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Euromicro Conference on parallel, distributed and Network-Based processing

作者： Nikolai Pfisterer Marco Berghoff Achim Streit Steinbuch Centre for Computing Karlsruhe Institute of Technology Karlsruhe Germany

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 *** 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 *** 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： Partial differential equations Graphics processing units Clustering algorithms Performance gain parallel architectures Central processing Unit Mathematical model

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Task Distribution Method for Index Classification based on Color Segmentation in Remote Sensing 4

Task Distribution Method for Index Classification based on C...

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4th International Conference on Advanced Information Technologies, ICAIT 2020

作者： Htun, Yan Naing Aye, Bawin Aung, Aung Higher Education Center Department of Computer Technology Pyin Oo Lwin Myanmar

ISBN: (纸本)9781728183640

Digital processing of remotely sensed image data has been great importance in recent times. This research work discusses task distribution method in parallel image processing and load balancing under the circumstance of multi-Tasks and multi-processors in remote sensing. Task distribution method can speed up computation and improve efficiency and perform larger computations which are not possible on single processor system. The tasks are distributed based on the segmentation of color and the Support Vector Machine (SVM) is used to classify the indices of the input image and intends to design and improve the color segmentation based task distribution method for index classification using machine learning. In the system, the RGB satellite image is used as an input image and the output is the four indices of forest, building, road and land The results of the system are more accurate and less time consumption than non-distributed computing methods. It is implemented in MATLAB platform with parallel computation toolbox because the system can solve computationally and data-intensive tasks using multicore processors and clusters of computer. © 2020 IEEE.

关键词： image enhancement

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Communication-efficient decentralised algorithms for seismic tomography with sensor networks

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INTERNATIONAL JOURNAL OF parallel EMERGENT AND distributed SYSTEMS 2020年第5期35卷 550-570页

作者： Zhao, Liang Univ South Carolina Upstate Div Math & Comp Sci Spartanburg SC 29303 USA

We consider the decentralised consensus optimisation problem arising from in-situ seismic tomography in large-scale sensor networks. Unlike traditional seismic imaging performed in a centralised location, each node in this setting privately holds an objective function and partial data. The goal of each node is to obtain the optimal solution of the whole seismic image, while communicating only with its immediate neighbours. We present a fast decentralised gradient descent method and prove that this novel method can reach optimal convergence rate ofO(1/k(2))wherekis the number of communication/iteration rounds. A gossip-based asynchronous version is also proposed which is preferable when there is a divergence on the processing speed of the nodes. Extensive numerical experiments on synthetic and real-world sensor network seismic data demonstrate that the proposed algorithms significantly outperform existing methods. [GRAPHICS] .

关键词： Big data decentralised computing in-network processing seismic tomography sensor network

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An efficient and scalable SPARK preprocessing methodology for Genome Wide Association Studies 28

An efficient and scalable SPARK preprocessing methodology fo...

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28th Euromicro International Conference on parallel, distributed and Network-Based processing (PDP)

作者： Agapito, Giuseppe Guzzi, Pietro Hiram Cannataro, Mario Univ Magna Grcecia Catanzaro Dept Med & Surg Sci Data Analyt Res Ctr Catanzaro Italy

ISBN: (纸本)9781728165820

The importance of the use of high-performance software frameworks to analyze omics data obtained by using High-Throughput (HT) essays is widely recognized. HT methodologies comprise microarrays, Genome-Wide Association Studies (GWAS), and Next Generation Sequencing (NGS), which provide a vast amount of data per a single experiment. Each HT vendor provides to the users only the software frameworks and the proprietary libraries for the annotation, and summarization of raw data. Consequently, the needs of algorithms for the preprocessing and analysis of omics data arise. GWAS aims to highlight the association between genetic variants and diseases by examining single nucleotide polymorphisms (SNPs), which differ in a statistically significant way between cases and controls. The effectiveness of GWAS analysis increases with the number of analyzed samples per single experiment. GWAS data analyzed through the use of statistical methods can detect associations among a single allelic variant and the clinical conditions of samples. To overcome these limitations, and to make it possible to discover multiple associations among allelic variants, it is possible to use Association Rules mining. Consequently, the need for the introduction of scalable Association Rule Mining (ARM) algorithms able to analyze GWAS data arises. Hence, the use of high-performance data analytics framework is needed. For this purpose, we propose a software framework called GARMS (GWAS Association Rule Mining in Spark) built on top of Apache Spark for the preprocessing, and mining of association rules from GWAS data sets. GARMS comprises a two steps analysis methodology: (i) in the first step, the GWAS data are preprocessed, along with the identification of the frequent itemsets;(ii) in the second step, frequent itemsets are employed to mine association rules without scanning the input data. We implemented our algorithm, and we tested it on some synthetic GWAS data sets. Preliminary results confirm that ou

