Syntax Tree Development for Automated Serial-to-Parallel Code Translator for Multicore Processors

Cover Page

Cite item

Full Text

Abstract

The emergence of multicore architectures has extremely stimulated the area of parallel computing. However, developing a parallel program and manually paralleling inherited sequential program codes are time-consuming work. The programmer should have good skills in using parallel programming methods. This fact determines the relevance of the subject of the research – the development of a serial-to-parallel code translator. The article gives a review of existing solutions in the chosen direction of research and considers their advantages and disadvantages. The principle of formation of a syntactic tree which is based on JSON format (the text format of data exchange based on JavaScript) is offered and an example of formation of a syntactic tree on the basis of this principle is considered. The result of the work is an approach for building a program platform for translating sequential code into parallel code. The distinctive feature of the developed platform is the web-service, which potentially allows you to extend the translator with other programming languages. The interaction with the programming environment is realized by means of REST-requests (HTTP-requests designed to call remote procedures). The developed software platform consists of three modules: the query processing module, which provides interaction with external systems through REST-requests; the tree building module, which forms a syntax tree on the basis of the source program code; the code conversion module, which obtains parallel program code on the basis of the syntax tree.

References

  1. P. Czarnul, J. Proficz and K. Drypczewski, “Survey of methodologies, approaches, and challenges in parallel programming using high-performance computing systems,” Scientific Programming, vol. 2020, pp. 1058–9244, 2020.
  2. D. B. Changdao, I. Firmansyah and Y. Yamaguchi, “FPGA-based computational fluid dynamics simulation architecture via high-level synthesis design method,” in Applied Reconfigurable Computing. Architectures, Tools, and Applications: 16th Int. Symp., ARC 2020, Toledo, Spain, Springer Nature. vol. 12083, pp. 232, 2020.
  3. D. Wang and F. Yuan, “High-performance computing for earth system modeling,” High Performance Computing for Geospatial Applications, vol. 23, pp. 175–184, 2020.
  4. M. U. Ashraf, F. A. Eassa, A. Ahmad and A. Algarni, “Empirical investigation: Performance and power-consumption based dual-level model for exascale computing systems,” IET Software, vol. 14, no. 4, pp. 319–327, 2020.
  5. B. Brandon, “Message passing interface (mpi),” in Workshop: High Performance Computing on Stampede, Cornell University Center for Advanced Computing (CAC), vol. 262, 2015.
  6. A. Podobas and S. Karlsson, “Towards unifying openmp under the task-parallel paradigm,” in Int. Workshop on OpenMP. Springer International Publishing, Springer International Publishing, Nara, Japan, vol. 9903, pp. 116–129, 2016.
  7. M. U. Ashraf, F. Fouz and F. A. Eassa, “Empirical analysis of hpc using different programming models,” International Journal of Modern Education & Computer Science, vol. 8, no. 6, pp. 27–34, 2016.
  8. J. A. Herdman, W. P. Gaudin, S. Smith, M. Boulton, D. A. Beckingsale et al., “Accelerating hydrocodes with openacc, opencl and cuda,” High Performance Computing, Networking, Storage and Analysis (SCC), vol. 66, pp. 465–471, 2012.
  9. H. Jin, D. Jespersen, P. Mehrotra, R. Biswas, L. Huang et al., “High performance computing using mpi and openmp on multi-core parallel systems,” Parallel Computing, vol. 37, no. 9, pp. 562–575, 2011.
  10. M. Marangoni and T. Wischgoll, “Togpu: Automatic source transformation from c++ to cuda using clang/llvm,” Electronic Imaging, vol. 1, pp. 1–9, 2016.
  11. X. Xie, B. Chen, L. Zou, Y. Liu, W. Le et al., “Automatic loop summarization via path dependency analysis,” IEEE Transactions on Software Engineering, vol. 45, no. 6, pp. 537–557, 2017.
  12. M. S. Ahmed, M. A. Belarbi, S. Mahmoudi, G. Belalem and P. Manneback, “Multimedia processing using deep learning technologies, high-performance computing cloud resources, and big data volumes,” Concurrency and Computation: Practice and Experience, vol. 32, no. 17, pp. 56–99, 2020

Supplementary files

Supplementary Files
Action
1. JATS XML
Download

Согласие на обработку персональных данных

 

Используя сайт https://journals.rcsi.science, я (далее – «Пользователь» или «Субъект персональных данных») даю согласие на обработку персональных данных на этом сайте (текст Согласия) и на обработку персональных данных с помощью сервиса «Яндекс.Метрика» (текст Согласия).