{"title":"面向大规模多变量SAMR数据分析与可视化的特征驱动紧凑表示模型","authors":"Yang Yang , Yu Pei , Yi Cao","doi":"10.1016/j.cag.2025.104331","DOIUrl":null,"url":null,"abstract":"<div><div>The storage overhead and I/O bottleneck of supercomputers creates a challenge in efficiently analyzing and visualizing large-scale multivariate SAMR data. It is thus necessary to greatly reduce the data size on the premise of maintaining data accuracy. In this paper, we propose a feature-driven compact representation model to handle structurally complex, high-dimensional, and nonlinear structured adaptive mesh refinement (SAMR) data for efficient storage, analysis, and visualization. We combine information-guided domain partition, distance-based dimensionality reduction, and error-bounded data representation to form a coherent three-component framework, achieving high compression ratios while ensuring low accuracy loss. Our approach addresses the key bottleneck in the visualization of large-scale multivariate SAMR data generated by massively parallel scientific simulations, namely the mutual restraint relationship between compression efficiency and data fidelity. We validate the effectiveness of our method using four datasets, the largest of which contains 4 billion grid points. Experimental results demonstrate that, compared with the state-of-the-art methods, our approach reduces data storage costs by approximately an order of magnitude while improving data reconstruction accuracy by nearly two orders of magnitude.</div></div>","PeriodicalId":50628,"journal":{"name":"Computers & Graphics-Uk","volume":"133 ","pages":"Article 104331"},"PeriodicalIF":2.8000,"publicationDate":"2025-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Feature-driven compact representation model for analysis and visualization of large-scale multivariate SAMR data\",\"authors\":\"Yang Yang , Yu Pei , Yi Cao\",\"doi\":\"10.1016/j.cag.2025.104331\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>The storage overhead and I/O bottleneck of supercomputers creates a challenge in efficiently analyzing and visualizing large-scale multivariate SAMR data. It is thus necessary to greatly reduce the data size on the premise of maintaining data accuracy. In this paper, we propose a feature-driven compact representation model to handle structurally complex, high-dimensional, and nonlinear structured adaptive mesh refinement (SAMR) data for efficient storage, analysis, and visualization. We combine information-guided domain partition, distance-based dimensionality reduction, and error-bounded data representation to form a coherent three-component framework, achieving high compression ratios while ensuring low accuracy loss. Our approach addresses the key bottleneck in the visualization of large-scale multivariate SAMR data generated by massively parallel scientific simulations, namely the mutual restraint relationship between compression efficiency and data fidelity. We validate the effectiveness of our method using four datasets, the largest of which contains 4 billion grid points. Experimental results demonstrate that, compared with the state-of-the-art methods, our approach reduces data storage costs by approximately an order of magnitude while improving data reconstruction accuracy by nearly two orders of magnitude.</div></div>\",\"PeriodicalId\":50628,\"journal\":{\"name\":\"Computers & Graphics-Uk\",\"volume\":\"133 \",\"pages\":\"Article 104331\"},\"PeriodicalIF\":2.8000,\"publicationDate\":\"2025-09-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computers & Graphics-Uk\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0097849325001712\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, SOFTWARE ENGINEERING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers & Graphics-Uk","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0097849325001712","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
Feature-driven compact representation model for analysis and visualization of large-scale multivariate SAMR data
The storage overhead and I/O bottleneck of supercomputers creates a challenge in efficiently analyzing and visualizing large-scale multivariate SAMR data. It is thus necessary to greatly reduce the data size on the premise of maintaining data accuracy. In this paper, we propose a feature-driven compact representation model to handle structurally complex, high-dimensional, and nonlinear structured adaptive mesh refinement (SAMR) data for efficient storage, analysis, and visualization. We combine information-guided domain partition, distance-based dimensionality reduction, and error-bounded data representation to form a coherent three-component framework, achieving high compression ratios while ensuring low accuracy loss. Our approach addresses the key bottleneck in the visualization of large-scale multivariate SAMR data generated by massively parallel scientific simulations, namely the mutual restraint relationship between compression efficiency and data fidelity. We validate the effectiveness of our method using four datasets, the largest of which contains 4 billion grid points. Experimental results demonstrate that, compared with the state-of-the-art methods, our approach reduces data storage costs by approximately an order of magnitude while improving data reconstruction accuracy by nearly two orders of magnitude.
期刊介绍:
Computers & Graphics is dedicated to disseminate information on research and applications of computer graphics (CG) techniques. The journal encourages articles on:
1. Research and applications of interactive computer graphics. We are particularly interested in novel interaction techniques and applications of CG to problem domains.
2. State-of-the-art papers on late-breaking, cutting-edge research on CG.
3. Information on innovative uses of graphics principles and technologies.
4. Tutorial papers on both teaching CG principles and innovative uses of CG in education.