Skip to main content
SpringerLink
Log in

Selected Case Studies

  • Chapter
Guide to DataFlow Supercomputing

Part of the book series: Computer Communications and Networks ((CCN))

  • 984 Accesses

Abstract

Following the first chapter that presents the DataFlow computer architecture paradigms, this chapter sheds more light on DataFlow applications. There are a lot of publicly available research papers and other sources that portray how successful transfers of applications from control flow to DataFlow architectures can be in terms of speed, power consumption, and equipment size. At the beginning of this chapter, a novel classification of typical DataFlow applications is presented, which is in line with the most recent proposals of the European FP7/H2020 initiative. Then, some of most indicative papers are presented grouped according to described classification. The most representative single article from each available classification group is in detail described followed by other articles from the same group that are shortly introduced stating the topic, content, and acquired results (speedups, power reductions, other improvements, etc.).

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 54.99
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. Arram J et al (2013) Hardware acceleration of genetic sequence alignment. In: Proceedings of the 9th international symposium on reconfigurable computing: architectures, tools and applications, Los Angeles, CA, 2013. pp 13–24

    Google Scholar 

  2. Bezanic N et al (2013) Implementation of the RSA algorithm on a dataflow architecture. IPSI Trans Intern Res Belgrade Serbia 9(2):11–18

    Google Scholar 

  3. Chow GCT et al (2012) A mixed precision Monte Carlo methodology for reconfigurable accelerator systems. In: Proceedings of the ACM/SIGDA international symposium on Field Programmable Gate Arrays (FPGA), Monterey, CA, 2012, pp 57–66

    Google Scholar 

  4. Chau TCP et al. (2013) Heterogeneous reconfigurable system for adaptive particle filters in real-time applications. In: Proceedings of the 9th international symposium on reconfigurable computing: architectures, tools and applications, Los Angeles, CA, 2013, pp 1–12

    Google Scholar 

  5. Cheung KS, Schultz R, Luk W (2012) A large-scale spiking neural network accelerator for FPGA systems. In: Proceedings of the 2nd international conference on artificial neural networks, Lausanne, Switzerland, 2012, Part I, pp 113–120

    Google Scholar 

  6. Dimond D, Racaniere S, Pell O (2011) Accelerating large-scale HPC applications using FPGAs. In: Proceedings of the 20th IEEE symposium on computer arithmetic (ARITH), Tubingen, Germany, 2011, pp 191–192.

    Google Scholar 

  7. Flynn M et al (2008) Finding speedup in parallel processors. In: Proceedings of the international symposium on parallel and distributed computing, Krakow, Poland, 2008, pp 3–7

    Google Scholar 

  8. Fu H, Clapp RG, Mencer O, Pell O (2009) Accelerating 3D convolution using streaming architectures on FPGAs. In: SEG Houston 2009 international exposition and annual meeting, Houston, Texas, 2009.

    Google Scholar 

  9. Guo C, Fu H, Luk W (2012) A fully-pipelined expectation-maximization engine for Gaussian mixture models. In: Proceedings of the 2012 international conference on field-programmable technology (FPT), Seoul, S. Korea, 2012, pp 182–189

    Google Scholar 

  10. Guo L et al (2013) Customizable architectures for the set covering problem. Comput Architect News ACM Sigarch, New York 41(5):101–106

    Google Scholar 

  11. Ivkovic S et al (2013) Source-sink model. IPSI Trans Inter Res Belgrade Serbia 9(2):28–33

    Google Scholar 

  12. Jin Q et al. (2012) Multi-level customization framework for curve based Monte Carlo financial simulations. In: Proc. 8th international conference on reconfigurable computing: architectures, tools and applications, Hong Kong, 2012, pp 187–201

    Google Scholar 

  13. Korolija N et al (2013) Accelerating Lattice-Boltzmann method using the Maxeler DataFlow approach. IPSI Trans Internet Res Belgrade Serbia 9(2):34–42

    Google Scholar 

  14. Kumar N, Satoor S,, Buck I (2009) Fast parallel expectation maximization for Gaussian mixture models on GPUs using CUDA. In: EEE international conference on high performance computing and communications, 2009, pp 103–109

    Google Scholar 

  15. Lindtjorn O et al (2011) Beyond traditional microprocessors for geoscience high-performance computing applications. Micro IEEE 31(2):41–49

    Article  Google Scholar 

  16. Liu W et al (2009) Anisotropic reverse-time migration using co-processors. SEG Houston 2009 international exposition and annual meeting, Houston, Texas, 2009

    Google Scholar 

  17. Marrocu M, Scardovelli R, Malguzzi P (1998) Parallelization and Performance of a Meteorological Limited Area Model. Parallel Comput 24(5–6):911–922

