skip to main content
10.1145/2598394.2605689acmconferencesArticle/Chapter ViewAbstractPublication PagesgeccoConference Proceedingsconference-collections
technical-note

Working with OpenCL to speed up a genetic programming financial forecasting algorithm: initial results

Published:12 July 2014Publication History

ABSTRACT

The genetic programming tool EDDIE has been shown to be a successful financial forecasting tool, however it has suffered from an increase in execution time as new features have been added. Speed is an important aspect in financial problems, especially in the field of algorithmic trading, where a delay in taking a decision could cost millions. To offset this performance loss, EDDIE has been modified to take advantage of multi-core CPUs and dedicated GPUs. This has been achieved by modifying the candidate solution evaluation to use an OpenCL kernel, allowing the parallel evaluation of solutions. Our computational results have shown improvements in the running time of EDDIE when the evaluation was delegated to the OpenCL kernel running on a multi-core CPU, with speed ups up to 21 times faster than the original EDDIE algorithm. While most previous works in the literature reported significantly improvements in performance when running an OpenCL kernel on a GPU device, we did not observe this in our results. Further investigation revealed that memory copying overheads and branching code in the kernel are potentially causes of the (under-)performance of the OpenCL kernel when running on the GPU device.

References

  1. F. Allen and R. Karjalainen. Using genetic algorithms to find technical trading rules. Journal of Financial Economics, 51:245--271, 1999.Google ScholarGoogle ScholarCross RefCross Ref
  2. D. A. Augusto and H. J. Barbosa. Accelerated parallel genetic programming tree evaluation with OpenCL. Journal of Parallel and Distributed Computing, 73(1):86--100, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. M. Austin, G. Bates, M. Dempster, V. Leemans, and S. Williams. Adaptive systems for foreign exchange trading. Quantitative Finance, 4(4):37--45, 2004.Google ScholarGoogle ScholarCross RefCross Ref
  4. J. Backus. The syntax and semantics of the proposed international algebraic language of Zurich. In International Conference on Information Processing, pages 125--132. UNESCO, 1959.Google ScholarGoogle Scholar
  5. A. Delévacq, P. Delisle, M. Gravel, and M. Krajecki. Parallel ant colony optimization on graphics processing units. Journal of Parallel and Distributed Computing, 73(1):52--61, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. R. Edwards and J. Magee. Technical Analysis of Stock Trends. Taylor & Francis, 1992.Google ScholarGoogle Scholar
  7. M. A. Franco, N. Krasnogor, and J. Bacardit. Speeding up the evaluation of evolutionary learning systems using GPGPUs. In Proceedings of the 12th annual conference on Genetic and evolutionary computation, pages 1039--1046. ACM, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. M. Harris. Optimizing parallel reduction in CUDA.Google ScholarGoogle Scholar
  9. M. Hutter. JOCL API documentation.Google ScholarGoogle Scholar
  10. M. Kampouridis. An initial investigation of choice function hyper-heuristics for the problem of financial forecasting. In Evolutionary Computation (CEC), 2013 IEEE Congress on, pages 2406--2413, 2013.Google ScholarGoogle ScholarCross RefCross Ref
  11. M. Kampouridis, A. Alsheddy, and E. Tsang. On the investigation of hyper-heuristics on a financial forecasting problem. Annals of Mathematics and Artificial Intelligence, 2012. Accepted for Publication. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. M. Kampouridis and E. Tsang. EDDIE for investment opportunities forecasting: extending the search space of the GP. In Evolutionary Computation (CEC), 2010 IEEE Congress on, pages 1--8. IEEE, 2010.Google ScholarGoogle ScholarCross RefCross Ref
  13. M. Kampouridis and E. Tsang. Investment Opportunities Forecasting: Extending the Grammar of a GP-based Tool. International Journal of Computational Intelligence Systems, 5(3):530--541, 2012.Google ScholarGoogle ScholarCross RefCross Ref
  14. J. Koza. Genetic Programming: On the programming of computers by means of natural selection. Cambridge, MA: MIT Press, 1992. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. W. B. Langdon. Graphics processing units and genetic programming: an overview. Soft Computing, 15(8):1657--1669, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. S. Martinez-Jaramillo. Artificial Financial Markets: An agent-based Approach to Reproduce Stylized Facts and to study the Red Queen Effect. PhD thesis, CFFEA, University of Essex, 2007.Google ScholarGoogle Scholar
  17. Oracle. java.nio.bytebuffer API.Google ScholarGoogle Scholar
  18. R. Poli, W. Langdon, and N. McPhee. A Field Guide to Genetic Programming. Lulu.com, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. T. Puźzniakowski and M. A. Bednarczyk. Towards an OpenCL implementation of genetic algorithms on gpus. pages 190--203, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. H. Wang and A. S. Weigend. Data mining for financial decision making. Decision support systems, 37(4):457--460, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Working with OpenCL to speed up a genetic programming financial forecasting algorithm: initial results

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Conferences
      GECCO Comp '14: Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation
      July 2014
      1524 pages
      ISBN:9781450328814
      DOI:10.1145/2598394

      Copyright © 2014 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 12 July 2014

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • technical-note

      Acceptance Rates

      GECCO Comp '14 Paper Acceptance Rate180of544submissions,33%Overall Acceptance Rate1,669of4,410submissions,38%

      Upcoming Conference

      GECCO '24
      Genetic and Evolutionary Computation Conference
      July 14 - 18, 2024
      Melbourne , VIC , Australia

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader