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检索条件"任意字段=International Conference on Parallel and Distributed Processing Techniques and Applications"

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Microstructural and mechanical characterization of parallel layered WC- NiCr weld overlay on 080 M40 steel substrate prepared using additive manufacturing

Microstructural and mechanical characterization of parallel ...

引用

5th international conference on Advances in Steel, Power and Construction Technology

作者： Kumar, K. Varun Phani, M. Kalyan OP Jindal Univ Dept Met Engn Raigarh 496109 Chhattisgarh India

080 M40 Medium carbon steel has potential applications in axles, shafts, bolts, studs, spindles, automotive components and many more. In high friction applications these steels possess better wear resistance, and after weld overlay, they can exhibit superior properties. Additive manufacturing (AM) is an advanced method in which material in the form of powder or wire is used to form a desired shape. Laser Metal Deposition (LMD) is an AM method which is used in industrial application to recreate, repair and produce corrosion resistance weld overlay on a part. In comparison with conventional overlay techniques such as Plasma Transfer Arc (PTA), Metal Inert Gas (MIG) & Tungsten Inert Gas (TIG);Laser weld overlays are faster techniques which can make thin sections with good metallurgical bond and with high productivity and flexibility. Apart from that it has also advantage of low heat affected zone, minimum metal dilution, and can produce thin layers with good aesthetics. In this paper, 080 M40 steel samples were used as substrate and WC- NiCr powder was weld overlayed using LMD method. Such kind of weld overlays will be had potential application in the field of mining. The weld overlay was deposited in parallel layered fashion and effect of set of parameters like feed rate, dilution, size of the bead and processing speed were used to produce weld overlays of 1 mm thickness. metallurgical, micro structural and mechanical properties of these weld overlay materials have been investigated. These properties are very important in the processing of mining tools as well as other parts produced using the LMD process and also for other future applications. Further effect of laser processing parameters on the resulting properties of these steels weld overlays are discussed. (c) 2022 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 5th international conference on Advances in Steel, Power and Construction Technology

关键词： Laser metal deposition Additive manufacturing Laser weld overlay Carbide distribution WC-NiCr

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Split-Knit Convolution: Enabling Dense Evaluation of Transpose and Dilated Convolutions on GPUs 29

Split-Knit Convolution: Enabling Dense Evaluation of Transpo...

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29th Annual IEEE international conference on High Performance Computing, Data, and Analytics (HiPC)

作者： Vadakkeveedu, Arjun Menon Mandal, Debabrata Ramachandran, Pradeep Chandrachoodan, Nitin Indian Inst Technol Dept Elect Engn Madras Tamil Nadu India KLA Corp AI & Adv Comp Lab ACL Chennai Tamil Nadu India

ISBN: (纸本)9781665494236

Transpose convolutions occur in several imagebased neural network applications, especially those involving segmentation or image generation. Unlike regular (forward) convolutions, they result in data access and computation patterns that are less regular, and generally have poorer performance when implemented in software. We present split-knit convolution (SKConv) - a technique to replace transpose convolutions with multiple regular convolutions followed by interleaving. We show how existing software frameworks for GPU implementation of deep neural networks can be adapted to realize this computation, and compare against the standard techniques used by such frameworks.

关键词： Convolutional Neural Networks Graphics processing Units Transpose Convolutions

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PEG: A Partial Evaluation-based distributed RDF Graph System 28th

PEG: A Partial Evaluation-based Distributed RDF Graph System

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28th international conference on Database Systems for Advanced applications, DASFAA 2023

作者： Ji, Shengyi Peng, Peng Hu, Jian Zou, Lei Huang, Zhen Qin, Zheng Hunan University Changsha China Peking University Beijing China National University of Defense Technology Changsha China

ISBN: (纸本)9783031306778

In this demo, we implement a Partial Evaluation-based distributed RDF Graph system (PEG for short), which can implement partial evaluation without modifying the single-machine RDF graph system at each site. When a query is newly input, PEG rewrites it into a set of local partial subqueries. Then, each local partial match defined in the framework of partial evaluation can be also found by evaluating one of the local partial subqueries. To further prune some unnecessary results of local partial subqueries, PEG also associates each vertex with an attribute to specify whether it belongs to the partition at the site. In this demo, we demonstrate the main features of PEG and show how PEG loads and queries large RDF graphs in a distributed environment. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Resource Description Framework (RDF)

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How to Fit the SCC Algorithm Efficiently into distributed Graph Iterative Computation

How to Fit the SCC Algorithm Efficiently into Distributed Gr...

