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distributed Graph processing: Techniques and Systems

Distributed Graph Processing: Techniques and Systems

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Joint International conference on Web and Big Data : Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM)

作者： Zhang, Yanfeng Wang, Qiange Gong, Shufeng Northeastern Univ Shenyang Peoples R China

ISBN: (纸本)9789811604782;9789811604799

During the past 10 years, there has been a surging interest in developing distributed graph processing systems. This tutorial provides a comprehensive review of existing distributed graph processing systems. We firstly review the programming models for distributed graph processing and then summarize the common optimization techniques for improving graph execution performance, including graph partitioning methods, communication mechanisms, parallel processing models, hardware-specific optimizations, and incremental graph processing. We also present an emerging hot topic, distributed Graph Neural Networks (GNN) frameworks, and review recent progress on this topic.

关键词： Graph processing distributed systems parallel models

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RDF Data Partitioning for Efficient SPARQL Query processing with Spark SQL 25th

RDF Data Partitioning for Efficient SPARQL Query Processing...

引用

25th International conference on Information Integration and Web Intelligence, iiWAS 2023

作者： Yamasaki, Kosuke Amagasa, Toshiyuki Graduate School of Science and Technology University of Tsukuba Tsukuba Japan Center for Computational Sciences University of Tsukuba Tsukuba Japan

ISBN: (纸本)9783031483158

In the age of big data, the volume of RDF data has been exploding due to the growing demands for open data, including Linked Open Data (LOD), semantic data processing, and knowledge graphs. Large-scale RDF data may contain millions to hundreds of millions of triples, comprising subject, predicate, and object, making fast query processing on such datasets challenging. To address this issue, distributed parallel processing systems like Apache Spark has been successfully used. One of the key issues in such systems is to partition the data to maximize performance while balancing the load and minimizing communication between processing nodes by taking into account the dataset’s characteristics and the workload. In this study, we propose a method of RDF data partitioning for efficient query processing by Spark SQL. We exploit the statistics of RDF data and the workload information representing typical user queries, allowing us to group strongly related RDF triples into the same partition. Moreover, we employ indexes whereby only the necessary partitions are loaded for answering a query, reducing the amount of data to be processed and improving query processing performance. Our evaluation experiments showed that the proposed scheme outperformed the comparative methods in table load time and query time for most benchmark queries in a single-node setting. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Query processing

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Towards a parallel Benchmark for Space Applications: Distributing OBPMark’s image processing

Towards a Parallel Benchmark for Space Applications: Distrib...

引用

Data Handling & Data processing conference (EDHPC), European

作者： Mahmoud M. Elbarrawy Carlos Gonzalez Cortes Andreas Lund Daniel Lüdtke Institute for Software Technology German Aerospace Center (DLR) Braunschweig Germany Institute for Software Technology German Aerospace Center (DLR) Weßling Germany

Modern space applications require high computing power and high reliability from on-board processors. To meet these requirements, the German Aerospace Center (DLR) is developing a Scalable On-board Computer for Space Avionics (ScOSA) system with a distributed non-shard memory architecture. As performance is an important criterion in the selection of hardware for space missions, the European Space Agency has published an open source benchmark suite called OBPMark. It is a set of benchmarks based on typical space applications and designed to measure system-level performance. However, there is currently no standard tool for evaluating the performance of distributed on-board computers. In this paper, we propose a parallelization strategy for running the OBPMark image processing benchmark on a distributed on-board computer. We used a split-map-reduce model to integrate the #1.1 image calibration and correction benchmark of OBPMark into the ScOSA system. We evaluated the developed distributed benchmark on the existing ScOSA High Performance Nodes (HPNs) consisting of 5 Xilinx Zynq 7020 SoCs. The results show a significant reduction of the benchmark execution time from 9.0 to 2.8 seconds using 5 nodes. In the case of dual-core with 4 nodes, the execution time was reduced to 2.5 seconds. We conclude that OBPMark is a valuable tool for evaluating the performance of distributed on-board computers with non-shared memory architectures and contributes to the standardisation of performance evaluation in the space domain.

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Boosting Performance of Iterative Applications on GPUs: Kernel Batching with CUDA Graphs

Boosting Performance of Iterative Applications on GPUs: Kern...

引用

Euromicro conference on parallel, distributed and Network-Based processing

作者： Jonah Ekelund Stefano Markidis Ivy Peng KTH Royal Institute of Technology Sweden

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

Graphics processing Units (GPUs) have become the standard in accelerating scientific applications on heterogeneous systems. However, as GPUs are getting faster, one potential performance bottleneck with GPU-accelerated applications is the overhead from launching several fine-grained kernels. CUDA Graph addresses these performance challenges by enabling a graph-based execution model that captures operations as nodes and dependence as edges in a static graph. Thereby consolidating several kernel launches into one graph launch. We propose a performance optimization strategy for iteratively launched kernels. By grouping kernel launches into iteration batches and then unrolling these batches into a CUDA Graph, iterative applications can benefit from CUDA Graph for performance boosting. We analyze the performance gain and overhead from this approach by designing a skeleton application. The skeleton application also serves as a generalized example of converting an iterative solver to CUDA Graph, and for deriving a performance model. Using the skeleton application, we show that when unrolling iteration batches for a given platform, there is an optimal size of the iteration batch, which is independent of workload, balancing the extra overhead from graph creation with the performance gain of the graph execution. Depending on workload, we show that the optimal iteration batch size gives more than $1.4 \times$ speed-up in the skeleton application. Furthermore, we show that similar speed-up can be gained in Hotspot and Hotspot3D from the Rodinia benchmark suite and a Finite-Difference Time-Domain (FDTD) Maxwell solver.

