Skip to main content

Transforming Applications from the Control Flow to the Dataflow Paradigm

  • Chapter
  • First Online:
Book cover DataFlow Supercomputing Essentials

Abstract

This chapter analyzes potentials of accelerating applications by transforming them from control flow to dataflow representation and mapping them directly to the hardware based on the FPGA. Firstly, potentials for improvements will be analyzed. Both reduction in execution time and power consumption will be analyzed. Transforming control flow to dataflow applications will be analyzed on a Huxley muscle model implemented using the dataflow approach.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. A. Kos, S. Tomažič, J. Salom, N. Trifunovic, M. Valero, and V. Milutinovic, “New Benchmarking Methodology and Programming Model for Big Data Processing,” International Journal of Distributed Sensor Networks, vol. 2015, Article ID 271752, pp. 1–7.

    Google Scholar 

  2. M. Flynn, O. Mencer, V., Milutinovic, G., Rakocevic, P., Stenstrom, M., Valero, and R., Trobec, “Moving from PetaFlops to PetaData,” Communications of the ACM, May 2013, pp. 39–43.

    Google Scholar 

  3. N. Trifunovic, V. Milutinovic, J. Salom, and A. Kos, “Paradigm Shift in Big Data SuperComputing: DataFlow vs. ControlFlow,” Journal of Big Data, 2015.

    Google Scholar 

  4. Blagojevic, et al, “A Systematic Approach to Generation of New Ideas for PhD Research in Computing,” Advances in Computers, Vol. 102, 2015.

    Google Scholar 

  5. A. Hurson and V. Milutinovic, “Special Issue on DataFlow SuperComputing,” Advances in Computers, Vol. 96, 2015.

    Google Scholar 

  6. V. Milutinovic, J. Salom, N. Trifunovic, and R. Giorgi, “Guide to DataFlow Supercomputing,” Springer International Publishing, 2015, pp. 1–129.

    Google Scholar 

  7. V. Milutinovic and A. Hurson, “Dataflow Processing,” Academic Press, 1st edition, 2015, pp. 1–266.

    Google Scholar 

  8. R. P. Feynman, “Lectures on Computation,” The ACM Digital Library, 1998.

    MATH  Google Scholar 

  9. T. Nowatzki, V. Gangadhar, and K. Sankaralingam, “Exploring the potential of heterogeneous von neumann/dataflow execution models,” Proceedings of the 42nd Annual International Symposium on Computer Architecture, June 13, 2015, ACM, pp. 298–310.

    Google Scholar 

  10. S. Stojanovic, D. Bojic, and M. Bojovic, “An Overview of Selected Heterogeneous and Reconfigurable Architectures,” Advances in Computers, Vol. 96, Burlington: Academic Press, 2015, pp. 1–45.

    Google Scholar 

  11. N. Korolija, T. Djukic, V. Milutinovic, and N. Filipovic, “Accelerating Lattice-Boltzman Method Using the Maxeler DataFlow Approach,” Transactions on Internet Research, Vol. 9, No. 2, July 2013, pp. 5–10.

    Google Scholar 

  12. S. Stojanovic, D. Bojic, and V. Milutinovic, “Solving Gross Pitaevskii Equation Using Dataflow Paradigm,” Transactions on Internet Research, Vol. 9, No. 2, July 2013.

    Google Scholar 

  13. A. Kos, V. Rankovic, and S. Tomazic, “Sorting networks on Maxeler dataflow supercomputing systems,” Advances in computers, Vol. 96. Amsterdam, Elsevier: Academic Press, cop, 2015, pp. 139–186.

    Google Scholar 

  14. V. Rankovic, A. Kos, and V. Milutinovic, “Bitonic Merge Sort Implementation on the Maxeler Dataflow Supercomputing System,” Transactions on Internet Research, Vol. 9, No. 2, July 2013, pp. 34–42.

    Google Scholar 

  15. J. Gustafson, “Reevaluating Amdahl’s law,” Communications of the ACM 31.5 (1988): 532–533.

    Article  Google Scholar 

  16. O. Pell, J. Bower, R. Dimond, O. Mencer, and M. J. Flynn “Finite Difference Wave Propagation Modeling on Special Purpose Dataflow Machines,” IEEE Transactions on Parallel and Distributed Systems, 2012, doi: 10.1109/TPDS.2012.198.

    Google Scholar 

  17. P. Marchetti, D. Oriato, O. Pell, A.M. Cristini, and D. Theis, “Fast 3D ZO CRS Stack,” 72nd European Association of Geoscientists and Engineers (EAGE) Conference, June 2010.

    Google Scholar 

  18. O. Lindtjorn, R. G. Clapp, O. Pell, O. Mencer, and M. J. Flynn, “Surviving the End of Scaling of Traditional Microprocessors in HPC,” IEEE HOT CHIPS 22, Stanford, USA, August 2010.

    Google Scholar 

  19. http://appgallery.maxeler.com/, web site visited on March 17, 2017.

  20. N. Trifunovic, V. Milutinovic, et al, “The Appgallery.Maxeler.com for BigData SuperComputing,” Journal of Big Data, Springer, 2016.

    Google Scholar 

  21. N. Korolija, J. Popovi, M. Cvetanovi, and M. Bojovi, “Dataflow Based Parallelization of Control-Flow Algorithms,” Creativity in Computing and Dataflow Supercomputing, Advances in Computers, Vol. 104, 2017.

    Google Scholar 

  22. S. Stojanovic et al., “Coupling finite element and huxley models in multiscale muscle modeling,” Bioinformatics and Bioengineering (BIBE), 2015 IEEE 15th International Conference on. IEEE, 2015.

    Google Scholar 

  23. M. Ivanovi et al., “Distributed multi-scale muscle simulation in a hybrid MPICUDA computational environment,” Simulation 92.1 (2016): 19–31.

    Article  Google Scholar 

  24. A. Kaplarevi-Malii, et al., “Employing phenomenological model in load-balancing optimization of parallel multi-scale muscle simulations,” Bioinformatics and Bioengineering (BIBE), 2015 IEEE 15th International Conference on. IEEE, 2015.

    Google Scholar 

  25. https://github.com/oandric/Huxley-Muscle-Model/tree/master/APP/EngineCode/src/huxleymusclemodel, web site visited on March 5, 2017.

  26. https://github.com/oandric/Huxley-Muscle-Model/blob/master/APP/CPUCode/HuxleyMuscleModelCpuCode.c, web site visited on March 5, 2017.

  27. https://github.com/oandric/Huxley-Muscle-Model/blob/master/APP/EngineCode/src/huxleymusclemodel/HuxleyMuscleModelKernel.maxj, web site visited on March 5, 2017.

  28. Blagojevic, V., et al, “A Systematic Approach to Generation of New Ideas for PhD Research in Computing,” Advances in Computers, Elsevier, Vol. 104, 2016, pp. 1–19.

    Google Scholar 

Download references

Acknowledgements

This research was supported by School of Electrical Engineering, Ministry of Education, Science, and Technological Development of the Republic of Serbia [TR32047] and Maxeler Technologies, Belgrade, Serbia.

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Milutinovic, V., Salom, J., Veljovic, D., Korolija, N., Markovic, D., Petrovic, L. (2017). Transforming Applications from the Control Flow to the Dataflow Paradigm. In: DataFlow Supercomputing Essentials. Computer Communications and Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-66128-5_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-66128-5_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-66127-8

  • Online ISBN: 978-3-319-66128-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics