The Journal of Supercomputing

, Volume 58, Issue 2, pp 151–159 | Cite as

Automatic tuning of iterative computation on heterogeneous multiprocessors with ADITHE

  • J. A. Martínez
  • E. M. GarzónEmail author
  • A. Plaza
  • I. García


This work studies the problem of balancing the workload of iterative algorithms on heterogeneous multiprocessors. An approach, called ADITHE, is proposed and evaluated. Its main features are: (1) using a homogeneous distribution of the workload on the heterogeneous system, the speed of every node is estimated during the first iterations of the algorithm; (2) according to the speed of every node, a new workload distribution is carried out; (3) the remaining iterations of the algorithm are executed. The result of this workload redistribution is that the execution times for every iteration at every node are similar and, consequently, the penalties due to synchronization between nodes at every iteration are mostly eliminated. This approach is appropriate for iterative algorithms with similar workload at every iteration, and with a relevant number of iterations. The high portability of ADITHE is guaranteed because the estimation of speed of nodes is included in the execution of the parallel algorithm. There is a wide variety of iterative algorithms related to science and engineering which can take advantage of ADITHE. An example of this kind of algorithms (morphological processing of hyperspectral images) is considered in this work to evaluate its performance when ADITHE is applied. The analysis of the results shows that ADITHE significantly improves the performance of parallel iterative algorithms on heterogeneous platforms.


Parallel iterative algorithms Heterogeneous multiprocessors 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bosque JL, Pastor L (2006) A parallel computational model for heterogeneous clusters. IEEE Trans Parallel Distrib Syst 17(12):1390–1400 CrossRefGoogle Scholar
  2. 2.
    Chang C-I (2003) Hyperspectral imaging: techniques for spectral detection and classification. Kluwer, New York Google Scholar
  3. 3.
    Chen Y, Xian-He S, Wu M (2008) Algorithm-system scalability of heterogeneous computing. J Parallel Distrib Comput 68(11):1403–1412 CrossRefGoogle Scholar
  4. 4.
    Galindo I, Almeida F, Blanco V, Badía-Contelles JM (2008) Dynamic load balancing on dedicated heterogeneous systems. In: Recent advances in parallel virtual machine and message passing interface. LNCS, vol 5205. Springer, Berlin, pp 64–74 CrossRefGoogle Scholar
  5. 5.
    Kalinov A (2006) Scalability of heterogeneous parallel systems. Program Comput Softw 32(1):1–7 MathSciNetzbMATHCrossRefGoogle Scholar
  6. 6.
    Lastovetsky A (2003) Parallel computing on heterogeneous networks. Wiley, Hoboken zbMATHCrossRefGoogle Scholar
  7. 7.
    Plaza A, Martinez P, Perez R, Plaza J (2002) Spatial/spectral endmember extraction by multi-dimensional morphological operations. IEEE Trans Geosci Remote Sens 40:2025–2041 CrossRefGoogle Scholar
  8. 8.
    Plaza A, Martinez P, Perez R, Plaza J (2004) A quantitative and comparative analysis of endmember extraction algorithms from hyperspectral data. IEEE Trans Geosci Remote Sens 42:650–663 CrossRefGoogle Scholar
  9. 9.
    Plaza A, Martinez P, Plaza J, Perez R (2005) Dimensionality reduction and classification of hyperspectral image data using sequences of extended morphological transformations. IEEE Trans Geosci Remote Sens 43:466–479 CrossRefGoogle Scholar
  10. 10.
    Serra J (1982) Image analysis and mathematical morphology. Academic Press, New York zbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • J. A. Martínez
    • 1
  • E. M. Garzón
    • 1
    Email author
  • A. Plaza
    • 2
  • I. García
    • 1
  1. 1.Department of Computer Architecture and ElectronicsUniversity of AlmeríaAlmeríaSpain
  2. 2.Department of Technology of Computers and CommunicationsUniversity of ExtremaduraCáceresSpain

Personalised recommendations