{"title":"大规模并行体系结构的α -树算法","authors":"Edwin Carlinet;Quentin Kaci;Nicolas Blin","doi":"10.1109/TIP.2025.3586495","DOIUrl":null,"url":null,"abstract":"The alpha-tree, also known as the quasi-flat zone hierarchy is a widely used representation of images in Mathematical Morphology. This structure organizes the regions according to a similarity criterion into a tree, that eases the multiscale analysis of images. Many alpha-tree algorithms exist and computing this structure efficiently is still an active field of research. Indeed, the alpha-tree is commonly used in remote sensing where there is an urge for fast processing of large terabytes images. In this paper, we propose the first massively parallel alpha-tree algorithm that leverages concurrent union-find data structures to exploit the SIMT (Single Instruction Multiple Threads) programming model of GPUs. Our algorithm outperforms the State-of-the-Art parallel CPU algorithms by a factor of 10 on average on desktop computers and servers. It also opens new perspectives for using Mathematical Morphology methods on GPU pipelines.","PeriodicalId":94032,"journal":{"name":"IEEE transactions on image processing : a publication of the IEEE Signal Processing Society","volume":"34 ","pages":"4402-4413"},"PeriodicalIF":13.7000,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An Alpha-Tree Algorithm for Massively Parallel Architectures\",\"authors\":\"Edwin Carlinet;Quentin Kaci;Nicolas Blin\",\"doi\":\"10.1109/TIP.2025.3586495\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The alpha-tree, also known as the quasi-flat zone hierarchy is a widely used representation of images in Mathematical Morphology. This structure organizes the regions according to a similarity criterion into a tree, that eases the multiscale analysis of images. Many alpha-tree algorithms exist and computing this structure efficiently is still an active field of research. Indeed, the alpha-tree is commonly used in remote sensing where there is an urge for fast processing of large terabytes images. In this paper, we propose the first massively parallel alpha-tree algorithm that leverages concurrent union-find data structures to exploit the SIMT (Single Instruction Multiple Threads) programming model of GPUs. Our algorithm outperforms the State-of-the-Art parallel CPU algorithms by a factor of 10 on average on desktop computers and servers. It also opens new perspectives for using Mathematical Morphology methods on GPU pipelines.\",\"PeriodicalId\":94032,\"journal\":{\"name\":\"IEEE transactions on image processing : a publication of the IEEE Signal Processing Society\",\"volume\":\"34 \",\"pages\":\"4402-4413\"},\"PeriodicalIF\":13.7000,\"publicationDate\":\"2025-07-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE transactions on image processing : a publication of the IEEE Signal Processing Society\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/11079786/\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE transactions on image processing : a publication of the IEEE Signal Processing Society","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/11079786/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An Alpha-Tree Algorithm for Massively Parallel Architectures
The alpha-tree, also known as the quasi-flat zone hierarchy is a widely used representation of images in Mathematical Morphology. This structure organizes the regions according to a similarity criterion into a tree, that eases the multiscale analysis of images. Many alpha-tree algorithms exist and computing this structure efficiently is still an active field of research. Indeed, the alpha-tree is commonly used in remote sensing where there is an urge for fast processing of large terabytes images. In this paper, we propose the first massively parallel alpha-tree algorithm that leverages concurrent union-find data structures to exploit the SIMT (Single Instruction Multiple Threads) programming model of GPUs. Our algorithm outperforms the State-of-the-Art parallel CPU algorithms by a factor of 10 on average on desktop computers and servers. It also opens new perspectives for using Mathematical Morphology methods on GPU pipelines.