关键词： Graphics processing units Performance gain Boosting Skeleton Iterative methods Kernel Time-domain analysis Optimization Standards Finite difference methods

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Deep learning based image processing for proactive data collecting system for autonomous vehicle 21

Deep learning based image processing for proactive data coll...

引用

21st ACIS International Winter conference on Software Engineering, Artificial Intelligence, Networking and parallel/distributed Computing (SNPD-Winter)

作者： Trong-Hop Do Ngan-Linh Nguyen Hoang-Thong Vo Thanh-Binh Nguyen Ngo Tan Vu Khanh Univ Informat Technol Ho Chi Minh City Vietnam Vietnam Natl Univ Ho Chi Minh City Vietnam Univ Econ Ho Chi Minh City Vietnam

ISBN: (纸本)9781665418430

Autonomous vehicle is the topic that gains a lot of attentions in recent years. To enable autonomous vehicle, a huge amount of data needs to be collected and processed. One of the most promising method to improve the accuracy of data collection is using vehicle communication. With the help of vehicle communication, controlling and warning signal can be transmitted between vehicles. Consequently, necessary information such as identity, position, speed, moving direction, future behavior can be obtained with higher accuracy. This paper focuses on a type of vehicle communication system called optical camera communication. One of the most important components of optical camera communication system is image processing. In this paper, a deep learning-based image processing algorithm is proposed for optical camera communication used for proactive data collecting system for autonomous vehicle. The experiment results show that the proposed algorithm can achieve much higher performance compared to its counterparts.

关键词： vehicular communication camera LED region detection deep learning

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Research on big data processing framework algorithm optimized by deep learning

Research on big data processing framework algorithm optimize...

引用

Electrical, Automation and Computer Engineering (ICEACE), International conference on

作者： Shuo Ma Business School Shandong University of Political Science and Law Jinan China

ISBN: (数字)9798350368208

ISBN: (纸本)9798350368215

This paper proposes a deep learning Optimization big data processing framework based on Hadoop distributed architecture, Convolutional Neural Network (CNN) and Whale Optimization Algorithm (WOA). First, the Hadoop distributed file system (HDFS) and MapReduce technologies are used to store and preprocess large-scale data in parallel, and reduce data redundancy and processing delay through efficient data distribution and cleaning. Secondly, as a feature extraction module, the CNN model extracts the spatial features of the data layer by layer by using multi-layer convolution and pooling operations to provide reliable feature representation for subsequent classification and prediction tasks. In order to further improve the performance of the model, this paper introduces whale optimization algorithm (WOA) to optimize the hyperparameters of the CNN model (such as learning rate, convolution kernel size, etc.). By simulating the hunting behavior of whales, WOA automatically adjusts the combination of hyperparameters during model training, thus significantly improving the accuracy and training efficiency of the model. The experimental results show that the proposed Hadoop-CNN-WOA framework has higher accuracy than the traditional single model in processing large-scale image classification tasks.

关键词： Training Deep learning Convolution Computational modeling distributed databases Big Data Feature extraction Whale optimization algorithms Classification algorithms Convolutional neural networks

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Optimized methods on comparator design

Optimized methods on comparator design

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2022 IEEE Asia-Pacific conference on image processing, Electronics and Computers, IPEC 2022

作者： Ren, Xinyi Yu, Yajing Zhai, Guangxun Zhang, Ziming School of Electrical Engineering and Automation Huazhong University of Science and Technology Wuhan China Glasgow College University of Electronic Science and Technology of China Chengdu China The Grainger College of Engineering University of Illinois Urbana Champaign Champaign United States

ISBN: (纸本)9781665409025

This paper introduces four types of optimization methods on comparators to improve its performance. The first comparator adopts a triple-latch feedforward design, performing a much lower delay less than 26.8ps. It dissipates 2.2mW at 13.5Gb/s, holding a wide common mode and supply range in less than 70ps delay. Compared to the conventional version with an offset standard deviation of only 2.05mV, the second one improves the speed of the comparator by adding additional parallel paths to the output node, which increases around 50% speed. The third comparator uses a PMOS transistor at the input of the preamplifier of the comparator and the latch stage controlled by a local clock generator, which sets the optimum delay for preamplification at a 30% higher speed and decreases power consumption up to 50%. The fourth comparator presents two designs. Referred to the conventional strong-arm latch topology, the first one proposes a DB integration using a pair of tail capacitor to reach a lower input common-mode voltage and a better power efficiency. However, the design becomes infeasible in specific extreme input voltage corner. To solve the drawback, it puts forward a floating inverter pre-amplifier with reservoir capacitor, which saves seven times energy efficiency to the conventional design. © 2022 IEEE.

