区域气候模式CWRF(Climate-Weather Research and Forecasting model)是国家气候中心区域气候预测系统的重要组成部分,也是系统最耗时的程序。高性能计算是提高CWRF数值预报计算性能的关键技术,开展CWRF模式在国产神威众核架构上的移植...区域气候模式CWRF(Climate-Weather Research and Forecasting model)是国家气候中心区域气候预测系统的重要组成部分,也是系统最耗时的程序。高性能计算是提高CWRF数值预报计算性能的关键技术,开展CWRF模式在国产神威众核架构上的移植和优化,提高模式的模拟效率,对模式的扩展、开发能力和可持续发展具有重要意义。基于国产众核SW26010处理器,完成了CWRF区域气候模式的移植、性能分析和深入性能优化,采用访存优化、Cache命中率优化及众核加速优化等方法,对CWRF模式动力过程、物理过程和I/O过程计算代码进行重构及众核加速。结果表明:优化技术可使CWRF动力过程平均加速2倍,最高加速6.4倍,物理过程平均加速1.7倍,最高加速5.4倍,I/O过程加速1.2倍,程序整体最高加速1.4倍,计算误差在合理范围内。展开更多
With the advent of the big data era,the amounts of sampling data and the dimensions of data features are rapidly growing.It is highly desired to enable fast and efficient clustering of unlabeled samples based on featu...With the advent of the big data era,the amounts of sampling data and the dimensions of data features are rapidly growing.It is highly desired to enable fast and efficient clustering of unlabeled samples based on feature similarities. As a fundamental primitive for data clustering,the k-means operation is receiving increasingly more attentions today.To achieve high performance k-means computations on modern multi-core/many-core systems,we propose a matrix-based fused framework that can achieve high performance by conducting computations on a distance matrix and at the same time can improve the memory reuse through the fusion of the distance-matrix computation and the nearest centroids reduction.We implement and optimize the parallel k-means algorithm on the SW26010 many-core processor,which is the major horsepower of Sunway TaihuLight.In particular,we design a task mapping strategy for load-balanced task distribution,a data sharing scheme to reduce the memory footprint and a register blocking strategy to increase the data locality.Optimization techniques such as instruction reordering and double buffering are further applied to improve the sustained performance.Discussions on block-size tuning and performance modeling are also presented.We show by experiments on both randomly generated and real-world datasets that our parallel implementation of k-means on SW26010 can sustain a double-precision performance of over 348.1 Gflops,which is 46.9% of the peak performance and 84%of the theoretical performance upper bound on a single core group,and can achieve a nearly ideal scalability to the whole SW26010 processor of four core groups.Performance comparisons with the previous state-of-the-art on both CPU and GPU are also provided to show the superiority of our optimized k-means kernel.展开更多
文摘区域气候模式CWRF(Climate-Weather Research and Forecasting model)是国家气候中心区域气候预测系统的重要组成部分,也是系统最耗时的程序。高性能计算是提高CWRF数值预报计算性能的关键技术,开展CWRF模式在国产神威众核架构上的移植和优化,提高模式的模拟效率,对模式的扩展、开发能力和可持续发展具有重要意义。基于国产众核SW26010处理器,完成了CWRF区域气候模式的移植、性能分析和深入性能优化,采用访存优化、Cache命中率优化及众核加速优化等方法,对CWRF模式动力过程、物理过程和I/O过程计算代码进行重构及众核加速。结果表明:优化技术可使CWRF动力过程平均加速2倍,最高加速6.4倍,物理过程平均加速1.7倍,最高加速5.4倍,I/O过程加速1.2倍,程序整体最高加速1.4倍,计算误差在合理范围内。
基金the National Key Research and Development Plan of China under Grant No.2016YFB0200603the National Natural Science Foundation of China under Grant No.91530323the Beijing Natural Science Foundation of China under Grant No.JQ18001.
文摘With the advent of the big data era,the amounts of sampling data and the dimensions of data features are rapidly growing.It is highly desired to enable fast and efficient clustering of unlabeled samples based on feature similarities. As a fundamental primitive for data clustering,the k-means operation is receiving increasingly more attentions today.To achieve high performance k-means computations on modern multi-core/many-core systems,we propose a matrix-based fused framework that can achieve high performance by conducting computations on a distance matrix and at the same time can improve the memory reuse through the fusion of the distance-matrix computation and the nearest centroids reduction.We implement and optimize the parallel k-means algorithm on the SW26010 many-core processor,which is the major horsepower of Sunway TaihuLight.In particular,we design a task mapping strategy for load-balanced task distribution,a data sharing scheme to reduce the memory footprint and a register blocking strategy to increase the data locality.Optimization techniques such as instruction reordering and double buffering are further applied to improve the sustained performance.Discussions on block-size tuning and performance modeling are also presented.We show by experiments on both randomly generated and real-world datasets that our parallel implementation of k-means on SW26010 can sustain a double-precision performance of over 348.1 Gflops,which is 46.9% of the peak performance and 84%of the theoretical performance upper bound on a single core group,and can achieve a nearly ideal scalability to the whole SW26010 processor of four core groups.Performance comparisons with the previous state-of-the-art on both CPU and GPU are also provided to show the superiority of our optimized k-means kernel.
