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检索条件"任意字段=International Conference on Parallel Distributed and Grid Computing"

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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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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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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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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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Duality-Based Locality-Aware Stream Partitioning in distributed Stream Processing Engines 25th

Duality-Based Locality-Aware Stream Partitioning in Distribu...

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25th international conference on parallel and distributed computing (Euro-Par)

作者： Son, Siwoon Moon, Yang-Sae Kangwon Natl Univ Chunchon South Korea

ISBN: (纸本)9783030483401;9783030483395

In this paper, we propose duality-based locality-aware stream partitioning (LSP) in distributed stream processing engines (DSPEs). In general, LSP directly uses the locality concept of distributed batch processing engines (DBPEs). This concept does not fully take into account the characteristics of DSPEs and therefore does not maximize cluster resource utilization. To solve this problem, we first explain the limitations of existing LSP, and we then propose a duality relationship between DBPEs and DSPEs. We finally propose a simple but efficient ping-based mechanism to maximize the locality of DSPEs based on the duality. The insights uncovered in this paper can maximize the throughput and minimize the latency in stream partitioning.

关键词： distributed processing Data stream Locality Duality

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3rd international Symposium on Signal and Image Processing, ISSIP 2020

3rd International Symposium on Signal and Image Processing, ...

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3rd international Symposium on Signal and Image Processing, ISSIP 2020

ISBN: (纸本)9789813369658

The proceedings contain 16 papers. The special focus in this conference is on Signal and Image Processing. The topics include: Deep Convolutional Neural Network-Based Diagnosis of Invasive Ductal Carcinoma;speaker Identification in Spoken Language Mismatch Condition: An Experimental Study;Ultrasound Image Classification Using ACGAN with Small Training Dataset;preface;Chaotic Ions Motion Optimization (CIMO) for Biological Sequences Local Alignment: COVID-19 as a Case Study;assessment of Eyeball Movement and Head Movement Detection Based on Reading;using Hadoop Ecosystem and Python to Explore Climate Change;a Brief Review of Intelligent Rule Extraction Techniques;the Effect of Different Feature Selection Methods for Classification of Melanoma;intelligent Hybrid Technique to Secure Bluetooth Communications;parallel Algorithm to find Integer k where a given Well-distributed Graph is k-Metric Dimensional;a Fog-Based Retrieval of Real-Time Data for Health Applications;differential Evolution-Based Shot Boundary Detection Algorithm for Content-Based Video Retrieval;qutrit-Based Genetic Algorithm for Hyperspectral Image Thresholding.

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A Novel Data-Centric Programming Model for Large-Scale parallel Systems 25th

A Novel Data-Centric Programming Model for Large-Scale Paral...

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25th international conference on parallel and distributed computing (Euro-Par)

作者： Talia, Domenico Trunfio, Paolo Marozzo, Fabrizio Belcastro, Loris Garcia-Blas, Javier del Rio, David Couvee, Philippe Goret, Gael Vincent, Lionel Fernandez-Pena, Alberto Martin de Blas, Daniel Nardi, Mirko Pizzuti, Teresa Spataru, Adrian Justyna, Marek Univ Calabria Arcavacata Di Rende Italy Univ Carlos III Madrid Madrid Spain Atos BDS R&D Data Management Echirolles France Inst Invest Sanitaria Gregorio Maranon Madrid Spain INTEGRIS Rome Italy Inst E Austria Timisoara Timisoara Romania PSNC Poznan Poland

ISBN: (纸本)9783030483401;9783030483395

This paper presents the main features and the programming constructs of the DCEx programming model designed for the implementation of data-centric large-scale parallel applications on Exascale computing platforms. To support scalable parallelism, the DCEx programming model employs private data structures and limits the amount of shared data among parallel threads. The basic idea of DCEx is structuring programs into data-parallel blocks to be managed by a large number of parallel threads. parallel blocks are the units of shared- and distributed-memory parallel computation, communication, and migration in the memory/storage hierarchy. Threads execute close to data using near-data synchronization according to the PGAS model. A use case is also discussed showing the DCEx features for Exascale programming.

关键词： Large-scale parallelism Exascale systems Data-centric applications

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The Entry-Extensible Cuckoo Filter 17th

The Entry-Extensible Cuckoo Filter

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

作者： Yu, Shuiying Wu, Sijie Chen, Hanhua Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9783030794774

The emergence of large-scale dynamic sets in real applications brings severe challenges in approximate set representation structures. A dynamic set with changing cardinality requires an elastic capacity of the approximate set representation structure, while traditional static structures, e.g., bloom filter, cuckoo filter, and their variants cannot satisfy the requirements. Existing dynamic approximate set representation structures only provide filter-level extensions, which require a single membership query to probe all discrete filters one by one. The large number of small discrete memory accesses takes up the vast majority of query time and results in unsatisfied query performance. To address the problem, in this work we propose the entry-extensible cuckoo filter (E2CF) to reduce memory access overhead for dynamic set representation and accelerate the membership query. E2CF utilizes adjacent buckets with continuous physical addresses in a cuckoo filter to extend bucket entries, which avoids many discrete memory accesses in a query. To further make E2CF space and time efficient, we adopt asynchronous extension and fine-grained splitting methods. Experiment results show that compared to state-of-the-art designs, E2CF reduces the query and insertion time by 82% and 28%, respectively. © 2021, IFIP international Federation for Information Processing.

关键词： Data structures

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