2023 31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP)最新文献_第5页

Blockchain-based schemes for continuous verifiability and traceability of IoT data 基于区块链的方案，用于物联网数据的持续可验证性和可追溯性

2023 31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP) Pub Date : 2023-03-01 DOI: 10.1109/PDP59025.2023.00034

Cristhian Martinez Rendon, J. L. González-Compeán, Dante D. Sánchez-Gallegos, J. Carretero

{"title":"Blockchain-based schemes for continuous verifiability and traceability of IoT data","authors":"Cristhian Martinez Rendon, J. L. González-Compeán, Dante D. Sánchez-Gallegos, J. Carretero","doi":"10.1109/PDP59025.2023.00034","DOIUrl":"https://doi.org/10.1109/PDP59025.2023.00034","url":null,"abstract":"This paper presents a continuous delivery/continuous verifiability (CD/CV) framework for IoT dataflows in edge-fog-cloud. In this framework a CD model based on extraction, transformation, and load (ETL) mechanism as well as a directed acyclic graph (DAG) construction, enable end-users to create efficient schemes for the continuous verification and validation of the execution of applications in edge-fog-cloud infrastructures. This framework also provides tools for continuous verification and validation (CV) of predefined execution sequences and the integrity of digital assets using blockchain. CV model converts ETL and DAG into business model, smart contracts in a private blockchain for the automatic and transparent registration of transactions performed by each application in workflows/pipelines created by CD model without altering applications nor edge-fog-cloud workflows. This framework ensures that IoT dataflow delivers verifiable information for organizations to conduct critical decision-making processes with certainty. A containerized parallelism approach solves portability issues and reduces/compensates the overhead produced by CD/CV operations. The talk will also present evaluation results of the CD/CV framework based on a case study where user mobility information is used to identify interest points, patterns, and maps. The experimental evaluation results show the feasibility of CD/CV to register transactions performed in IoT dataflows through edge-fog-cloud in a private blockchain network.","PeriodicalId":153500,"journal":{"name":"2023 31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130397587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fine-grained parallel social modelling for analyzing COVID-19 propagation 用于分析COVID-19传播的细粒度并行社会建模

2023 31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP) Pub Date : 2023-03-01 DOI: 10.1109/PDP59025.2023.00026

Aymar Cublier Martínez, Álejandro Alvarez Isabel, Jesús Carretero, D. E. Singh

{"title":"Fine-grained parallel social modelling for analyzing COVID-19 propagation","authors":"Aymar Cublier Martínez, Álejandro Alvarez Isabel, Jesús Carretero, D. E. Singh","doi":"10.1109/PDP59025.2023.00026","DOIUrl":"https://doi.org/10.1109/PDP59025.2023.00026","url":null,"abstract":"Agent-based epidemiological simulators have been proven to be one of the most successful tools for the analysis of the COVID-19 propagation. The ability of these tools to reproduce the behavior and interactions of each single individual leads to accurate and detailed results, that can be used to model fine-grained health-related policies like selective vaccination campaigns or immunity waning. One characteristic of these tools is the large amount of input data and computational resources and that they require. This relies on the development of parallel algorithms and methodologies for generating, accessing and processing large volumes of data from multiple data sources. This work presents a parallel workflow for extending the social modelling of EpiGraph, an agent-based simulator. We have included two novel parallel social generation stages -that provide detailed and realistic social model- and one new visualization stage. The work presents a description of the algorithms used in each stage and a practical evaluation on a real platform. Results show that this contribution can be efficiently executed in parallel architectures and increases the simulation detail level, representing a significant advance in the simulator scenario modelling.","PeriodicalId":153500,"journal":{"name":"2023 31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP)","volume":"224 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126124881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Coupling constrained-based flux sampling and clustering to tackle cancer metabolic heterogeneity 耦合基于约束的通量采样和聚类处理癌症代谢异质性

2023 31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP) Pub Date : 2023-03-01 DOI: 10.1109/PDP59025.2023.00037

B. Galuzzi, Stefano Izzo, F. Giampaolo, Salvatore Cuomo, Marco E. Vanoni, L. Alberghina, C. Damiani, F. Piccialli

引用次数: 0

Parallel and hierarchically-distributed Shoreline Alert Model (SAM) 平行分层分布的海岸线预警模型

2023 31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP) Pub Date : 2023-03-01 DOI: 10.1109/PDP59025.2023.00024

Ciro Giuseppe de Vita, Gennaro Mellone, Aniello Florio, Catherine Alessandra Torres Charles, D. Di Luccio, M. Lapegna, G. Benassai, G. Budillon, R. Montella