关键词： Apache Spark distributed Computing GWAS SNPs Preprocessing Association Rules

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TCM Constitution Analysis Method Based on parallel FP-Growth Algorithm in Hadoop Framework

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JOURNAL OF HEALTHCARE ENGINEERING 2022年第1期2022卷 9006096页

作者： Li, Mingzheng Lv, Xiaojuan Liu, Ye Wang, Lin Song, Jianqiang Henan Univ Sci & Technol Sch Informat Engn Luoyang 471003 Peoples R China Lushi Chinese Med Hosp Sanmenxia 472100 Lushi Cty Peoples R China PLA Rocket Force Characterist Med Ctr Informat Dept Beijing 100120 Peoples R China Henan Qunzhi Informat Technol Co Ltd Luoyang 471003 Peoples R China

This work is devoted to establishing a comparatively accurate classification model between symptoms, constitutions, and regimens for traditional Chinese medicine (TCM) constitution analysis to provide preliminary screening and decision support for clinical diagnosis. However, for the analysis of massive distributed medical data in a cloud platform, the traditional data mining methods have the problems of low mining efficiency and large memory consumption, and long tuning time, an association rules method for TCM constitution analysis (ARA-TCM) is proposed that based on FP-growth algorithm and the open-source distributed file system in Hadoop framework (HDFS) to make full use of its powerful parallel processing capability. Firstly, the proposed method was used to explore the association rules between the 9 kinds of TCM constitutions and symptoms, as well as the regimen treatment plans, so as to discover the rules of typical clinical symptoms and treatment rules of different constitutions and to conduct an evidence-based medical evaluation of TCM effects in constitution-related chronic disease health management. Secondly, experiments were applied on a self-built TCM clinical records database with a total of 30,071 entries and it is found that the top three constitutions are mid constitution (42.3%), hot and humid constitution (31.3%), and inherited special constitution (26.2%), respectively. What is more, there are obvious promotions in the precision and recall rate compared with the Apriori algorithm, which indicates that the proposed method is suitable for the classification of TCM constitutions. This work is mainly focused on uncovering the rules of "disease symptoms constitution regimen" in TCM medical records, but tongue image and pulse signal are also very important to TCM constitution analysis. Therefore, this additional information should be considered into further studies to be more in line with the actual clinical needs.

关键词： Association rules

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An Unsupervised Approach For Detecting Lateral Movement Logins Based On Knowledge Graph

An Unsupervised Approach For Detecting Lateral Movement Logi...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Xiaohan Ma Chen Li Bibo Tu Chinese Academy of Sciences Institute of Information Engineering Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China

The lateral movement that relies on privacy theft is receiving increasing attention. This type of attack can be achieved by Cross-Host Login. Constructing an authentication graph between hosts is a common way to detect lateral movement, but the previous graph-based detection methods lack an effective means to integrate the account information into graph-based features. We abstract the login events as edges on the knowledge graph and account as the relations on the edges. Then we utilize a graph auto-encoder model comprised of an entity encoder and a scoring function (decoder), where entities are represented as low-dimensional vectors learned from the relational graph neural network. The decoder scores the edges to get the likelihood of the edges belonging to normal behaviors. We also propose a novel negative sample approach to reduce the false alarm rate. We apply this technique to authentication data derived from a large-scale real-world environment. The results show our approach can detect malicious authentication events associated with lateral movement with a true positive rate of 95 % and false positive rate of 10%, compared to 85% and 0.9% by the previous random walk approach as a baseline.