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IEEE Annual international Computer Software and applications conference (COMPSAC)

作者： Xiaochen Sun Wei Wang Tao Huang State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

This paper reviews the sequential, parallel and distributed implementations of strongly connected component algorithms, and analyzes the challenges of each implementation in the graph iteration paradigm of distributed processing. We also review the graph data layout and communication mode of each distributed graph processing system, and analyze the defect of high memory usage in the implementation of the strongly connected component algorithm of the existing distributed graph processing system. Therefore, we propose a strongly connected component algorithm that performs pull mode communication on CSR instead of traversing the transposed graph. Experiments show that the memory consumption of our method is greatly reduced, and the running time of the algorithm is much lower than that of the most advanced implementation. On four publicly accessible data sets, our approach reduces computation time and storage space by an average of 26% and 39%, respectively. In addition, we optimize the general pull communication mode, resulting in 15.2°/0 computation time reduction on the four publicly available data sets.

关键词： distributed processing Reviews Image color analysis Layout Memory management distributed databases Iterative algorithms

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Cloud-Driven AI: Enhancing Machine Learning Performance Through distributed Computing

Cloud-Driven AI: Enhancing Machine Learning Performance Thro...

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international conference on ICT in Business Industry & Government (ICTBIG)

作者： Kunal Dixit Himani Parul Bhardwaj Ritu Sachdeva School of Computer Applications Manav Rachna International Institute of Research & Studies Faridabad India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

The exponential data volume and complexity of Machine Learning (ML) algorithms has resulted in an increase in computational limitations, which affects artificial intelligence [AI]. This research seeks to establish whether cloud-based distributed computing can effectively address these limitations and improve the performance of ML. Our approach is a new architecture that deploys the computing load over a cloud framework in order to distribute and process in parallel some of the computational majorities of learning algorithms. To further enhance the application’s efficiency of processing the circuit data, a combination of adaptive resource allocation techniques, data partitioning strategies, and a new synchronization protocol has been proposed and implemented. This framework was assessed with deep neural networks, random forests, gradient-boosting machines to different datasets, and complex tasks. Focusing on the obtained results, it becomes possible to state that the proposed methods yield up to 87% improvement in the training speed and 5–12% increase in the accuracy of the models compared to the traditional implementations in single nodes. Moreover, our approach proved its capability of scalability did not degrade the performance and achieved near-linear speedup throughout the distributed nodes of up to 1000. These results propose that cloud-based AI can significantly reduce time to on-ramp for training numerous models on larger datasets. In light of this, distributed computing in the cloud offers a way to meet the computational demands of today’s AI applications while opening the door to new advancements in ML throughout different domains.

关键词： Training Machine learning algorithms Protocols Scalability Government Partitioning algorithms Synchronization Resource management distributed computing Random forests

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On GPU optimizations of stencil codes for highly parallel simulations 29

On GPU optimizations of stencil codes for highly parallel si...

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29th Euromicro international conference on parallel, distributed and Network-Based processing (PDP)

作者： Pfisterer, Nikolai Berghoff, Marco Streit, Achim Karlsruhe Inst Technol Steinbuch Ctr Comp Karlsruhe Germany

ISBN: (纸本)9781665414555

Stencil codes are valuable methods to solve partial differential equations of models in a wide range of applications in science and engineering. Graphics processing units (GPUs) provide a highly parallel architecture with fast directly accessible memory that is desirable to run stencil codes. They enable larger and more complex simulations that are solved faster compared to simulating on CPUs. We provide a solution for users to run highly parallel stencil codes on GPUs, that can also be efficiently used on a distributed GPU cluster. In this work, we present an extension to the multi-disciplinary framework NAStJA originally designed to efficiently run stencil codes on the CPUs in current high-performance computing (HPC) systems, to also be able to run on GPUs. We describe different methods to increase the performance of stencil codes on GPUs like a border exchange method which is transparent to the user, as well as a buffer for gradient values that are needed multiple times. We show their performance using the phase-field method as a stencil code example. With this GPU extension and optimizations implemented into the NAStJA framework, we can show highly improved performance compared to the CPU implementation, and an efficiency of 92% (weak scaling) for a large-scale example simulation on 64 GPUs on the ForHLR II HPC system.

