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Performance Analysis of parallelized PageRank Algorithm using OpenMP, MPI and CUDA

Performance Analysis of Parallelized PageRank Algorithm usin...

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2024 International conference on Smart Systems for Electrical, Electronics, Communication and Computer Engineering, ICSSEEC 2024

作者： Visali, V.S. Manimegalai, R. Noor Mahammad, S.K. Sunitha Nandhini, A. PSG Institute of Technology and Applied Research Department of Cse Tamilnadu India Indian Institute of Information Technology Design and Manufacturing Department of Cse Tamilnadu India

ISBN: (数字)9798350378177

ISBN: (纸本)9798350378177

Web search engines have developed into a crucial tool for effectively and quickly locating information among the vast web data. The PageRank algorithm is essential for web search as it measures the importance and relevance of web pages based on their incoming links, allowing search engines to rank results by prioritizing high-quality and authoritative content. This ensures that users receive more accurate and valuable information. The PageRank algorithm improves search engine results by assigning a numerical weight to each element in a hyperlinked set of documents, effectively measuring the importance of web pages based on quantity and quality of links pointing to them. With billions of web pages and an extensive network of hyperlinks, the traditional sequential computation of PageRank becomes impractical for timely and efficient processing. parallelization allows the algorithm to be distributed across multiple processors or computing nodes, enabling simultaneous computation of PageRank scores for different web pages. The main objective of this work is to parallelize the PageRank algorithm using OpenMP, MPI and CUDA and to compare their execution time to find the optimal one. OpenMP simplifies shared-memory parallelism, MPI facilitates communication between distributed processes and CUDA harnesses GPU power for high-performance parallel processing in diverse parallel computing environments. The experimental results demonstrate notable performance enhancements through parallelization using different technologies: OpenMP improves the algorithm's performance by 49.7%, MPI by 62.4%, and CUDA by 84.3%. Hence, optimal results are found when the PageRank algorithm is parallelized using CUDA. © 2024 IEEE.

关键词： Graphics processing unit

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PEANUTS: A Persistent Memory-based network Unilateral Transfer System for Enhanced MPI-IO Data Transfer 30th

PEANUTS: A Persistent Memory-Based Network Unilateral Transf...

引用

30th European conference on parallel and distributed processing (Euro-Par)

作者： Hiraga, Kohei Tatebe, Osamu Univ Tsukuba Tsukuba Ibaraki Japan

ISBN: (纸本)9783031697654;9783031697661

This paper introduces PEANUTS, a system designed to improve I/O performance for Single Shared File (SSF) in high-performance computing scenarios. I/O performance evaluation of SSF on 100 nodes demonstrated write speeds of 2.47 TB/s, remote read speeds of 2.39 TB/s, and local read speeds of 7.75 TB/s. These outcomes are close to the hardware's performance limits and represent significant improvements of approximately 2.2 times, 2.3 times, and 7.5 times, respectively, compared to existing state-of-the-art systems. A major feature of PEANUTS is the integration of persistent memory with RDMA one-sided communication, supporting high-speed and low-latency data transfers without the need for separate storage servers on the compute nodes. This configuration allows all compute node CPU cores to be fully available for application processing. Seamless integration with the MPI runtime enables rapid data sharing through the MPI-IO interface. The advancements by PEANUTS suggest that utilizing large-scale SSF could solidify as the standard I/O framework in HPC, demonstrating a viable solution to overcome traditional performance limitations.

关键词： Persistent Memory One-Sided Communication Storage System MPI-IO Single Shared File High-Performance Computing

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WebInf: Accelerating WebGPU-based In-browser DNN Inference via Adaptive Model Partitioning 29

WebInf: Accelerating WebGPU-based In-browser DNN Inference v...

