{"title":"gpu上稀疏矩阵向量乘法的CUDA参数自动调优","authors":"Ping Guo, Liqiang Wang","doi":"10.1109/ICCIS.2010.285","DOIUrl":null,"url":null,"abstract":"Graphics Processing Unit (GPU) has become an attractive coprocessor for scientific computing due to its massive processing capability. The sparse matrix-vector multiplication (SpMV) is a critical operation in a wide variety of scientific and engineering applications, such as sparse linear algebra and image processing. This paper presents an auto-tuning framework that can automatically compute and select CUDA parameters for SpMV to obtain the optimal performance on specific GPUs. The framework is evaluated on two NVIDIA GPU platforms, GeForce 9500 GTX and GeForce GTX 295.","PeriodicalId":227848,"journal":{"name":"2010 International Conference on Computational and Information Sciences","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"52","resultStr":"{\"title\":\"Auto-Tuning CUDA Parameters for Sparse Matrix-Vector Multiplication on GPUs\",\"authors\":\"Ping Guo, Liqiang Wang\",\"doi\":\"10.1109/ICCIS.2010.285\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Graphics Processing Unit (GPU) has become an attractive coprocessor for scientific computing due to its massive processing capability. The sparse matrix-vector multiplication (SpMV) is a critical operation in a wide variety of scientific and engineering applications, such as sparse linear algebra and image processing. This paper presents an auto-tuning framework that can automatically compute and select CUDA parameters for SpMV to obtain the optimal performance on specific GPUs. The framework is evaluated on two NVIDIA GPU platforms, GeForce 9500 GTX and GeForce GTX 295.\",\"PeriodicalId\":227848,\"journal\":{\"name\":\"2010 International Conference on Computational and Information Sciences\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-12-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"52\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 International Conference on Computational and Information Sciences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCIS.2010.285\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 International Conference on Computational and Information Sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCIS.2010.285","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Auto-Tuning CUDA Parameters for Sparse Matrix-Vector Multiplication on GPUs
Graphics Processing Unit (GPU) has become an attractive coprocessor for scientific computing due to its massive processing capability. The sparse matrix-vector multiplication (SpMV) is a critical operation in a wide variety of scientific and engineering applications, such as sparse linear algebra and image processing. This paper presents an auto-tuning framework that can automatically compute and select CUDA parameters for SpMV to obtain the optimal performance on specific GPUs. The framework is evaluated on two NVIDIA GPU platforms, GeForce 9500 GTX and GeForce GTX 295.
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