关键词： Comparators (optical)

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EgpuIP: An Embedded GPU Accelerated Library for image processing 24

EgpuIP: An Embedded GPU Accelerated Library for Image Proces...

引用

24th IEEE International conference on High Performance Computing and Communications, 8th IEEE International conference on Data Science and Systems, 20th IEEE International conference on Smart City and 8th IEEE International conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Wang, Luhan Jia, Haipeng Zhang, Yunquan Li, Kun Wei, Cunyang Institute of Computing Technology Chinese Academy of Sciences State Key Lab of Processors Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Microsoft Research China

ISBN: (纸本)9798350319934

With the advances of embedded GPUs' programming models like GLES and OpenCL, the mobile processor has gained more parallel computing capability, which enables real-time image processing on portable devices. GLES is an excellent option to implement high-performance image processing on embedded GPUs due to its low hardware overhead. However, most GLES studies only focus on porting specific algorithms to embedded GPUs without a general optimization guide. In order to address the pending problems, this paper presents effective performance optimization chains to guide the optimization of image processing algorithms by using GLES. The image processing algorithms can be divided into three modes including data-independent, data-sharing and data-related according to their memory access and calculation characteristics. Based on this classification, our optimization chains contain four optimization directions. 1) Optimizing access to off-chip memory for the memory-bound data-independent algorithm;2) Exploiting data locality by utilizing shared memory and cache for the data-sharing algorithm;3) Redesigning the algorithm to optimize the sharing of computational results between threads for the data-related algorithm;4) Making full use of computation resource for the above three algorithms. Based on these optimization chains, we design an embedded GPU accelerated image processing library, EgpuIP. To evaluate specific optimization methods and performance improvements, we employ histogram equalization, Gaussian pyramid and integral filter as the representative algorithm from EgpuIP and adequately optimize them guided by the proposed optimization chains. Compared to the OpenCV, experiments show that the three algorithms in EgpuIP provide up to 19×88×, and 3× speedup respectively. © 2022 IEEE.

关键词： Graphics processing unit

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Efficient, Reliable, and Scalable distributed Data processing Based on Hadoop

Efficient, Reliable, and Scalable Distributed Data Processin...

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Power, Electrical Engineering, Electronics and Control (PEEEC), International conference on

作者： Yingwei Liang Taipeng Zhu Chenghui Liu Yue Chen Yufei Yang CSG Guangdong Power Grid Co. Ltd. Information Center Guangzhou Guangdong China

ISBN: (数字)9798350378917

ISBN: (纸本)9798350378924

With the rapid growth of information technology and the intelligent upgrading of terminal devices, the amount of data in modern society is showing an explosive growth trend. These data are not only large-scale, but also structurally diverse. This data characteristic poses significant challenges to traditional data processing and storage methods. Although traditional distributed database and data warehouse technologies can to some extent cope with large-scale data processing, efficiency and flexibility are still limited when dealing with unstructured and massive data. In this context, the emergence of Hadoop and Map Reduce technologies has provided new solutions for large-scale data processing. Hadoop has become one of the mainstream technologies for handling big data due to its simple programming model, good scalability, and powerful fault tolerance. This article designs a distributed data processing system based on Hadoop, which fully utilizes the efficiency, high reliability, and high scalability of Hadoop, aiming to meet the needs of large-scale data processing. The experimental results show that the system in this paper has demonstrated excellent performance and stability in practical applications, and can process massive data, providing an effective solution for large-scale data processing.

关键词： Fault tolerance Scalability Fault tolerant systems distributed databases Programming parallel processing Data processing Reliability engineering Stability analysis Real-time systems

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Infrared and Visible image Fusion Based on Contrast Enhancement and Multi-scale Edge-preserving Decomposition

Infrared and Visible Image Fusion Based on Contrast Enhancem...

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Information Science, parallel and distributed Systems (ISPDS), International conference on

作者： Haoran Zhu Wenying Zhang School of Electronics and Information Engineering Changchun University of Science and Technology Changchun China Institute for Interdisciplinary Quantum Information Technology Jilin Engineering Normal University Changchun China

ISBN: (数字)9798350352719

ISBN: (纸本)9798350352726

To improve the contrast of details, an infrared and visible image fusion method based on contrast enhancement and multi-scale edge-preserving decomposition is proposed. A multi-scale contrast adaptive enhancement algorithm is used to improve the visibility of visible images before the fusion. The contrast of details can be effectively enhanced with this method. Then, fusion rules are designed according to the properties of different layers to preserve the restored information as much as possible. In order to retain more texture information of source images, the parameter-adaptive model is applied to fuse the small-scale detail layer. In order to avoid mutual interference of different characteristic images, a new edge detection method is used to fuse large-scale detail layer. In order to improve the contrast of fusion results, the weighted averaging technique based on visual saliency map is used to fuse the basic layer. Finally, a clear image is obtained by combining the fused layers. The experimental results show that the proposed method has better visual effect than other fusion methods.

关键词： Information science Fuses image edge detection Lighting Interference Visual effects Information filters

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