Abstract Since specific hardware characteristics and low-level programming model are adapted to both NVIDIA GPU and new generation Sunway architecture, automatically translating mature CUDA kernels to Sunway ATHREAD kernels are realistic but challenging work. To address this issue, swCUDA, an auto parallel code translation framework is proposed. To that end, we create scale affine translation to transform CUDA thread hierarchy to Sunway index, directive based memory hierarchy and data redirection optimization to assign optimal memory usage and data stride strategy, directive based grouping-calculation-asynchronous-reduction (GCAR) algorithm to provide general solution for random access issue. swCUDA utilizes code generator ANTLR as compiler frontend to parse CUDA kernel and integrate novel algorithms in the node of abstracted syntax tree (AST) depending on directives. Automatically translation is performed on the entire Polybench suite and NBody simulation benchmark. We get an average 40x speedup compared with baseline on the Sunway architecture, average speedup of 15x compared to x86 CPU and average 27 percentage higher than NVIDIA GPU. Further, swCUDA is implemented to translate major kernels of the real world application Gromacs. The translated version achieves up to 17x speedup.

People are increasingly concerned about how tectonic processes affect climate and vice versa. We establish a cross-sphere modeling system for volcanic eruptions and atmosphere circulation on a new Sunway supercomputer with a spatial resolution from 10m locally to 3km globally, using an improved multimedium and multiphase smoothed particle hydrodynamics (SPH) combined with a fully coupled meteorology-chemistry global atmospheric modeling scheme. We achieve 400 billion particles and 80% parallel efficiency using 39,000,000 processor cores. The simulation captures the whole dynamic process of the Tonga eruption from shock waves, earthquakes, tsunamis, mushroom clouds to the following 6--7 days of transport and diffusion of ash and water vapor, and preliminarily obtains the influence effect of full coupling of volcano, earthquake, ocean and atmosphere. This work is of great significance for deeply understanding the interaction between tectonic processes and climate change, and establishing an early warning simulation system for similar global hazard events.

Over the past three decades, ab initio electronic structure calculations of large, complex and metallic systems are limited to tens of thousands of atoms in computational accuracy and efficiency on leadership supercomputers. We present a massively parallel discontinuous Galerkin density functional theory (DGDFT) implementation, which adopts adaptive local basis functions to discretize the Kohn-Sham equation, resulting in a block-sparse Hamiltonian matrix. A highly efficient pole expansion and selected inversion (PEXSI) sparse direct solver is implemented in DGDFT to achieve O(N1.5) scaling for quasi two-dimensional systems. DGDFT allows us to compute the electronic structures of complex metallic heterostructures with 2.5 million atoms (17.2 million electrons) using 35.9 million cores on the new Sunway supercomputer. The peak performance of PEXSI can achieve 64 PFLOPS (~5% of theoretical peak), which is un-precedented for sparse direct solvers. This accomplishment paves the way for quantum mechanical simulations into mesoscopic scale for designing next-generation electronic devices.

Compared with the conventional network data analysis, the data analysis based on social network has a very clear object of analysis, various forms of analysis, and more methods and contents of analysis. If the conventional analysis methods are applied to social network data analysis, we will find that the analysis results do not reach our expected results. The results of the above studies are usually based on statistical methods and machine learning methods, but some systems use other methods, such as self‐organizing self‐learning mechanisms and concept retrieval. With regard to the current data analysis methods, data models, and social network data, this paper conducts a series of researches from data acquisition, data cleaning and processing, data model application and optimization of the model in the process of application, and how the formed data analysis results can be used for managers to make decisions. In this paper, the number of customer evaluations, the time of evaluation, the frequency of evaluation, and the score of evaluation are clustered and analyzed, and finally, the results obtained by the two clustering methods applied in the analysis process are compared to build a customer grading system. The analysis results can be used to maintain the current Amazon purchase customers in a hierarchical manner, and the most valuable customers need to be given key attention, combining social network big data with micro marketing to improve Amazon’s sales performance and influence, developing from the original single shopping mall model to a comprehensive e‐commerce platform, and cultivating their own customer base.

As people’s awareness of the issue of privacy leakage continues to increase, and the demand for privacy protection continues to increase, there is an urgent need for some effective methods or means to achieve the purpose of protecting privacy. So far, there have been many achievements in the research of location‐based privacy services, and it can effectively protect the location privacy of users. However, there are few research results that require privacy protection, and the privacy protection system needs to be improved. Aiming at the shortcomings of traditional differential privacy protection, this paper designs a differential privacy protection mechanism based on interactive social networks. Under this mechanism, we have proved that it meets the protection conditions of differential privacy and prevents the leakage of private information with the greatest possibility. In this paper, we establish a network evolution game model, in which users only play games with connected users. Then, based on the game model, a dynamic equation is derived to express the trend of the proportion of users adopting privacy protection settings in the network over time, and the impact of the benefit‐cost ratio on the evolutionarily stable state is analyzed. A real data set is used to verify the feasibility of the model. Experimental results show that the model can effectively describe the dynamic evolution of social network users’ privacy protection behaviors. This model can help social platforms design effective security services and incentive mechanisms, encourage users to adopt privacy protection settings, and promote the deployment of privacy protection mechanisms in the network.