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
With the increasing demand for flexible and efficient implementation of image and video processing algorithms, there should be a good tradeoff between hardware and software design method. This paper utilized the HW-SW...With the increasing demand for flexible and efficient implementation of image and video processing algorithms, there should be a good tradeoff between hardware and software design method. This paper utilized the HW-SW codesign method to implement the H.264 decoder in an SoC with an ARM core, a multimedia processor and a deblocking filter coprocessor. For the parallel processing features of the multimedia processor, clock cycles of decoding process can be dramatically reduced. And the hardware dedicated deblocking filter coprocessor can improve the efficiency a lot. With maximum clock frequency of 150 MHz, the whole system can achieve real time processing speed and flexibility.展开更多
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.
文摘With the increasing demand for flexible and efficient implementation of image and video processing algorithms, there should be a good tradeoff between hardware and software design method. This paper utilized the HW-SW codesign method to implement the H.264 decoder in an SoC with an ARM core, a multimedia processor and a deblocking filter coprocessor. For the parallel processing features of the multimedia processor, clock cycles of decoding process can be dramatically reduced. And the hardware dedicated deblocking filter coprocessor can improve the efficiency a lot. With maximum clock frequency of 150 MHz, the whole system can achieve real time processing speed and flexibility.