关键词： Privacy image edge detection Authentication Knowledge graphs Feature extraction Resource description framework Graph neural networks

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NebulaFL: Effective Asynchronous Federated Learning for JointCloud Computing

arXiv

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arXiv 2024年

作者： Gao, Fei Hu, Ming Xie, Zhiyu Shi, Peichang Xie, Xiaofei Yi, Guodong Wang, Huaimin National Key Lab. of Parallel and Distributed Processing National University of Defense Technology Changsha China School of Computing and Information Systems Singapore Management University Singapore Xiangjiang Lab Changsha China

With advancements in AI infrastructure and Trusted Execution Environment (TEE) technology, Federated Learning as a Service (FLaaS) through JointCloud Computing (JCC) is promising to break through the resource constraints caused by heterogeneous edge devices in the traditional Federated Learning (FL) paradigm. Specifically, with the protection from TEE, data owners can achieve efficient model training with high-performance AI services in the cloud. By providing additional FL services, cloud service providers can achieve collaborative learning among data owners. However, FLaaS still faces three challenges, i.e., i) low training performance caused by heterogeneous data among data owners, ii) high communication overhead among different clouds (i.e., data centers), and iii) lack of efficient resource scheduling strategies to balance training time and cost. To address these challenges, this paper presents a novel asynchronous FL approach named NebulaFL for collaborative model training among multiple clouds. To address data heterogeneity issues, NebulaFL adopts a version control-based asynchronous FL training scheme in each data center to balance training time among data owners. To reduce communication overhead, NebulaFL adopts a decentralized model rotation mechanism to achieve effective knowledge sharing among data centers. To balance training time and cost, NebulaFL integrates a reward-guided strategy for data owners selection and resource scheduling. The experimental results demonstrate that, compared to the state-of-the-art FL methods, NebulaFL can achieve up to 5.71% accuracy improvement. In addition, NebulaFL can reduce up to 50% communication overhead and 61.94% costs under a target accuracy. Copyright © 2024, The Authors. All rights reserved.

关键词： Costs

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Customizing Super-Resolution Framework According to image Features 18

Customizing Super-Resolution Framework According to Image Fe...

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18th IEEE Int Symp on parallel and distributed Proc with Applicat (ISPA) / 10th IEEE Int Conf on Big Data and Cloud Comp (BDCloud) / IEEE Int Symp on Social Comp and Networking (SocialCom) / IEEE Int Conf on Sustainable Comp and Commun (SustainCom)

作者： Yuan, Ninghui Liu, Jingyu Wang, Qiong Shen, Li Natl Univ Def Technol Sch Comp Changsha Peoples R China

ISBN: (纸本)9781665414852

As a popular research field of computer vision, super resolution(SR) has received more and more attention in recent years. Although the deep learning methods have achieved good results in SR, there are still some problems. For example, the previous models are often based on single depth mechanism. This means that the SR reconstruction problem of all images is regarded as of equal complexity. And we found that some images have more details and are suitable for recovering in complex models, while other images have less texture information and are suitable for recovering in simple models. At the same time, the size of the training set is too large, which creats a lot of resource overhead. To solve these problems, this paper proposes a new SR framework can be customized according to image features. We choose 3 representative models for testing and in test our framework can reduce the size of the training set by 41.9%. For MSRResNet, we can reduce the training time from 2517 minutes to 2449 minutes. The reconstruction quality of 61% test images has been improved and the average perceptual index has dropped from 5.1912 to 5.155833, at the same time the reconstruction time has been optimized from 85 seconds to 59 seconds. For SRGAN, the training time can be reduced from 1920 minutes to 1812 minutes. The reconstruction quality of 58% test images have been improved and the average perceptual index has dropped from 2.0869 to 2.0509, while the reconstruction time has been optimized from 82 seconds to 46 seconds. For ESRGAN, the training time can be reduced from 4368 minutes to 4249 minutes. The reconstruction quality of 78% test images has been improved and the average perceptual index has dropped from 2.2041 to 2.0535. The reconstruction time has been optimized from 138 seconds to 95 seconds. Our framework can improve the effect of super-resolution models while reducing the resource overhead.

