers In this paper, we develop a compiler analysis to automate memory management for heterogeneous computing. We propose an optimization framework that casts the problem of detection and removal of redundant data movements into a partial redundancy elimination (PRE) problem and applies the lazy code motion technique to optimize these data movements. We chose OpenMP as the underlying parallel programming model and implemented our optimization framework in the LLVM toolchain. We evaluated it with ten benchmarks and obtained a geometric speedup of 2.3x, and reduced on average 50% of the total bytes transferred between the host and GPU.

关键词： Compiler optimization GPUs OpenMP Memory management

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