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In today’s era of digital development of big data information, cross-cultural and cross-regional communication are becoming more and more frequent, and human beings have the right and desire to enjoy certain common cultural information, and it has been 38 years since China and Thailand established diplomatic relations in 1975. Cultural differences in Sino-Thai translation based on big data are studied in four aspects: inter-translation of personal names, inter-translation of geographical names, inter-translation of quantity sets, and inter-translation of date and time. This paper focuses on the research of cultural differences in Sino-Thai mutual translation based on big data analysis, constructing a bilingual word translation model with monolingual word vectors under the big data platform Spark, and conducting data analysis on the accuracy and efficiency of translation by collecting Chinese dataset and Thai dataset in parallelization algorithm in two structures SchemeA and SchemeB . The results show that the accuracy of the bilingual word vectors generated by either SchemeA or SchemeB improves as the size of the dataset increases. In addition, SchemeA is better than SchemeB in terms of accuracy and SchemeB is better than SchemeA in terms of efficiency performance, so Sche A me is suitable for translation tasks with high accuracy requirements, while Sche B me is suitable for translation tasks on large data sets. This study can improve the efficiency and accuracy of mutual translation between China and Thailand and train more translators. Today’s high technology brings much convenience to people, and social knowledge is constantly updated and developed. It is necessary to study in order to keep up with the changes of the times and to grasp the updates of the words of the times in order to translate well and more accurately, and this study not only contributes to the translation work in China but also has an important historical significance to the cultural exchange and communication between China and Thailand.

In this paper, in the context of rural revitalization + data platform, a grassroots employment platform for college students is designed according to the data architecture, and the data is processed hierarchically to achieve the organization and knowledge accumulation of cross-domain employment data, which makes multiple employment data standardized. Using data analysis parallelization algorithm and dimensionality reduction operation method, the training data are read and loaded into the distributed memory of the employment data platform to select the best employment features. According to the design platform of this paper, the employment rate for those employed in the eastern region and 80.16% in the central region has reached 91.15%. This paper's design model can improve employee efficiency and expand the grassroots employment space of college students, as indicated.

Abstract Whole -genome sequencing projects of millions of subjects contain enormous genotypes, entailing a huge memory burden and time for computation. Here, we present GBC, a toolkit for rapidly compressing large-scale genotypes into highly addressable byte-encoding blocks under an optimized parallel framework. We demonstrate that GBC is up to 1000 times faster than state-of-the-art methods to access and manage compressed large-scale genotypes while maintaining a competitive compression ratio. We also showed that conventional analysis would be substantially sped up if built on GBC to access genotypes of a large population. GBC’s data structure and algorithms are valuable for accelerating large-scale genomic research.

Traditional visual-inertial simultaneous localization and mapping algorithms are usually designed based on CPUs, and they cannot effectively utilize the parallel computing function of GPUs if they are directly transplanted to an embedded board with a GPU module. However, the computing power of embedded devices is limited. It is unreasonable for the visual-inertial simultaneous localization and mapping algorithm to occupy most CPU computing resources while the GPU is idle. In this article, a parallelization scheme for the VINS-Mono algorithm based on GPU parallel computing technology is proposed. Based on the compute unified device architecture, the construction and solution of the incremental equation are parallelized in the nonlinear optimization process of the algorithm, and the parallelization methods provided by cuSOLVER and cuBLAS are used to carry out the marginalization of the algorithm. In addition, the program for the detection and matching of image feature points in the process of optical flow tracking is rewritten in the algorithm to realize the parallelization of optical flow tracking. After parallelization, the algorithm is found to run well on a heterogeneous computing model composed of a CPU and GPU and can fully exploit the parallel computing power of the GPU. The proposed method was tested on an NVIDIA's Jetson TX2 module and compared with the VINS-Mono algorithm; the speeds of the construction and solution of the incremental equation were found to be the same, but the optical flow tracking and marginalization speed of the proposed scheme exhibited improvements of about 1.5–1.7 times and 1.9 times, respectively. [ABSTRACT FROM AUTHOR]

Abstract An effective parallelization algorithm based on the compute-unified-device-architecture (CUDA) is developed for DEM generalization that is critical to multi-scale terrain analysis. It aims to efficiently retrieve the critical points for generating coarser-resolution DEMs which maximally maintain the significant terrain features. CUDA is embedded into a multi-point algorithm to provide a parallel-multi-point algorithm for enhancing its computing efficiency. The outcomes are compared with the ANUDEM, compound and maximum z-tolerance methods and the results demonstrate the proposed algorithm reduces response time by up to 96% compared to other methods. As to RMSE, it performs better than ANUDEM and needs half the number of points to keep the same RMSE. The mean slope and surface roughness are reduced by less than 1% in the tested cases. The parallel algorithm provides better streamline matching. Given its high computing efficiency, the proposed algorithm can retrieve more critical points to meet the demands of higher precision. Highlights • We present a parallelization method for DEM generalization based on CUDA. • We propose a parallel-multi-point algorithm to extract the critical points from the DEM. • The method reduces response time by up to 96% compared with three existing methods. • The method can better sustain the drainage features during the generalization process than three existing methods. [ABSTRACT FROM AUTHOR]

Aiming at the low real-time processing efficiency of massive astronomical data, through time-consuming analysis of gridding algorithm in SKA image acquisition and imaging ARL library, two functions of convo-lutional-grid and convolutional-degrid with high frequency and long running time were found out in this algorithm. Then, two functions were parallelized on GPU by multi-threading to realize the cooperative operation of gridding algorithm on GPU and CPU. The experimental results showed that under the same amount of data, the running time of the improved gridding algorithm was greatly shortened, especially when dealing with massive data, the overall running efficiency of ARL was effectively improved. [ABSTRACT FROM AUTHOR]

This letter presents an efficient parallel algorithm for solving locally one-dimensional (LOD) finite-difference time domain (FDTD) in cloud computing environment. As opposed to the existing LOD-FDTD algorithm parallelization scheme, the proposed method solves the implicit tridiagonal system in parallel by using the Sherman–Morrison formula to decompose the tridiagonal matrix into smaller matrices. The parallel nodes in cloud computers solve the matrices simultaneously. Numerical results show that the proposed method is more efficient in cloud computing environment than the conventional parallelization scheme and shows better scalability. [ABSTRACT FROM AUTHOR]

A manager–worker-based parallelization algorithm for Quantum Monte Carlo (QMC-MW) is presented and compared with the pure iterative parallelization algorithm, which is in common use. The new manager–worker algorithm performs automatic load balancing, allowing it to perform near the theoretical maximal speed even on heterogeneous parallel computers. Furthermore, the new algorithm performs as well as the pure iterative algorithm on homogeneous parallel computers. When combined with the dynamic distributable decorrelation algorithm (DDDA) [Feldmann et al., J Comput Chem 28, 2309 (2007)], the new manager–worker algorithm allows QMC calculations to be terminated at a prespecified level of convergence rather than upon a prespecified number of steps (the common practice). This allows a guaranteed level of precision at the least cost. Additionally, we show (by both analytic derivation and experimental verification) that standard QMC implementations are not “perfectly parallel” as is often claimed. © 2007 Wiley Periodicals, Inc. J Comput Chem, 2008 [ABSTRACT FROM AUTHOR]