Advertisement

Case Study of a Functional Genomics Application for an FPGA-Based Coprocessor

  • Tom Van Court
  • Martin C. Herbordt
  • Richard J. Barton
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2778)

Abstract

Although microarrays are already having a tremendous impact on biomedical science, they still present great computational challenges. We examine a particular problem involving the computation of linear regressions on a large number of vector combinations in a high-dimensional parameter space, a problem that was found to be virtually intractable on a PC cluster. We observe that characteristics of this problem map particularly well to FPGAs and confirm this with an implementation that results in a 1000-fold speed-up over a serial implementation. Other contributions involve the data routing structure, the analysis of bit-width allocation, and the handling of missing data. Since this problem is representative of many in functional genomics, part of the overall significance of this work is that it points to a potential new area of applicability for FPGA coprocessors.

Keywords

Healthy Sample FPGA Implementation Serial Implementation Field Programmable Logic Block Multiplier 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Hansen, E., et al.: Topics in Interval Analysis. Clarendon Press, Oxford (1969)Google Scholar
  2. 2.
    Hartman, A.: The fundamental construction for 3-designs. Discrete Mathematics 124, 107–132 (1994)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Kim, S.: Finding Genes for Cancer Classification: Many Genes and Small Number of Samples. In: 2nd Ann. Houston Forum on Cancer Genomics and Informatics (2001)Google Scholar
  4. 4.
    Kohane, I.S., Kho, A.T., Butte, A.J.: Microarrays for an Integrative Genomics. MIT Press, Cambridge (2003)Google Scholar
  5. 5.
    Little, R.J.A., Rubin, D.B.: Statistical Analysis with Missing Data. John Wiley and Sons, Hoboken (2002)Google Scholar
  6. 6.
    Mahlke, S., Ravindran, R., Schlansker, M., Schreiber, R., Sherwood, T.: Bitwidth Cognizant Architecture Synthesis of Custom Hardware Accelerators. IEEE Trans. on CAD of Integrated Circuits and Systems 20, 1355–1370 (2001)CrossRefGoogle Scholar
  7. 7.
    Perou, C.M., et al.: Molecular Portraits of Human Breast Tumors. Nature 406, 747–752 (2000)CrossRefGoogle Scholar
  8. 8.
    Ryan, T.P.: Modern Regression Methods. John Wiley and Sons, Inc., New York (1997)Google Scholar
  9. 9.
    Xilinx, Inc.: Virtex-II Pro Platform FPGA User Guide (2002)Google Scholar
  10. 10.
    Xilinx, Inc.: Integrated Software Environment (2002)Google Scholar
  11. 11.
    Yamaguchi, Y., Miyajima, Y., Maruyama, T., Konagaya, A.: High Speed Homology Search Using Run-Time Reconfiguration. In: Glesner, M., Zipf, P., Renovell, M. (eds.) FPL 2002. LNCS, vol. 2438, pp. 281–291. Springer, Heidelberg (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Tom Van Court
    • 1
  • Martin C. Herbordt
    • 1
  • Richard J. Barton
    • 2
  1. 1.Department of Electrical and Computer EngineeringBoston UniversityBostonUSA
  2. 2.Department of Electrical and Computer EngineeringUniversity of HoustonHoustonUSA

Personalised recommendations