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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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DistMILE: A distributed Multi-Level Framework for Scalable Graph Embedding

DistMILE: A Distributed Multi-Level Framework for Scalable G...

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international conference on High Performance computing

作者： Yuntian He Saket Gurukar Pouya Kousha Hari Subramoni Dhabaleswar K. Panda Srinivasan Parthasarathy The Ohio State University

ISBN: (纸本)9781665410175

Scalable graph embedding on large networks is challenging because of the complexity of graph structures and limited computing resources. Recent research shows that the multi-level framework can enhance the scalability of graph embedding methods with little loss of quality. In general, methods using this framework first coarsen the original graph into a series of smaller graphs then learn the representations of the original graph from them in an efficient manner. However, to the best of our knowledge, most multi-level based methods do not have a parallel implementation. Meanwhile, the emergence of high-performance computing for machine learning provides an opportunity to boost graph embedding by distributed computing. In this paper, we propose a distributed MultI -Level Embedding (DistMILE 1 1 Our code is available at https://***/heyuntian/DistMILE) framework to further improve the scalability of graph embedding. DistMILE leverages a novel shared-memory parallel algorithm for graph coarsening and a distributed training paradigm for embedding refinement. With the advantage of high-performance computing techniques, Dist-MILE can smoothly scale different base embedding methods over large networks. Our experiments demonstrate that DistMILE learns representations of similar quality with respect to other baselines, while reduces the time of learning embeddings on large-scale networks to hours. Results show that DistMILE can achieve up to 28 x speedup compared with a popular multi-level embedding framework MILE and expedite existing embedding methods with 40 x speedup.

关键词： Training Codes Scalability High performance computing conferences distributed databases Machine learning

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Pencil: A Pipelined Algorithm for distributed Stencils

Pencil: A Pipelined Algorithm for Distributed Stencils

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international conference on High Performance computing, Networking, Storage and Analysis (SC)

作者： Wang, Hengjie Chandramowlishwaran, Aparna Univ Calif Irvine Irvine CA 92697 USA

ISBN: (纸本)9781728199986

Stencil computations are at the core of various Computational Fluid Dynamics (CFD) applications and have been well-studied for several decades. Typically they're highly memory-hound and as a result, numerous tiling algorithms have been proposed to improve its performance. Although efficient, most of these algorithms are designed for single iteration spaces on shared-memory machines. However, in CFD, we are confronted with multi-block structured girds composed of multiple connected iteration spaces distributed across many nodes. In this paper, we propose a pipelined stencil algorithm called Pencil for distributed memory machines that applies to practical CFD problems that span multiple iteration spaces. Based on an in-depth analysis of cache tiling on a single node, we first identify both the optimal combination of MPI and OpenMP for temporal tiling and the best tiling approach, which outperforms the stateof-the-art automatic parallelization tool Pluto by up to 1.92x. Then, we adopt DeepHalo to decouple the multiple connected iteration spaces so that temporal tiling can be applied to each space. Finally, we achieve overlap by pipelining the computation and communication without sacrificing the advantage from temporal cache tiling. Pencil is evaluated using 4 stencils across 6 numerical schemes on two distributed memory machines with Omni-Path and InfiniBand networks. On the Omni-Path system, Pencil exhibits outstanding weak and strong scalability for up to 128 nodes and outperforms MPI+OpenMP Funneled with space tiling by 1.33-3.41x on a multi-block grid with 32 nodes.

关键词： Computational fluid dynamics stencils multiple connected iteration spaces cache tiling pipelining distributedmemory machines

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Local-Adaptive Transformer for Multivariate Time Series Anomaly Detection and Diagnosis

Local-Adaptive Transformer for Multivariate Time Series Anom...

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IEEE international conference on Systems, Man and Cybernetics

作者： Xiaohui Zhou Yijie Wang Hongzuo Xu Mingyu Liu Ruyi Zhang National Key Laboratory of Parallel and Distributed Computing College of Computer National University of Defense Technology Changsha China

Time series data are pervasive in varied real-world applications, and accurately identifying anomalies in time series is of great importance. Many current methods are insufficient to model long-term dependence, whereas some anomalies can be only identified through long temporal contextual information. This may finally lead to disastrous outcomes due to false negatives of these anomalies. Prior arts employ Transformers (i.e., a neural network architecture that has powerful capability in modeling long-term dependence and global association) to alleviate this problem; however, Transformers are insensitive in sensing local context, which may neglect subtle anomalies. Therefore, in this paper, we propose a local-adaptive Transformer based on cross-correlation for time series anomaly detection, which unifies both global and local information to capture comprehensive time series patterns. Specifically, we devise a cross-correlation mechanism by employing causal convolution to adaptively capture local pattern variation, offering diverse local information into the long-term temporal learning process. Furthermore, a novel optimization objective is utilized to jointly optimize reconstruction of the entire time series and matrix derived from cross-correlation mechanism, which prevents the cross-correlation from becoming trivial in the training phase. The generated cross-correlation matrix reveals underlying interactions between dimensions of multivariate time series, which provides valuable insights into anomaly diagnosis. Extensive experiments on six real-world datasets demonstrate that our model outperforms state-of-the-art competing methods and achieves 6.8%-27.5% $F_{1}$ score improvement. Our method also has good anomaly interpretability and is effective for anomaly diagnosis.

