The proliferation of data in graph form calls for the development of scalable graph algorithms that exploit parallel processing environments. One such problem is the computation of a graph's minimum spanning forest (MSF). Past research has proposed several parallel algorithms for this problem, yet none of them scales to large, high-density graphs. In this paper we propose a novel, scalable, Parallel MSF Algorithm (PMA) for undirected weighted graphs. Our algorithm leverages Prim's algorithm in a parallel fashion, concurrently expanding several subsets of the computed MSF. Our effort focuses on minimizing the communication among different processors without constraining the local growth of a processor's computed subtree. In effect, we achieve a scalability that previous approaches lacked. We implement our algorithm in CUDA, running on a GPU and study its performance using real and synthetic, sparse as well as dense, structured and unstructured graph data. Our experimental study demonstrates that our algorithm outperforms the previous state-of-the-art GPU-based MSF algorithm, while being several orders of magnitude faster than sequential CPU-based algorithms.
Sadegh Nobari, Thanh-Tung Cao, Panagiotis Karras, and Stéphane Bressan, "Scalable parallel minimum spanning forest computation", Proceedings of the 17th ACM SIGPLAN symposium on Principles and Practice of Parallel Programming (PPoPP'12), New Orleans, LA, USA
@inproceedings{Nobari:2012, author = {Sadegh Nobari and Thanh{-}Tung Cao and Panagiotis Karras and St{'{e}}phane Bressan}, title = {Scalable parallel minimum spanning forest computation}, booktitle = {Proceedings of the 17th {ACM} {SIGPLAN} Symposium on Principles and Practice of Parallel Programming, {PPOPP} 2012, New Orleans, LA, USA, February 25-29, 2012}, pages = {205--214}, year = {2012}, url = {http://doi.acm.org/10.1145/2145816.2145842}, doi = {10.1145/2145816.2145842} }
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