Studying the Performance of Vector-Based Quicksort Algorithm

  • Ami MarowkaEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12044)


The performance of parallel algorithms is often inconsistent with their preliminary theoretical analyses. Indeed, the difference is increasing between the ability to theoretically predict the performance of a parallel algorithm and the results measured in practice. This is mainly due to the accelerated development of advanced parallel architectures, whereas there is still no agreed model for parallel computation, which has implications for the design of parallel algorithms.

In this study, we examined the practical performance of Cormen’s Quicksort parallel algorithm. We determined the performance of the algorithm with different parallel programming approaches and examine the capacity of theoretical performance analyses of the algorithm for predicting the actual performance.


Python Quicksort Performance modeling 


  1. 1.
    Marowka, A.: Pitfalls and issues of many-core programming. Adv. Comput. 79, 71–117 (2010)CrossRefGoogle Scholar
  2. 2.
    Cormen, T.H.: Chapter 9: Parallel computing in a Python-based computer science course. In: Prasad, S.K., et al. (eds.) Topics in Parallel and Distributed Computing: Introducing Concurrency in Undergraduate Courses. Morgan Kaufmann (2015)Google Scholar
  3. 3.
    Blelloch, G.E.: Scan primitives and parallel vector models. Ph.D. dissertation, Massachusetts Institute of Technology (1988)Google Scholar
  4. 4.
    Blelloch, G.E.: Vector Models for Data-Parallel Computing. The MIT Press, Cambridge (1990)Google Scholar
  5. 5.
  6. 6.
  7. 7.
  8. 8.
  9. 9.
  10. 10.

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Parallel Research LabJerusalemIsrael

Personalised recommendations