关键词： GPU optimizations stencil code heterogeneous parallelization scalability analysis scalability analysis

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Adaptive AI-based Decentralized Resource Management in the Cloud-Edge Continuum

Adaptive AI-based Decentralized Resource Management in the C...

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Lanpei Li Jack Bell Massimo Coppola Vincenzo Lomonaco ISTI-CNR Pisa Italy Department of Computer Science University of Pisa Pisa Italy

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

In the Cloud-Edge Continuum, dynamic infrastructure change and variable workloads complicate efficient resource management. Centralized methods can struggle to adapt, whilst purely decentralized policies lack global oversight. This paper proposes a hybrid framework using Graph Neural Network (GNN) embeddings and collaborative multi-agent reinforcement learning (MARL). Local agents handle neighbourhood-level decisions, and a global orchestrator coordinates system-wide. This work contributes to decentralized application placement strategies with centralized oversight, GNN integration and collaborative MARL for efficient, adaptive and scalable resource management.

关键词： Training Scalability Collaboration Reinforcement learning Organizations Dynamic scheduling Decentralized applications Graph neural networks Robustness Resource management

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distributed Graph processing: techniques and Systems

Distributed Graph Processing: Techniques and Systems

引用

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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parallel K-means on GPU using Warp-Centric Strategies

Parallel K-means on GPU using Warp-Centric Strategies

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international conference on parallel and distributed Systems (ICPADS)

作者： Michel B. Cordeiro Wagner M. Nunan Zola Federal University of Parana Curitiba Brazil

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

K-means clustering is a popular unsupervised machine learning method widely used in various applications, such as data mining, image processing, and social sciences. However, clustering can be computationally expensive, especially when applied to large datasets. In this article, we explore the performance optimization of k-means clustering through GPU parallelization strategies using the CUDA framework. Due to a warp-centric approach, the proposed implementation is named warp-KMeans. We have compared warp-KMeans to other best-in-class GPU implementations of the k-means algorithm, including the top-tier NVIDIA RAPIDS, a widely used set of libraries for high-performance data analysis on GPUs. Throughout our experiments, we observed that warp-KMeans excels in all scenarios, achieving an acceleration of up to 8.4 compared to the NVIDIA RAPIDS algorithm, when the number of centroids is low, while also exhibiting good scalability and up to 2.94 speedup, concerning various other dataset sizes, higher dimensional points, and number of centroids.

关键词： Machine learning algorithms Scalability Social sciences Memory management Graphics processing units Clustering algorithms Machine learning parallel processing Labeling Optimization

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Load Balancing based Scheduling for Mixed Real Time Tasks on Multicore Systems

Load Balancing based Scheduling for Mixed Real Time Tasks on...

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international conference on parallel distributed and Grid Computing (PDGC)

作者： Shruti Jadon Pradyuman K.K. Urmil Kalaria K.R. Varsha Karthik Gupta Dept of Computer Science PES University Bengaluru India

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

Effective load balancing is essential for ensuring the efficient operation of multi-core systems, particularly in environments with diverse task priorities and deadlines. The primary objective is to intelligently distribute tasks among cores to optimize system responsiveness, prioritizing the timely completion of urgent tasks. Through rigorous computer simulations, this paper explores various load-balancing techniques tailored for multi-core systems operating in mixed real-time environments. Notably, the focus is on strategies applicable to such systems, excluding hardware-level optimizations and specific real-time scheduling algorithms. The paper introduces an algorithm aimed at enhancing both load balancing and response time. This involves leveraging the LBPSA (Load Balancing-based Partitioned Scheduling Algorithm) for load balancing and implementing the TBS (Total Bandwidth Server) migration method to further improve response time. The insights and findings presented offer valuable guidance for researchers, system designers, and practitioners involved in load balancing within mixed real-time multi-core systems across a broad spectrum of applications. The LBPSA and TBS methods are presented as contributions of the paper, enhancing the existing body of knowledge in load-balancing techniques for multi-core systems.

关键词： Scheduling algorithms Multicore processing Bandwidth Mean square error methods Load management Real-time systems Partitioning algorithms Time factors Servers Optimization

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