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29th IEEE International conference on parallel and distributed Systems, ICPADS 2023

作者： Dong, Bing Liu, Tianen Li, Borui Zhou, Xiaolei Wang, Shuai Xu, Zhao-Dong Southeast University School of Computer Science and Engineering China National University of Defense Technology The Sixty-Third Research Institute China Southeast University School of Civil Engineering China

ISBN: (纸本)9798350330717

Artificial intelligence (AI) model inference performance in browsers is constrained, and transmitting data to the server consumes substantial transfer time by cloud computing. In this paper, we investigate the status quo of cloud and browser processing and explore model computation partitioning methods. Our study is rooted in WebGPU and employs the *** framework, encompassing seven AI models spanning computer vision, natural language processing, and automatic speech recognition domains. Leveraging the characteristics of neural network layers, we find a significant performance boost through a method that partitions AI models at layer granularity. We design a system called WebInf to partition AI models at layer granularity between the browser and server for faster inferencing-based adaptive model partitioning. WebInf supports diverse hardware, wireless networks, neural network structures, servers, and adaptive partitioning models for optimal inference performance. We evaluate WebInf on two laptops and servers, demonstrating that WebInf yields inference time improvements of 30% and 52%, respectively, when compared to separate inference execution in servers and browsers. The improvements can even peak at 33% and 69% respectively. © 2023 IEEE.

关键词： Edge device Model partitioning Neural network WebGPU

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DAG-based Efficient parallel Scheduler for Blockchains: Hyperledger Sawtooth as a Case Study 29th

DAG-Based Efficient Parallel Scheduler for Blockchains: Hype...

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29th International conference on parallel and distributed Computing (Euro-Par)

作者： Piduguralla, Manaswini Chakraborty, Saheli Anjana, Parwat Singh Peri, Sathya Indian Inst Technol Hyderabad Kandi 502284 Telangana India

ISBN: (纸本)9783031396977;9783031396984

Blockchain technology is a distributed, decentralized, and immutable ledger system. It is the platform of choice for managing smart contract transactions (SCTs). Smart contracts are pieces of code that capture business agreements between interested parties and are commonly implemented using blockchains. A block in a blockchain contains a set of transactions representing changes to the system and a hash of the previous block. The SCTs are executed multiple times during the block production and validation phases across the network. In most of the existing blockchains, transactions are executed sequentially. In this work, we propose a parallel direct acyclic graph (DAG) based scheduler module for concurrent execution of SCTs. This module can be seamlessly integrated into the blockchain framework, and the SCTs in a block can be executed efficiently, resulting in higher throughput. The dependencies among the SCTs of a block are represented as a DAG data structure which enables parallel execution of the SCTs. Furthermore, the DAG data structure is shared with block validators, allowing resource conservation for DAG creation across the network. To ensure secure parallel execution, we design a secure validator capable of validating and identifying incorrect DAGs shared by malicious block producers. For evaluation, our framework is implemented in Hyperledger Sawtooth V1.2.6. The performance across multiple smart contract applications is measured for the various schedulers. We observed that our proposed executor exhibits a 1.58 times performance improvement on average over serial execution.

关键词： Smart Contract Executions Blockchains Hyperledger Sawtooth parallel Scheduler

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MESDD: A distributed Geofence-based Discovery Method for the Computing Continuum 29th

MESDD: A Distributed Geofence-Based Discovery Method for the...

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29th International conference on parallel and distributed Computing (Euro-Par)

作者： Horvath, Kurt Kimovski, Dragi Uran, Christoph Woellik, Helmut Prodan, Radu Univ Klagenfurt Inst Informat Technol Klagenfurt Austria Carinthian Univ Appl Sci Fac Engn & IT Klagenfurt Austria

ISBN: (纸本)9783031396977;9783031396984

Service discovery is a vital process that enables low latency provisioning of Internet of Things (IoT) applications across the computing continuum. Unfortunately, it becomes increasingly difficult to identify a proper service within strict time constraints due to the high heterogeneity of the computing continuum. Moreover, the plethora of network technologies and protocols commonly used by IoT applications further hinders service discovery. To address these issues, we introduce a novel Mobile Edge Service Discovery using the DNS (MESDD) algorithm, which uses a so-called Intermediate Discovery Code to identify suitable service instances. MESDD uses geofences for fine-grained service segmentation based on a naming scheme that identifies users' locations across the computing continuum. We deployed a real-life distributed computing continuum testbed and compared MESDD with three related methods, outperformed by 60% after eight update iterations.

