Advertisement

Performance Evaluation of Two Load Balancing Algorithms on a Hybrid Parallel Architecture

  • Tiago M. do Nascimento
  • Rodrigo W. dos Santos
  • Marcelo LoboscoEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10421)

Abstract

Accelerated Processing Units (APUs) are an emerging architecture that integrates, in a single silicon chip, the traditional CPU and the GPU. Due to its heterogeneous architecture, APUs impose new challenges to data parallel applications that want to take advantage of all the processing units available on the hardware to minimize its execution time. Some standards help in the task of writing parallel code for heterogeneous devices, but it is not easy to find the data division between CPU and GPU that will minimize the execution time. In this context, this work further extends and details load balancing algorithms designed to be used in a data parallel problem. Also, a sensitivity analysis of the parameters used in our models was performed. The results have shown that the algorithms are effective in their purpose of improving the performance of an application on an heterogeneous environment.

Keywords

Load balancing Hybrid parallel architectures APU HPC 

References

  1. 1.
    The OpenACC application programming interface - version 2.5. Technical report (2015). OpenAcc.org
  2. 2.
    Branover, A., Foley, D., Steinman, M.: AMD fusion APU: Llano. IEEE Micro 32(2), 28–37 (2012)CrossRefGoogle Scholar
  3. 3.
    Hennessy, J.L., Patterson, D.A.: Computer Architecture: A Quantitative Approach, 5th edn. Morgan Kaufmann Publishers Inc., San Francisco (2011)zbMATHGoogle Scholar
  4. 4.
    Kirk, D.B., Hwu, W.W.: Programming Massively Parallel Processors: A Hands-on Approach, 2nd edn. Morgan Kaufmann Publishers Inc., San Francisco (2013)Google Scholar
  5. 5.
    Mattson, T., Sanders, B., Massingill, B.: Patterns for Parallel Programming, 1st edn. Addison-Wesley Professional, Reading (2004)zbMATHGoogle Scholar
  6. 6.
    Munshi, A., Gaster, B., Mattson, T.G., Fung, J., Ginsburg, D.: OpenCL Programming Guide, 1st edn. Addison-Wesley Professional, Reading (2011)Google Scholar
  7. 7.
    do Nascimento, T.M., de Oliveira, J.M., Xavier, M.P., Pigozzo, A.B., dos Santos, R.W., Lobosco, M.: On the use of multiple heterogeneous devices to speedup the execution of a computational model of the human immune system. Appl. Math. Comput. 267, 304–313 (2015)MathSciNetGoogle Scholar
  8. 8.
    do Nascimento, T.M., dos Santos, R.W., Lobosco, M.: On a dynamic scheduling approach to execute opencl jobs on apus. In: Osthoff, C., Navaux, P.O.A., Barrios Hernandez, C.J., Silva Dias, P.L. (eds.) CARLA 2015. CCIS, vol. 565, pp. 118–128. Springer, Cham (2015). doi: 10.1007/978-3-319-26928-3_9 Google Scholar
  9. 9.
    Pigozzo, A.B., Macedo, G.C., Santos, R.W., Lobosco, M.: On the computational modeling of the innate immune system. BMC Bioinform. 14(Suppl. 6), S7 (2013)CrossRefGoogle Scholar
  10. 10.
    Rocha, P.A.F., Xavier, M.P., Pigozzo, A.B., M. Quintela, B., Macedo, G.C., Santos, R.W., Lobosco, M.: A three-dimensional computational model of the innate immune system. In: Murgante, B., Gervasi, O., Misra, S., Nedjah, N., Rocha, A.M.A.C., Taniar, D., Apduhan, B.O. (eds.) ICCSA 2012. LNCS, vol. 7333, pp. 691–706. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-31125-3_52 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Tiago M. do Nascimento
    • 1
  • Rodrigo W. dos Santos
    • 1
  • Marcelo Lobosco
    • 1
    Email author
  1. 1.Graduate Program on Computational ModelingFederal University of Juiz de ForaJuiz de ForaBrazil

Personalised recommendations