Population stratification is a problem in genetic association studies because it is likely to highlight loci that underlie the population structure rather than disease-related loci. At present, principal component analysis(PCA) has been proven to be an effective way to correct for population stratification. However, the conventional PCA algorithm is time-consuming when dealing with large datasets. We developed a Graphic processing unit(GPU)-based PCA software named SHEsis PCA(http://analysis.bio-x.cn/SHEsis Main.htm) that is highly parallel with a highest speedup greater than 100 compared with its CPU version. A cluster algorithm based on X-means was also implemented as a way to detect population subgroups and to obtain matched cases and controls in order to reduce the genomic inflation and increase the power. A study of both simulated and real datasets showed that SHEsis PCA ran at an extremely high speed while the accuracy was hardly reduced. Therefore, SHEsis PCA can help correct for population stratification much more efficiently than the conventional CPU-based algorithms. Population presentations, principal component analysis (PCA) has been proven to be an effective way to correct for population stratification However, the conventional PCA algorithm is time-consuming when dealing with large datasets. We developed a Graphic processing unit (GPU) -based PCA software named SHEsis PCA (http://analysis.bio-x.cn/SHEsis Main.htm ) that is highly parallel with a highest speedup greater than 100 compared with its CPU version. A cluster algorithm based on X-means was also implemented as a way to detect population subgroups and to obtain matched cases and controls in order to reduce the genomic inflation and increase the power. A study of both simulated and real datasets showed that SHEsis PCA ran at an extremely high speed while the accuracy was hardly reduced. Thus, SHEsis PCA can help correct fo r population stratification much more than the conventional conventional CPU-based algorithms.