关键词： Fog and Edge computing service discovery geofence DNS

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A Multi-attention based CNN-BiLSTM Intrusion Detection Model for In-vehicle networks 21

A Multi-attention Based CNN-BiLSTM Intrusion Detection Model...

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21st IEEE International Symposium on parallel and distributed processing with Applications, 13th IEEE International conference on Big Data and Cloud Computing, 16th IEEE International conference on Social Computing and networking and 13th International conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Gao, Kai Huang, Hao Liu, Linhong Du, Ronghua Zhang, Jinlai Changsha University of Science & Technology College of Automotive and Mechanical Engineering Changsha410114 China

ISBN: (纸本)9798350329223

The development of vehicular networking technology continuously enhances the internet connectivity of modern vehicles. However, for in-vehicle networks, constant communication with the outside world dramatically increases vehicle security risks, posing serious security threats to automotive systems and occupants. This paper proposes a CNN-BiLSTM vehicular network intrusion detection model based on multi-head attention by analyzing the information security vulnerabilities in the Controller Area network (CAN) bus. By extracting only the communication features of CAN messages, including CAN ID and Data field, this paper conducted a multiclass classification study on the real-time traffic dataset CarHacking. The model achieved a detection accuracy of 99.992%. The reduction of features effectively simplifies the model and improves its computation efficiency. © 2023 IEEE.

关键词： Anomaly detection

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Quantum Information Technology for network Security: An Application-Focused Overview 21

Quantum Information Technology for Network Security: An Appl...

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作者： Wang, Huan Lai, XiaoYang China Electronics Technology Group Corporation Beijing China China Innovation Fund Management Co. Ltd. Beijing China

ISBN: (纸本)9798350329223

In recent years, Quantum information technology (QIT) has accelerated its development in academic research with continuously highlights and achievements that have attracted the attention of the entire society. network security is currently the main application field of QIT and two types of quantum security products have been released: Quantum Key Distribution systems and Quantum Random Number Generators. This article introduces the two quantum security products and reveals that the application of QIT in network security is currently in its initial stage, consisting of a total of six stages. Both product and market sides need continuous innovation and development, and it may take time for QIT to truly manifest significant commercial value. It also offers prospects for the technological evolution of the two quantum products in the further process of industrialization, mainly involving the multiplexing of QKD with classical optical communication and the chip based QRNG. The aim of this study is to consolidate consensus and contribute to the industrial application of QIT in network security. © 2023 IEEE.

关键词： network security

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Anomaly Detection with Graph Attention network for Multimodal IoT Data Monitoring 29

Anomaly Detection with Graph Attention Network for Multimoda...

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29th IEEE International conference on parallel and distributed Systems, ICPADS 2023

作者： Li, Haoyu Gao, Yi Dong, Wei Zhejiang University College of Computer Science China

ISBN: (纸本)9798350330717

The popularity of the wireless network and the embedded system has promoted the widespread application of the Internet of Things (IoT) monitoring system which detects anomalies for discovering emergency events to avoid losses by deploying various sensors. In the face of anomaly detection algorithms for multi-dimensional data in the IoT monitoring system, the currently popular method is to implement a model based on the graph neural network (GNN). Graph attention network (GAT), a mutant of GNN, is widely used because of the attention mechanism in capturing multi-dimensional nonlinear features between nodes. However, the GAT's weakness in processing complex temporal features leads to the fact that anomalies with distinctive temporal features are hard to detect. In this paper, we propose Time-Series Graph Attention network (TSGAT), an innovative GAT-based model for anomaly detection. Given the simplified attention mechanism of the GAT and its weakness in extracting temporal pattern features, we embed the time-series layer to enhance the ability to process time-series data. In addition, we designed the loss function and anomaly scoring mechanism based on data reconstruction features, multidimensional features, and temporal features. TSGAT achieves the improvement of 7%-8% F1 score on two classical public datasets for IoT monitoring: SWaT and WADI, compared with the state-of-the-art GNN-based anomaly detection model. © 2023 IEEE.

