Other formats:
BibTeX
LaTeX
RIS
@inproceedings{1417426,
author = {Aldegheri, Stefano and Barnat, Jiří and Bombieri, Nicola and Busato, Federico and Češka, Milan},
address = {Cham},
booktitle = {22nd International Conference on Parallel and Distributed Computing, Euro-Par 2016},
doi = {http://dx.doi.org/10.1007/978-3-319-58943-5_42},
keywords = {Directed graphs; Distributed computer systems; Problem solving},
howpublished = {tištěná verze "print"},
language = {eng},
location = {Cham},
isbn = {978-3-319-58942-8},
pages = {519-531},
publisher = {Springer Verlag},
title = {Parametric multi-step scheme for GPU-accelerated graph decomposition into strongly connected components},
year = {2017}
}
TY - JOUR
ID - 1417426
AU - Aldegheri, Stefano - Barnat, Jiří - Bombieri, Nicola - Busato, Federico - Češka, Milan
PY - 2017
TI - Parametric multi-step scheme for GPU-accelerated graph decomposition into strongly connected components
PB - Springer Verlag
CY - Cham
SN - 9783319589428
KW - Directed graphs
KW - Distributed computer systems
KW - Problem solving
N2 - The problem of decomposing a directed graph into strongly connected components (SCCs) is a fundamental graph problem that is inherently present in many scientific and commercial applications. Clearly, there is a strong need for good high-performance, e.g., GPU-accelerated, algorithms to solve it. Unfortunately, among existing GPU-enabled algorithms to solve the problem, there is none that can be considered the best on every graph, disregarding the graph characteristics. Indeed, the choice of the right and most appropriate algorithm to be used is often left to inexperienced users. In this paper, we introduce a novel parametric multi-step scheme to evaluate existing GPU-accelerated algorithms for SCC decomposition in order to alleviate the burden of the choice and to help the user to identify which combination of existing techniques for SCC decomposition would fit an expected use case the most. We support our scheme with an extensive experimental evaluation that dissects correlations between the internal structure of GPU-based algorithms and their performance on various classes of graphs. The measurements confirm that there is no algorithm that would beat all other algorithms in the decomposition on all of the classes of graphs. Our contribution thus represents an important step towards an ultimate solution of automatically adjusted scheme for the GPU-accelerated SCC decomposition.
ER -
ALDEGHERI, Stefano, Jiří BARNAT, Nicola BOMBIERI, Federico BUSATO and Milan ČEŠKA. Parametric multi-step scheme for GPU-accelerated graph decomposition into strongly connected components. In \textit{22nd International Conference on Parallel and Distributed Computing, Euro-Par 2016}. Cham: Springer Verlag, 2017, p.~519-531. ISBN~978-3-319-58942-8. Available from: https://dx.doi.org/10.1007/978-3-319-58943-5\_{}42.
|