{"title":"Parallel and hierarchically-distributed Shoreline Alert Model (SAM)","authors":"Ciro Giuseppe de Vita, Gennaro Mellone, Aniello Florio, Catherine Alessandra Torres Charles, D. Di Luccio, M. Lapegna, G. Benassai, G. Budillon, R. Montella","doi":"10.1109/PDP59025.2023.00024","DOIUrl":"https://doi.org/10.1109/PDP59025.2023.00024","url":null,"abstract":"In this paper, the Shoreline Alert Model (SAM) is presented as a component of a computation platform based on workflows dedicated to extreme weather/marine event simulation. The model aims to mitigate the effects of global change by providing decision-makers, scientists, and engineers with a novel, next-generation tool set for facing extreme weather events and implementing related management or emergency responses. SAM uses a parallelization schema, allowing users to run it on heterogeneous parallel architectures. As a result, SAM produces approximately 24 times faster results than the baseline when using shared memory with distributed memory and dealing with about 20,000 transects along the Campania coastline. The system is based on the algorithms of the open-source numerical models WRF (Weather Research and Forecasting) and WW3 (Wave-watch III) implemented with refraction and shoaling routines together with run-up equations to form the modeling chain used for coastal flooding assessment.","PeriodicalId":153500,"journal":{"name":"2023 31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115796652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bandit-based Variable Fixing for Binary Optimization on GPU Parallel Computing 基于bandit的GPU并行二进制优化变量固定

2023 31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP) Pub Date : 2023-03-01 DOI: 10.1109/PDP59025.2023.00031

Ryota Yasudo

引用次数: 0

FastFlow targeting FPGAs FastFlow瞄准fpga

2023 31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP) Pub Date : 2023-03-01 DOI: 10.1109/PDP59025.2023.00023

M. Danelutto, G. Mencagli, Alberto Ottimo, Francesco Iannone, P. Palazzari

引用次数: 0

Synchronization Efficient Scheduling of Fine-grained Irregular Programs 细粒度不规则程序的同步高效调度

2023 31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP) Pub Date : 2023-03-01 DOI: 10.1109/PDP59025.2023.00044

Tao Tao

引用次数: 0

Dynamic Resource Partitioning for Multi-Tenant Systolic Array Based DNN Accelerator 基于收缩阵列的多租户DNN加速器动态资源分区

2023 31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP) Pub Date : 2023-02-21 DOI: 10.1109/PDP59025.2023.00019

M. Reshadi, David Gregg

引用次数: 0

Toward Matrix Multiplication for Deep Learning Inference on the Xilinx Versal 基于Xilinx的矩阵乘法深度学习推理

2023 31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP) Pub Date : 2023-02-15 DOI: 10.1109/PDP59025.2023.00043

Jie Lei, J. Flich, Enrique S. Quintana-Ort'i

引用次数: 0

An Efficient Accelerator for Deep Learning-based Point Cloud Registration on FPGAs 基于fpga的深度学习点云配准的高效加速器

2023 31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP) Pub Date : 2022-03-11 DOI: 10.1109/PDP59025.2023.00018

K. Sugiura, Hiroki Matsutani

{"title":"An Efficient Accelerator for Deep Learning-based Point Cloud Registration on FPGAs","authors":"K. Sugiura, Hiroki Matsutani","doi":"10.1109/PDP59025.2023.00018","DOIUrl":"https://doi.org/10.1109/PDP59025.2023.00018","url":null,"abstract":"Point cloud registration is the basis for many robotic applications such as odometry and Simultaneous Localization And Mapping (SLAM), which are increasingly important for autonomous mobile robots. The limitation of computational resources and power budgets on such robots motivates us to study the resource-efficient registration method on low-cost edge devices. In this paper, we propose an FPGA-based novel pipeline for 3D point cloud registration built upon a recent deep learning-based method, PointNetLK. Based on the profiling results, we focus on the PointNet feature extraction as it becomes a major bottleneck; we improve its scalability and memory-efficiency by consuming each input point one-by-one in a pipelined manner instead of processing the whole point cloud at once. We then design a fully-parallelized and pipelined accelerator consisting of a custom PointNet IP core, which fits within both low-cost and mid-range FPGAs (e.g., Avnet Ultra96v2 and Xilinx ZCU104). Experimental results show that our proposed pipeline achieves up to 21.34x and 69.60x faster registration speed than the vanilla PointNetLK and ICP, respectively, while only consuming 722mW and maintaining the same level of accuracy.","PeriodicalId":153500,"journal":{"name":"2023 31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121771718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2