Monte Carlo Radiative Heat Transfer Simulation on a Reconfigurable Computer

  • Maya Gokhale
  • Janette Frigo
  • Christine Ahrens
  • Justin L. Tripp
  • Ron Minnich
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3203)


Recently, the appearance of very large (3 – 10M gate) FPGAs with embedded arithmetic units has opened the door to the possibility of floating point computation on these devices. While previous researchers have described peak performance or kernel matrix operations, there is as yet relatively little experience with mapping an application-specific floating point loop onto FPGAs. In this work, we port a supercomputer application benchmark onto Xilinx Virtex II and Virtex II Pro FPGAs and compare performance with three Pentium IV Xeon microprocessors. Our results show that this application-specific pipeline, with 12 multiply, 10 add/subtract, one divide, and two compare modules of single precision floating point data type, shows speed up of 10.37×. We analyze the trade-offs between hardware and software to characterize the algorithms that will perform well on current and future FPGA architectures.


Radiative Heat Transfer Point Library Loop Iteration Single Precision Spatial Parallelism 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Underwood, K.: FPGAs vs. CPUs: Trends in peak floating-point performance. In: ACM/SIGDA Twelfth ACM International Symposium on Field-Programmable Gate Arrays, FPGA 2004 (2004)Google Scholar
  2. 2.
    Seventh Annual Workshop on High Performance Embedded Computing (HPEC 2003), Area and Power Performance Analysis of Floating-point based Application on FPGAs (Lexington, MA) (September 2003)Google Scholar
  3. 3.
    Choi, S., Prasanna, V.: Time and energy efficient matrix factorization using fpgas. In: Y. K. Cheung, P., Constantinides, G.A. (eds.) FPL 2003. LNCS, vol. 2778, Springer, Heidelberg (2003)CrossRefGoogle Scholar
  4. 4.
    Top 500, Top 500 supercomputer sites (2004),
  5. 5.
    Burns, P.J., Pryor, D.V.: Vector and parallel monte carlo radiative heat transfer simulation. Numerical Heat Transfer 16 (1989)Google Scholar
  6. 6.
    Shirazi, N., Walters, A., Athanas, P.: Quantitative analysis of floating point arithmetic of FPGA based custom computing machines. In: IEEE Symposium on Field-Programmable Custom Computing Machines, Napa, CA, pp. 155–162. IEEE Computer Society Press, Los Alamitos (1995)Google Scholar
  7. 7.
    Ligon III, W.B., McMillan, S., Monn, G., Schoonover, K., Stivers, F., Underwood, K.D.: A re-evaluation of the practicality of floating-point operations on fpgas. In: IEEE Symposium on Field-Programmable Custom Computing Machines, Napa, CA, April 1998, pp. 206–215. IEEE Computer Society Press, Los Alamitos (1998)Google Scholar
  8. 8.
    Belanovic, P., Leeser, M.: A library of parameterized floating-point modules and their use. In: Glesner, M., Zipf, P., Renovell, M. (eds.) FPL 2002. LNCS, vol. 2438, pp. 657–666. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  9. 9.
    Nichols, K.R., Moussa, M.A., Areibi, S.M.: Feasibility of floating-point arithmetic in FPGA based artificial neural networks. In: CAINE 2002 (November 2002)Google Scholar
  10. 10.
    Roesler, E., Nelson, B.: Novel optimizations for hardware floating-point units. In: Glesner, M., Zipf, P., Renovell, M. (eds.) FPL 2002. LNCS, vol. 2438, pp. 637–646. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  11. 11.
    QinetiQ Holdings Ltd., Real time systems lab. (2002),
  12. 12.
    Nallatech, Floating point IP cores for virtex-II (2003),
  13. 13.
    Detrey, J., de Dinechin, F.: FPLibrary, a VHDL library of parametrisable floating-point and LNS operators for FPGA (2004),
  14. 14.
    Minnich, R., Pryor, D.V.: A radiative heat transfer simulation on a SPARCStation farm. In: First International Symposium on High Performance Distributed Computing, HPDC 1992 (1992)Google Scholar
  15. 15.
    Gokhale, M.B., Stone, J.M., Arnold, J., Kalinowski, M.: Stream-oriented fpga computing in the streams-c high level language. In: Proceedings of the IEEE Symposium on Field-Programmable Custom Computing Machines, Napa, CA (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Maya Gokhale
    • 1
  • Janette Frigo
    • 1
  • Christine Ahrens
    • 1
  • Justin L. Tripp
    • 1
  • Ron Minnich
    • 1
  1. 1.Los Alamos National Laboratory 

Personalised recommendations