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Understanding HPC Application I/O Behavior Using System Level Statistics 27

Understanding HPC Application I/O Behavior Using System Leve...

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27th IEEE international conference on High Performance computing, Data, and Analytics (HiPC)

作者： Paul, Arnab K. Faaland, Olaf Moody, Adam Gonsiorowski, Elsa Mohror, Kathryn Butt, Ali R. Virginia Tech Blacksburg VA 24061 USA Lawrence Livermore Natl Lab Livermore CA 94550 USA

ISBN: (纸本)9781665422925

The processor performance of high performance computing (HPC) systems is increasing at a much higher rate than storage performance. This imbalance leads to I/O performance bottlenecks in massively parallel HPC applications. Therefore, there is a need for improvements in storage and file system designs to meet the ever-growing I/O needs of HPC applications. Storage and file system designers require a deep understanding of how HPC application I/O behavior affects current storage system installations in order to improve them. In this work, we contribute to this understanding using application-agnostic file system statistics gathered on compute nodes as well as metadata and object storage file system servers. We analyze file system statistics of more than 4 million jobs over a period of three years on two systems at Lawrence Livermore National Laboratory that include a 15 PiB Lustre file system for storage. The results of our study add to the state-of-the-art in I/O understanding by providing insight into how general HPC workloads affect the performance of large-scale storage systems. Some key observations in our study show that reads and writes are evenly distributed across the storage system;applications which perform I/O, spread that I/O across similar to 78% of the minutes of their runtime on average;less than 22% of HPC users who submit write-intensive jobs perform efficient writes to the file system;and I/O contention seriously impacts I/O performance.

关键词： High Performance computing I/O analysis I/O contention Lustre File System parallel File Systems

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Pipelining Graph Construction and Agent-based Computation over distributed Memory

Pipelining Graph Construction and Agent-based Computation ov...

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IEEE international conference on Big Data

作者： Yan Hong Munehiro Fukuda Computing and Software Systems University of Washington Bothell Bothell

ISBN: (纸本)9781665480468

Graph streaming has received substantial attention for the past 10+ years to cope with large-scale graph computation. Two major approaches, one using conventional data-streaming tools and the other accessing graph databases, facilitate continuous analysis of endlessly flowing graphs and query-based incremental construction of huge graphs, respectively. However, some scientific graphs including biological networks need to stay in memory for repetitive but various analyses. Although a cluster system, thus distributed memory can entirely handle a big graph in memory, a challenge is substantial overhead incurred by loading graphs into memory. A solution is hiding such graph-loading and construction overheads with graph computation in a pipelined fashion. We adapted this pipelining approach for agent-based graph computing where thousands of agents traverse a graph for finding its attributes and shape. We used the multi-agent spatial simulation (MASS) library to implement the concept. A huge graph is incrementally constructed in batches, each spawning and walking agents over the corresponding subgraph, and thus all eventually completing a given computation. We coded and ran two MASS benchmark programs: triangle counting and connected components, with which we evaluated our pipelined graph processing. The best performance was obtained once the batch size shrunk enough to fit cache memory, regardless of the number of cluster nodes. For a single node execution of connected components over a 140MB graph, our graph-pipelining implementation performed 7.7 times faster than non-pipelining execution. Its parallel execution with 24 cluster nodes achieved 8.3 times speed-up as compared to the pipelined single-node execution.

关键词： Shape Scalability Loading XML Big Data Size measurement Libraries

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An Intelligent TEV Sensor for Partial Discharge Detection of Cable Terminals 5th

An Intelligent TEV Sensor for Partial Discharge Detection of...

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5th international conference on Intelligent, Interactive Systems and Applications, IISA 2020

作者： Shen, Chen Bai, Xiaochun Jing, Yan Ma, Jiangang Ren, Ming Zhuang, Tianxin Xia, Changjie Electric Power Research Institute of State Grid Shaanxi Electric Power Company Beijing China Xi’an Jiaotong University Xi’an China

ISBN: (纸本)9783030637835

Power cable, as an important power equipment widely used in urban power transmission and distribution, its insulation state influences the reliability of urban power supply. Partial discharge (PD) detection is an effective approach to realize early fault detection and diagnosis. Transient earth voltage (TEV) is an electrification detection technology with the advantages of high sensitivity and strong anti-interference ability, which has emerged as a powerful PD detection method of cable terminals or joints. However, different with other power equipment, due to the wide distribution and underground installation of attachment monitoring points, it is difficult to apply the current TEV to achieve the real-time monitoring. In this paper, a distributed TEV intelligent sensing network for PD detection of cable terminals is proposed. On the basis of analyzing the time-frequency characteristics of the internal discharge signals at the cable terminations and joints, the intelligent TEV sensing unit based on wireless network is designed. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Fault detection

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