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swTensor: accelerating tensor decomposition on Sunway architecture

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Modern applications are digesting and generating data with rich features that are stored in high dimensional array or tensor. The computation applied to tensor, such as Canonical Polyadic decomposition (CP decomposition) plays an important role in understanding the internal relationships within the data. Using CP decomposition to analyze large tensor with billions of sizes requires tremendous computation power. In the meanwhile, the emerging Sunway many-core processor has demonstrated its computation advantage in powering the first hundred petaFLOPS supercomputer in the world. In this paper, we propose swTensor that adapts the CP decomposition to Sunway processor by leveraging the MapReduce framework for automatic parallelization and the unique architecture of Sunway for high performance. Specifically, we divide the major computation of CP decomposition into four sub-procedures and implement each using MapReduce framework with customized design key-value pair. Also, we tile the data during the computation so that it fits into the limited local device memory on Sunway for better performance. Moreover, we propose a performance auto-tuning mechanism to search for the optimal parameter settings in swTensor. The experimental results demonstrate swTensor achieves better performance than the state-of-the-art BigTensor and CSTF with the average speedup of 1.36 \(\times \) and 1.24 \(\times \), respectively. Besides, swTensor exhibits better scalability when scaling across multiple Sunway processors.

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Acknowledgements

The authors would like to thank all anonymous reviewers for their insightful comments and suggestions. This work is supported by National Key R&D Program of China (Grant No. 2016YFB1000503 and 2016YFA0602200), National Natural Science Foundation of China (Grant No. 61502019 and 61732002), State Key Laboratory of Software Development Environment (Grant No. SKLSDE-2018ZX-19), Center for High Performance Computing and System Simulation, Pilot National Laboratory for Marine Science and Technology (Qingdao). Hailong Yang is the corresponding author.

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  1. Hailong Yang

    Present address: Beihang University, G823, New Main Building, Beijing, 100191, China

Authors and Affiliations

  1. Sino-German Joint Software Institute, the School of Computer Science and Engineering, Beihang University, Beijing, 100191, China

    Xiaogang Zhong, Hailong Yang, Zhongzhi Luan & Depei Qian

  2. State Key Laboratory of Software Development Environment, Beihang University, Beijing, 100191, China

    Xiaogang Zhong & Hailong Yang

  3. Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China

    Lin Gan & Guangwen Yang

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Zhong, X., Yang, H., Luan, Z. et al. swTensor: accelerating tensor decomposition on Sunway architecture. CCF Trans. HPC 1, 161–176 (2019). https://doi.org/10.1007/s42514-019-00017-5

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