关键词： anomaly detection GAT multimodal IoT data

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PriCE: Privacy-Preserving and Cost-Effective Scheduling for parallelizing the Large Medical Image processing Workflow over Hybrid Clouds 30th

PriCE: Privacy-Preserving and Cost-Effective Scheduling for ...

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30th European conference on parallel and distributed processing (Euro-Par)

作者： Wang, Yuandou Kanwal, Neel Engan, Kjersti Rong, Chunming Grosso, Paola Zhao, Zhiming Univ Amsterdam Multiscale Networked Syst Amsterdam Netherlands Univ Stavanger Dept Elect Engn & Comp Sci Stavanger Norway

ISBN: (纸本)9783031695766;9783031695773

Running deep neural networks for large medical images is a resource-hungry and time-consuming task with centralized computing. Outsourcing such medical image processing tasks to hybrid clouds has benefits, such as a significant reduction of execution time and monetary cost. However, due to privacy concerns, it is still challenging to process sensitive medical images over clouds, which would hinder their deployment in many real-world applications. To overcome this, we first formulate the overall optimization objectives of the privacy-preserving distributed system model, i.e., minimizing the amount of information about the private data learned by the adversaries throughout the process, reducing the maximum execution time and cost under the user budget constraint. We propose a novel privacy-preserving and cost-effective method called PriCE to solve this multi-objective optimization problem. We performed extensive simulation experiments for artifact detection tasks on medical images using an ensemble of five deep convolutional neural network inferences as the workflow task. Experimental results show that PriCE successfully splits a wide range of input gigapixel medical images with graph-coloring-based strategies, yielding desired output utility and lowering the privacy risk, makespan, and monetary cost under user's budget.

关键词： Privacy Cost-effectiveness Hybrid Clouds Medical Image splitting Multi-Objective Optimization Scheduling

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Credit-based Differential Privacy Stochastic Model Aggregation Algorithm for Robust Federated Learning via Blockchain 23

Credit-based Differential Privacy Stochastic Model Aggregati...

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52nd International conference on parallel processing (ICPP)

作者： Du, Mengyao Zhang, Miao Liu, Lin Xu, Kai Yin, Quanjun Natl Univ Def Technol Changsha Peoples R China

ISBN: (纸本)9798400708435

By encapsulating model parameters in blocks when training machine learning models collaboratively, blockchain is recognized as a promising enabling technology to facilitate reliable federated learning under a distributed and untrusted environment. However, storing updated models of each worker in blockchain induces potential privacy risks, such as membership inference attacks. Besides, the volatile network conditions in the distributed environment may cause the deterioration of system robustness. This paper aims at addressing the privacy and robustness issues mentioned above. Specifically, a Credit-based Differential Privacy stochastic model aggregation algorithm combined with SIGN operation (Cre-DPSIGN) is adopted in our peer-to-peer network, which can realize the tradeoff between privacy and accuracy. Furthermore, leveraging the transparency and tamper-proofing of blockchain, we design practical and reliable smart contracts for unbiased sampling based on the credit of workers to improve system robustness against Byzantine workers. In addition, we have demonstrated that the use of biased differential privacy mechanisms can lead to performance degradation. Therefore, we have introduced two unbiased differential privacy mechanisms and have proven their convergence and privacy guarantee. Extensive experiments conducted on MNIST datasets show that our algorithm can achieve 1/3 byzantine fault tolerance rate with a private loss is an element of = 0.4. Compared with the state-of-the-art, aka DP-RSA (IJCAI-22), Cre-DPSIGN shows lower privacy loss consumption and better system robustness.

关键词： federated learning blockchain differential privacy robustness

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