location:Home > 2019 VOL.2 Feb No.1 > Based on large sample data analysis of financial information parallel computing method research

2019 VOL.2 Feb No.1

  • Title: Based on large sample data analysis of financial information parallel computing method research
  • Name: Na Cai
  • Company: Shanghai Wenhua Financial Information Consultative Co., Ltd,Shan
  • Abstract:

    Traditional financial information is present in the study of the sample parallel computation method for analyzing low precision disadvantages. This paper presents the financial information sample parallel computing method based on big data. The introduction of large data technology, the design method for parallel computing, to achieve financial model building is large sample; sample information determining algorithm, based on a sample-computing model is embedded, the sample parallel computing financial information. The experimental data shows that the proposed parallel computing method for big data financial sample information not only greatly improves the analysis accuracy, but also increases the application scope and usage dimension of financial sample information.

  • Keyword: sample data; parallel computing; detection method; experimental data;
  • DOI: 10.12250/jpciams2019010118
  • Citation form: Na Cai.Based on large sample data analysis of financial information parallel computing method research[J]. Computer Informatization and Mechanical System, 2019, vol. 2, pp. 56-60.
Reference:

[1] Chen Guoliang, Ann Hong, Chen Li, et al. Parallel algorithm practice [J]. higher education press, 2018 (18): 35-40.
[2] Du Zhihui. Parallel Programming Technology MPI for High Performance Computing [J].Beijing: Tsinghua University Press, 2016 (2): 95-95.
[3] Chen Wenguang, Wu Yong Wei.M PI and OpenM P parallel programming [J]. Beijing: Tsinghua University press, 2017 (5): 35-45.
Li Yan, Zhang Yunquan. Implementation and optimization of OpenCL based FFT on heterogeneous platforms. Computer science, 2016 (3): 66-76.
[5] Espinoza S, Panteli M, Mancarella P, et al. Multi-phase assessment and adaptation of power systems resilience to natural hazards[J]. Electric Power Systems Research, 2016, 136:352-361.
[6] Kim J H, Chau-Dinh T, Zi G, et al. Probabilistic fatigue integrity assessment in multiple crack growth analysis associated with equivalent initial flaw and material variability[J]. Engineering Fracture Mechanics, 2016, 156:182-196.
[7] Rahman M U, Rahman S, Mansoor S, et al. Implementation of ICT and Wireless Sensor Networks for Earthquake Alert and Disaster Management in Earthquake Prone Areas ☆[J]. Procedia Computer Science, 2016, 85:92-99.
[8] Oguchi M, Hara R. A Speculative Control Mechanism of Cloud Computing Systems Based on Emergency Disaster Information Using SDN ☆[J]. Procedia Computer Science, 2016, 98:515-521.
[9] Min G E, Chen X, Fengping W U. Disaster Chain,Loss of Victims in the Disaster and Multi-period Allocation of Complex Emergency Resources Networks[J]. Journal of Beijing Institute of Technology, 2017.
[10] Festa G, Picozzi M, Caruso A, et al. Performance of Earthquake Early Warning Systems during the 2016–2017 Mw 5–6.5 Central Italy Sequence[J]. Seismological Research Letters, 2018, 89(1).

Tsuruta Institute of Medical Information Technology
Address:[502,5-47-6], Tsuyama, Tsukuba, Saitama, Japan TEL:008148-28809 fax:008148-28808 Japan,Email:jpciams@hotmail.com,2019-09-16