de.mpg.escidoc.pubman.appbase.FacesBean
English

# Item

ITEM ACTIONSEXPORT

Released

Conference Paper

#### Practical Parallel List Ranking

##### MPS-Authors

Sibeyn,  Jop F.
Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons44543

Guillaume,  Frank
Algorithms and Complexity, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45450

Seidel,  Tillmann
Algorithms and Complexity, MPI for Informatics, Max Planck Society;
Programming Logics, MPI for Informatics, Max Planck Society;

##### Locator
There are no locators available
##### Fulltext (public)
There are no public fulltexts available
##### Supplementary Material (public)
There is no public supplementary material available
##### Citation

Sibeyn, J. F., Guillaume, F., & Seidel, T. (1997). Practical Parallel List Ranking. In G. Bilardi, A. Ferreira, R. Lüling, & J. Rolim (Eds.), Proceedings of the 4th Symposium on Solving Irregularly Structured Problems in Parallel (IRREGULAR-97) (pp. 25-36). Berlin: Springer.

Cite as: http://hdl.handle.net/11858/00-001M-0000-000F-3972-0
##### Abstract
Parallel list ranking is a hard problem due to its extreme degree of irregularity. Also because of its linear sequential complexity, it requires considerable effort to just reach speed-up one (break even). In this paper, we address the question of how to solve the list-ranking problem for lists of length up to $2 \cdot 10^8$ in practice: we consider implementations on the Intel Paragon, whose PUs are laid-out as a grid. It turns out that pointer jumping, independent-set removal and sparse ruling sets, all have practical importance for current systems. For the sparse-ruling-set algorithm the speed-up strongly increases with the number $k$ of nodes per PU, to finally reach $27$ with 100 PUs, for $k = 2 \cdot 10^6$.