关键词： super-resolution framework random forest

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A novel experimental based statistical study for water management in proton exchange membrane fuel cells

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APPLIED ENERGY 2020年第0期264卷 114713-000页

作者： Hasheminasab, M. Kermani, M. J. Nourazar, S. S. Khodsiani, M. H. Amirkabir Univ Technol Dept Mech Engn 424 Hafez Ave Tehran 158754413 Iran Ctr Solar Energy & Hydrogen Res ZSW Helmholtz Str 8 D-89081 Ulm Germany

Water management is a key issue in low-temperature PEMFCs. For proper water management, processes leading to dehydration or flooding of cells should be clearly understood, hence prevented. Here, the effects of Anode Stoichiometry (AST), Cathode Stoichiometry (CST) and inlet gas temperature (T*) on the cell performance and water management in PEMFC are experimentally studied. Two methods, namely (i) direct- and (ii) indirect-method are employed to monitor water accumulation in cathode flow channels of a multi-path-parallel-serpentine PEMFC. Flow visualization served as direct method (item (i)), in which recorded videos of transparent channels were analyzed via an image-processing algorithm to monitor water coverage ratio (WCR). Simultaneously an inlet-to-exit pressure drop coefficient (Phi) was defined for indirect-method (item (ii)). AST, CST and T* were taken as input to provide three experimental based correlations for generated electrical power, WCR and Phi as a response. According to the results, an increase in AST reduces water-content in cathode channels;decreases performance also raises the sensitivity of generated power to variations in other operating-parameters. However, an increase in CST and T* reduces water contents in cathode channels also increases the cell power and lowers sensitivity of the power to the variation of other operating-parameters. It is observed that strong connectivity exists between WCR and Phi. This is verified by obtaining a high Pearson correlation coefficient of 0.887 between WCR and Phi. It is demonstrated that dehydration and flooding could be prevented when 3 <= WCR <= 4, from which appropriate operating-parameters AST, CST and T* could be retrieved.

关键词： Performance enhancement of PEMFC Water management Two-phase flow Design of Experiment (DOE) Direct optical visualization Digital image processing

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WBA: A Warping-based Approach to Generating Imperceptible Adversarial Examples

WBA: A Warping-based Approach to Generating Imperceptible Ad...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Chengyao Hua Shigeng Zhang Weiping Wang Zhankai Li Jian Zhang School of Computer Science and Engineering Central South University Changsha China Science and Technology on Parallel and Distributed Processing Laboratory (PDL)

The human can easily recognize the incongruous parts of an image, for example, perturbations unrelated to the image itself, but are poor at spotting the small geometric transformations. However, in terms of the robustness of deep neural networks (DNNs), the ability to properly recognize objects with small geometric transformations is still a challenge. In this work, we investigate the problem from the perspective of adversarial attacks: does the performance of DNNs degrade even when small geometric transformations are applied to images? To this end, we propose a novel adversarial attack method, called WBA, a Warping-Based Adversarial attack method, which does not introduce information independent of the original images but manipulates the existing pixels of the images by elastic warping transformations to generate adversarial examples that are imperceptible to the human eye. At the same time, existing adversarial attacks typically generate adversarial examples by modifying pixels in the spatial domain of the image, the addition of such perturbations introduces extra information unrelated to the image itself and is easily detected by the naked eyes. We demonstrate the effectiveness of WBA by extensive experiments on commonly used datasets, including MNIST, CIFAR10, and imageNet. The results show that WBA can quickly generate adversarial examples with the highest adversarial strength, consumes less time, and can be comparable to optimization-based adversarial attack methods in image perception evaluation metrics such as LPIPS, SSIM, and far more than gradient direction-based iterative methods.

关键词： Measurement Deep learning Privacy image recognition Perturbation methods Neural networks Robustness

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