Advertisement

Exploiting Throughput for Pipeline Execution in Streaming Image Processing Applications

  • F. Guirado
  • A. Ripoll
  • C. Roig
  • A. Hernàndez
  • E. Luque
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4128)

Abstract

There is a large range of image processing applications that act on an input sequence of image frames that are continuously received. Throughput is a key performance measure to be optimized when executing them. In this paper we propose a new task replication methodology for optimizing throughput for an image processing application in the field of medicine. The results show that by applying the proposed methodology we are able to achieve the desired throughput in all cases, in such a way that the input frames can be processed at any given rate.

Keywords

Number Replication Task Graph IVUS Imaging Image Processing Application Streaming Application 
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.
    Lee, M., Liu, W., Prasanna, V.K.: A Mapping Methodology for Designing Software Task Pipelines for Embedded Signal Processing. In: Proc. Workshop on Embedded HPC Systems and Applications of IPPS/SPDP 1998, pp. 937–944 (1998)Google Scholar
  2. 2.
    Hoang, P., Rabey, J.: Scheduling of DSP Programs onto Multiprocessors for Maximum Throughput. IEEE Trans. Signal Processing 41(6), 2225–2235 (1993)MATHCrossRefGoogle Scholar
  3. 3.
    Yang, M.-T., Kasturi, R., Sivasubramaniam, A.: A Pipeline-Based Approach for Scheduling Video Processing Algorithms on NOW. IEEE Trans. on Par. and Distr. Systems 14(2), 119–130 (2003)CrossRefGoogle Scholar
  4. 4.
    Choudhary, A., Liao, W.K., Weiner, D., Varshwey, P., Linderman, R., Linderman, M.: Design Implementation and Evaluation of Parallel Pipelined STAP on Parallel Computers. In: Proc. 12th Int. Parallel Processing Symposium. Florida, April, pp. 220–225 (1998)Google Scholar
  5. 5.
    Lee, M., Liu, W., Prasanna, V.K.: Parallel Implementation of a Class of Adaptive Signal Processing Applications. Algorithmica (30) 645–684 (2001)Google Scholar
  6. 6.
    Pujol, O., Radeva, P.: Supervised texture classification for intravascular tissue characterization. Handbook of Medical Imaging 2, 57–110 (2005)Google Scholar
  7. 7.
    Gil, D., Hernàndez, A., Rodríguez, O., Mauri, J., Radeva, P.: Statistical Strategy for Anisotropic Adventitia Modelling in IVUS. IEEE Trans. on Medical Imaging (in press)Google Scholar
  8. 8.
    Subhlok, J., Vongran, G.: Optimal Use of Mixed Task and Data Parallelism for Pipelined Computations. J. Par. Distr. Computing 60, 297–319 (2000)MATHCrossRefGoogle Scholar
  9. 9.
    Hwang, J.J., Chow, Y.C., Anger, F.D., Lee, C.Y.: Scheduling Precedence Task Graphs in Systems with Interprocessor Communication Times. SIAM J. Comp. 18(2), 244–257 (1989)MATHCrossRefMathSciNetGoogle Scholar
  10. 10.
    Guirado, F., Ripoll, A., Roig, C., Luque, E.: Performance Prediction Using an Application Oriented Mapping Tool. In: IEEE Proc. Euromicro Conf. on Parallel, Distributed and Network-based Processing (PDP), February 2004, pp. 184–191 (2004)Google Scholar
  11. 11.
    Guirado, F., Ripoll, A., Roig, C., Luque, E.: Exploitation of Parallelism for Applications with an Input Data Stream: Optimal Resource-Throughput Tradeoffs. In: IEEE Proc. Euromicro Conf. on Parallel, Distributed and Network-based Processing (PDP), February 2005, pp. 170–178 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • F. Guirado
    • 1
  • A. Ripoll
    • 2
  • C. Roig
    • 1
  • A. Hernàndez
    • 3
  • E. Luque
    • 2
  1. 1.Universitat de Lleida 
  2. 2.Univ. Autònoma of Barcelona 
  3. 3.Computer Vision CenterUniv. Autònoma of Barcelona 

Personalised recommendations