Inspired by the great success of neural networks, graph convolutional neural networks (GCNs) are proposed to analyze graph data. GCNs mainly include two phases with distinct execution patterns. The Aggregation phase, behaves as graph processing, showing a dynamic and irregular execution pattern. The Combination phase, acts more like the neural networks, presenting a static and regular execution pattern. The hybrid execution patterns of GCNs require a design that alleviates irregularity and exploits regularity. Moreover, to achieve higher performance and energy efficiency, the design needs to leverage the high intra-vertex parallelism in Aggregation phase, the highly reusable inter-vertex data in Combination phase, and the opportunity to fuse phase-by-phase execution introduced by the new features of GCNs. However, existing architectures fail to address these demands.In this work, we first characterize the hybrid execution patterns of GCNs on Intel Xeon CPU. Guided by the characterization, we design a GCN accelerator, HyGCN, using a hybrid architecture to efficiently perform GCNs. Specifically, first, we build a new programming model to exploit the fine-grained parallelism for our hardware design. Second, we propose a hardware design with two efficient processing engines to alleviate the irregularity of Aggregation phase and leverage the regularity of Combination phase. Besides, these engines can exploit various parallelism and reuse highly reusable data efficiently. Third, we optimize the overall system via inter-engine pipeline for inter-phase fusion and priority-based off-chip memory access coordination to improve off-chip bandwidth utilization. Compared to the state-of-the-art software framework running on Intel Xeon CPU and NVIDIA V100 GPU, our work achieves on average 1509× speedup with 2500× energy reduction and average 6.5× speedup with 10× energy reduction, respectively. * Corresponding author is Xiaochun Ye and his email is yexi-aochun@ict.ac.cn.
In this paper, a new method is presented for solving the constitutive equations of fractional-order viscoelastic Euler-Bernoulli beams. Firstly, the constitutive equation of the Euler-Bernoulli beam is established by analyzing the constitutive relation between the fractional viscoelastic materials. Secondly, the constitutive equation of the beam is transformed into a matrix equation by using a Quasi-Legendre polynomial in the time domain. Then the matrix equation is discretized and solved, and the numerical solution is obtained for the constitutive equation of the beam. Finally, numerical analysis of two different fractional viscoelastic materials is carried out by numerical experiments. Displacements under different external loads are obtained for the polybutadiene beam and butyl B252 beam. With the change of time and position, the change law of displacements is found. And the performance of the two materials is compared and analyzed.
This paper uses the global trade analysis project (GTAP) to evaluate the impact of grain export restrictions on world food security during the COVID-19 epidemic. The study found that export restrictions distort world market prices, which in turn distort consumption and production, harm the interests of consumers and farmers in some countries, and threaten food security. In this regard, maintaining the convenience of the food trade is the wise choice of all countries. It is necessary to tighten the World Trade Organization (WTO) disciplines related to export restrictions, strengthen global food security governance and jointly build a community with a shared future for mankind.
Based on the panel data of 31 provinces in China from 2011 to 2020, this paper analyzes the development status and distribution characteristics of digital finance, and studies the impact of digital finance on the growth of the real economy. First, whether China’s digital finance development can be classified by region through quartile images is investigated, and whether there are differences in the development of digital finance between regions and within regions is explored. Then, the dynamic characteristics of regional digital finance development distribution are analyzed by kernel density estimation, and the regression model is constructed to analyze the effect of digital finance development on promoting the growth of the real economy. The numerical result shows that the development characteristics of digital finance are different between regions and within regions, and the development of digital finance can significantly promote the growth of the real economy.
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