{"title":"基于GPU CUDA的图像绘制交替方向隐式(ADI)方法的计算加速","authors":"Mutaqin Akbar, Pranowo, Suyoto Magister","doi":"10.1109/ICCEREC.2017.8226691","DOIUrl":null,"url":null,"abstract":"This paper presents a computational acceleration of image inpainting using parallel processing based on Graphics Processing Unit (GPU) Compute Unified Device Architecture (CUDA). We use parabolic partial differential equation (PDE) called heat equation as the model equation. The heat equation is discretized numerically using Finite Difference method. Semi-algebraic equation that formed then solved by using Alternating-Direction Implicit (ADI) scheme. The numerical algorithm is implemented in GPU CUDA parallel computing to speed up the computational time. The computational process of the inpainting can be done using larger time-step. The computational time can be accelerated to 5.86 times faster using an image with 2736×1824 resolution.","PeriodicalId":328054,"journal":{"name":"2017 International Conference on Control, Electronics, Renewable Energy and Communications (ICCREC)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Computational acceleration of image inpainting Alternating-Direction Implicit (ADI) method using GPU CUDA\",\"authors\":\"Mutaqin Akbar, Pranowo, Suyoto Magister\",\"doi\":\"10.1109/ICCEREC.2017.8226691\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a computational acceleration of image inpainting using parallel processing based on Graphics Processing Unit (GPU) Compute Unified Device Architecture (CUDA). We use parabolic partial differential equation (PDE) called heat equation as the model equation. The heat equation is discretized numerically using Finite Difference method. Semi-algebraic equation that formed then solved by using Alternating-Direction Implicit (ADI) scheme. The numerical algorithm is implemented in GPU CUDA parallel computing to speed up the computational time. The computational process of the inpainting can be done using larger time-step. The computational time can be accelerated to 5.86 times faster using an image with 2736×1824 resolution.\",\"PeriodicalId\":328054,\"journal\":{\"name\":\"2017 International Conference on Control, Electronics, Renewable Energy and Communications (ICCREC)\",\"volume\":\"27 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 International Conference on Control, Electronics, Renewable Energy and Communications (ICCREC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCEREC.2017.8226691\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 International Conference on Control, Electronics, Renewable Energy and Communications (ICCREC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCEREC.2017.8226691","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Computational acceleration of image inpainting Alternating-Direction Implicit (ADI) method using GPU CUDA
This paper presents a computational acceleration of image inpainting using parallel processing based on Graphics Processing Unit (GPU) Compute Unified Device Architecture (CUDA). We use parabolic partial differential equation (PDE) called heat equation as the model equation. The heat equation is discretized numerically using Finite Difference method. Semi-algebraic equation that formed then solved by using Alternating-Direction Implicit (ADI) scheme. The numerical algorithm is implemented in GPU CUDA parallel computing to speed up the computational time. The computational process of the inpainting can be done using larger time-step. The computational time can be accelerated to 5.86 times faster using an image with 2736×1824 resolution.