Formalization of the concept of adaptive tasks mapping in the reconfigurable computers on FPGA

Authors

DOI:

https://doi.org/10.15587/1729-4061.2018.127361

Keywords:

reconfigurable computer systems, partial dynamic reconfiguration, reconfiguration overheads, tasks mapping

Abstract

The effectiveness of data processing in the reconfigurable computer systems depends significantly on the unproductive time costs of the reconfiguration of the FPGA computational space. It is an important modern problem that hinders the intensive progress of reconfigurable computations. The aim of the research is to improve the efficiency of the process of tasks mapping into the reconfigurable computing structure of the dynamically RCs by reducing the communication delays when the reconfiguring FPGA space in the Run Time mode.

Mathematical models for determining the main efficiency criteria of the dynamically RCs and estimating the execution time of the main stages of adaptive tasks mapping that take into consideration the influence of delays of the configuration data transfer at all organization levels of the system are proposed. The concept of adaptive tasks mapping into the dynamically reconfigurable FPGA space based on the new approach to the transformation of algorithms’ MDG and the multilevel configuration data caching is proposed and formalized. That allows the realization of various strategies of adaptive tasks mapping based on the criteria of overhead time minimization considering FPGA hardware limitations and parameters of the changing computing environment during the tasks mapping. The experiments showed that the use of adaptive tasks mapping allows to reduce the overhead time and increase the effectiveness of reconfigurable computations for executing the algorithms with frequent repetition of similar tasks.

Author Biographies

Iryna Klymenko, National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” Peremohy ave., 37, Kyiv, Ukraine, 03056

Doctor of Technical Sciences, Associate Professor

Department of Computer Engineering

Valentyna Tkachenko, National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” Peremohy ave., 37, Kyiv, Ukraine, 03056

PhD, Associate Professor

Department of Computer Engineering

Anastasia Serhienko, National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” Peremohy ave., 37, Kyiv, Ukraine, 03056

Postgraduate student

Department of System Programming and Specialized Computer Systems

Yurii Kulakov, National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” Peremohy ave., 37, Kyiv, Ukraine, 03056