    Google Scholar 

  18. Mencer O et al (2011) Finding the Right Level of Abstraction for Minimizing Operational Expenditure. iN: Proceedings of the 4th workshop on high performance computational finance, ACM New York, NY, 2011, pp 13–18

    Google Scholar 

  19. Nemeth T et al. (2008) An implementation of the acoustic wave equation on FPGAs. In: Proceedings of the 78th Society of Exploration Geophysicists (SEG) meeting, Las Vegas, November 2008, pp 2874–2878

    Google Scholar 

  20. Oriato D et al (2012) Acceleration of a meteorological limited area model with dataflow engines. In: Proceedings of the 2012 symposium on application accelerators in high performance computing, Chicago, IL, July, 2012, pp 129–132

    Google Scholar 

  21. Pell O, Mencer O (2011) Surviving the end of frequency scaling with reconfigurable dataflow computing. Newsletter ACM SIGARCH Comput Architect News Arch 39(4):60–65. ACM, New York, NY

    Google Scholar 

  22. Pell O, Averbukh V (2012) Maximum performance computing with dataflow engines. Comput Sci Eng 14(4):98–103, Los Alamitos, CA

    Google Scholar 

  23. Pell O et al (2013) Finite-difference wave propagation modeling on special-purpose dataflow machines. IEEE Trans Parallel Distrib Syst 24(5):906–915

    Article  Google Scholar 

  24. Pell O et al (2014) Summary FD modeling beyond 70Hz with FPGA acceleration [Online]. Available: http://www.maxeler.com/media/documents/MaxelerSummaryFDModelingBeyond70Hz.pdf

  25. Rafique A, Kapre N, Constantinides GA (2012) Enhancing performance of Tall-Skinny Qr Factorization using FPGAs. In: Proceedings of the 22nd international conference on Field Programmable Logic and applications (FPL), Oslo, Norway, August 2012, pp 443–450

    Google Scholar 

  26. Rankovic V, Kos A, Milutinovic V (2013) Bitonic merge sort implementation on the Maxeler Dataflow supercomputing system. IPSI Trans Internet Res Belgrade Serbia 9(2):5–9

    Google Scholar 

  27. Ruan H et al (2014) A fully pipelined probability density function engine for Gaussian Copula model. Tsinghua Sci Technol Tsinghua Univ Press (TUP) 19(2):194–202

    Article  Google Scholar 

  28. Stojanovic S, Bojic D, Milutinovic V (2013) Solving Gross Pitaevskii equation using DataFlow paradigm. IPSI Trans Internet Res Belgrade Serbia 9(2):19–22

    Google Scholar 

  29. Stanojevic I, Senk V, Milutinovic V (2013) Application of Maxeler Dataflow supercomputing to spherical code design. IPSI Trans Internet Res Belgrade Serbia 9(2):1–4

    Google Scholar 

  30. Sustran Z, Todorovic M, Milutinovic V (2013) Feasibility study on the SAT solver on DataFlow architecture. IPSI Trans Internet Res Belgrade Serbia 9(2):23–27

    Google Scholar 

  31. Tomas C et al. (2012) Acceleration of anisotropic phase shift plus interpolation with dataflow engines. In: Proceedings of the 82nd annual meeting and international exposition of the Society of Exploration Geophysics-SEG, Las Vegas, NE, 2012, pp 3402–3406

    Google Scholar 

  32. Tse HT (Anson) (2012) Accelerating reconfigurable financial computing. PhD thesis, Imperial College, London, G. Britain, 2012

    Google Scholar 

  33. Tse AHT et al (2012) Optimizing performance of quadrature methods with reduced precision. In: Proceedings of the 8th international symposium ARC 2012, Hong Kong, China, March, 2012, pp 251–263

    Google Scholar 

  34. Weston S et al (2010) Accelerating the computation of portfolios of Tranched credit derivatives. In: Proceedings of the 2010 IEEE Workshop on High Performance Computational Finance (WHPCF), New Orleans, LA, November 2010, pp 1–8

    Google Scholar 

  35. Weston S et al (2012) Rapid computation of value and risk for derivatives portfolios. Concurr Comput Pract Exp 24(8):880–894

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Electrical Engineering, University of Belgrade, Belgrade, Serbia

    Veljko Milutinović

  2. MISANU, Belgrade, Serbia

    Jakob Salom

  3. Maxeler Technologies Inc., Palo Alto, CA, USA

    Nemanja Trifunovic

  4. University of Siena, Siena, Italy

    Roberto Giorgi

Authors
  1. Veljko Milutinović
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jakob Salom
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nemanja Trifunovic
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Roberto Giorgi
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Milutinović, V., Salom, J., Trifunovic, N., Giorgi, R. (2015). Selected Case Studies. In: Guide to DataFlow Supercomputing. Computer Communications and Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-16229-4_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-16229-4_2

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16228-7

  • Online ISBN: 978-3-319-16229-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation