4 research outputs found
An Experimental Study of Parallel Biconnected Components Algorithms on Symmetric Multiprocessors (SMPs)
We present an experimental study of parallel biconnected components algorithms
employing several fundamental parallel primitives, e.g., prefix sum, list ranking, sorting, connectivity, spanning tree, and tree computations. Previous experimental studies
of these primitives demonstrate reasonable parallel speedups. However, when these
algorithms are used as subroutines to solve higher-level problems, there are two factors that hinder fast parallel implementations. One is parallel overhead, i.e., the large
constant factors hidden in the asymptotic bounds; the other is the discrepancy among
the data structures used in the primitives that brings non-negligible conversion cost.
We present various optimization techniques and a new parallel algorithm that significantly improve the performance of finding biconnected components of a graph
on symmetric multiprocessors (SMPs). Finding biconnected components has application in fault-tolerant network design, and is also used in graph planarity testing.
Our parallel implementation achieves speedups up to 4 using 12 processors on a Sun
E4500 for large, sparse graphs, and the source code is freely-available at our web site
http://www.ece.unm.edu/~dbader.This work was supported in part by NSF Grants CAREER ACI-00-93039, ITR ACI-00-81404, DEB-99-
10123, ITR EIA-01-21377, Biocomplexity DEB-01-20709, DBI-0420513, ITR EF/BIO 03-31654 and DBI-04-
20513; and DARPA Contract NBCH30390004
Theoretically Efficient Parallel Graph Algorithms Can Be Fast and Scalable
There has been significant recent interest in parallel graph processing due
to the need to quickly analyze the large graphs available today. Many graph
codes have been designed for distributed memory or external memory. However,
today even the largest publicly-available real-world graph (the Hyperlink Web
graph with over 3.5 billion vertices and 128 billion edges) can fit in the
memory of a single commodity multicore server. Nevertheless, most experimental
work in the literature report results on much smaller graphs, and the ones for
the Hyperlink graph use distributed or external memory. Therefore, it is
natural to ask whether we can efficiently solve a broad class of graph problems
on this graph in memory.
This paper shows that theoretically-efficient parallel graph algorithms can
scale to the largest publicly-available graphs using a single machine with a
terabyte of RAM, processing them in minutes. We give implementations of
theoretically-efficient parallel algorithms for 20 important graph problems. We
also present the optimizations and techniques that we used in our
implementations, which were crucial in enabling us to process these large
graphs quickly. We show that the running times of our implementations
outperform existing state-of-the-art implementations on the largest real-world
graphs. For many of the problems that we consider, this is the first time they
have been solved on graphs at this scale. We have made the implementations
developed in this work publicly-available as the Graph-Based Benchmark Suite
(GBBS).Comment: This is the full version of the paper appearing in the ACM Symposium
on Parallelism in Algorithms and Architectures (SPAA), 201