Doctor of Technical Sciences, Professor

Department of Computer Engineering

References

  1. Kumar, S. (2015). Fundamental limits to Moore's law. arXiv. 2015. Available at: https://www.researchgate.net/profile/Suhas_Kumar5/publication/284219009_Fundamental_Limits_to_Moore's_Law/links/5663fd9408ae192bbf901e85.pdf
  2. Dondo Gazzano, J., Rincon, F., Vaderrama, C., Villanueva, F., Caba, J., Lopez, J. C. (2014). Facilitating Preemptive Hardware System Design Using Partial Reconfiguration Techniques. The Scientific World Journal, 2014, 1–15. doi: 10.1155/2014/164059
  3. Koch, D. (2013). Partial reconfiguration on FPGAs. Architectures, tools and applications. Springer-Verlag, 296. doi: 10.1007/978-1-4614-1225-0
  4. Melnyk, V. (2016). Self-configurable FPGA-based computer systems: basics and proof of concept. Advances in cyber-physical systems, 1 (1), 39–50.
  5. Singh, S., Saurav, S., Shekhar, C., Vohra, A. (2016). Prototyping an Automated Video Surveillance System Using FPGAs. International Journal of Image, Graphics and Signal Processing, 8 (8), 37–46. doi: 10.5815/ijigsp.2016.08.06
  6. Mentens, N., Vandorpe, J., Vliegen, J., Braeken, A., da Silva, B., Touhafi, A. et. al. (2015). DynamIA: Dynamic Hardware Reconfiguration in Industrial Applications. Applied Reconfigurable Computing, 513–518. doi: 10.1007/978-3-319-16214-0_47
  7. Minaev, Yu., Filimonova, O. (2008). Fuzzy Mathematics on the Basic of Uncertainty Tensor Models. Chapter I. Tensor-variable in the Fuzzy Set System. Elektronnoe modelirovanie, 30 (1), 43–59.
  8. Guerra, R., Martel, E., Khan, J., Lopez, S., Athanas, P., Sarmiento, R. (2017). On the Evaluation of Different High-Performance Computing Platforms for Hyperspectral Imaging: An OpenCL-Based Approach. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10 (11), 4879–4897. doi: 10.1109/jstars.2017.2737958
  9. Rajasekhar, Y., Sass, R. (2012). Architecture and Applications for an All-FPGA Parallel Computer. 2012 41st International Conference on Parallel Processing Workshops. doi: 10.1109/icppw.2012.22
  10. George, A., Lam, H., Stitt, G. (2011). Novo-G: At the Forefront of Scalable Reconfigurable Supercomputing. Computing in Science & Engineering, 13 (1), 82–86. doi: 10.1109/mcse.2011.11
  11. Iturbe, X., Benkrid, K., Hong, C., Ebrahim, A., Arslan, T., Martinez, I. (2013). Runtime Scheduling, Allocation, and Execution of Real-Time Hardware Tasks onto Xilinx FPGAs Subject to Fault Occurrence. International Journal of Reconfigurable Computing, 2013, 1–32. doi: 10.1155/2013/905057
  12. Al-Wattar, A., Areibi, S., Saffih, F. (2012). Efficient On-line Hardware/Software Task Scheduling for Dynamic Run-time Reconfigurable Systems. 2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops & PhD Forum. doi: 10.1109/ipdpsw.2012.50
  13. Liu, S., Pittman, R. N., Forin, A., Gaudiot, J.-L. (2013). Achieving energy efficiency through runtime partial reconfiguration on reconfigurable systems. ACM Transactions on Embedded Computing Systems, 12 (3), 1–21. doi: 10.1145/2442116.2442122
  14. Klymenko, I., Rudnytsky, M. (2014). Classification of reconfigurable computing systems. Visnyk of Vinnytsia Politechnical Institute, 5 (116), 120–128.
  15. Jing, C., Zhu, Y., Li, M. (2013). Energy-efficient scheduling on multi-FPGA reconfigurable systems. Microprocessors and Microsystems, 37 (6-7), 590–600. doi: 10.1016/j.micpro.2013.05.001
  16. Sergiyenko, A. Klymenko, I., Sergiyenko, P. (2016). Reconfigurable many-core computer based on FPGA. Visnyk NTUU “KPI”. Informatyka, upravlinnia ta obtchislyuvalna technika, 64, 47–50.
  17. Klymenko, I. A. (2015). The effectiveness analysis of resources management in reconfigurable computer systems. Visnyk NTUU “KPI”. Informatyka, upravlinnia ta obtchislyuvalna technika, 62, 11–21.
  18. Smith, M. C., Peterson, G. D. (2012). Optimization of Shared High-Performance Reconfigurable Computing Resources. ACM Transactions on Embedded Computing Systems, 11 (2), 1–22. doi: 10.1145/2220336.2220348
  19. Dunets, R., Tykhanskyy, D. (2010). Problems of partially reconfigurable FPGA-based system design. Radioelectronic and computer systems, 7 (48), 200–204.
  20. Ahmed, W., Shafique, M., Bauer, L., Henkel, J. (2011). Adaptive resource management for simultaneous multitasking in mixed-grained reconfigurable multicore processors. CODES+ISSS '11 Proceedings of the seventh IEEE/ACM/IFIP international conference on Hardware/software codesign and system synthesis. Taipei, Taiwan, 365–374.
  21. Happe, M., Traber, A., Keller, A. (2015). Preemptive Hardware Multitasking in ReconOS. Applied Reconfigurable Computing, 79–90. doi: 10.1007/978-3-319-16214-0_7
  22. Dümmler, J., Rauber, T., Rünger, G. (2009). Scalable computing with parallel tasks. Proceedings of the 2nd Workshop on Many-Task Computing on Grids and Supercomputers – MTAGS ’09. doi: 10.1145/1646468.1646477
  23. Kulakov, Y. O., Klymenko, I. A., Rudnytskyi, M. V. (2015). The method for providing quality of service time requirements in reconfigurable computing systems. Eastern-European Journal of Enterprise Technologies, 4 (4 (76)), 25–30. doi: 10.15587/1729-4061.2015.47227
  24. Kulakov, Y. O., Klymenko, I. A. (2014). The multilevel memory in the reconfigurable computing system. Visnyk NTUU «KPI». Informatyka, upravlinnia ta obchislyuvalna technika, 61, 18–26.
  25. Klymenko, I., Kulakov, Y., Tkachenko, V., Storozhuk, O. (2016). The method for providing quality of service time requirements in reconfigurable computing systems. Eastern-European Journal of Enterprise Technologies, 5 (9 (83)), 4–12. doi: 10.15587/1729-4061.2016.81003

Downloads

Published

2018-03-30

How to Cite

Klymenko, I., Tkachenko, V., Serhienko, A., & Kulakov, Y. (2018). Formalization of the concept of adaptive tasks mapping in the reconfigurable computers on FPGA. Eastern-European Journal of Enterprise Technologies, 2(9 (92), 20–28. https://doi.org/10.15587/1729-4061.2018.127361

Issue

Section

Information and controlling system