SCCMulti

Tomkins, Daniel; Smith, Timmie; Amato, Nancy M.; Rauchwerger, Lawrence

doi:10.1145/2555243.2555286

Cited by 4 publications

(1 citation statement)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many SCC algorithms are based on reachability searches [11,19,31,58,89,98,103]. The key idea is that 1) all vertices reachable both from and to a vertex 𝑣 are in the same SCC, and 2) two vertices are in the same SCC iff.…”

Section: Preliminariesmentioning

confidence: 99%

Parallel Strong Connectivity Based on Faster Reachability

Wang¹,

Dong²,

Gu³

et al. 2023

Preprint

View full text Add to dashboard Cite

Computing strongly connected components (SCC) is among the most fundamental problems in graph processing. As today's realworld graphs are getting larger and larger, parallel SCC is increasingly important. SCC is challenging in the parallel setting and is particularly hard on large-diameter graphs. Many existing parallel SCC implementations can be even slower than Tarjan's sequential algorithm on large-diameter graphs.To tackle this challenge, we propose an efficient parallel SCC implementation using a new parallel reachability algorithm. Our solution is based on a novel idea referred to as vertical granularity control (VGC). It breaks the synchronization barriers to increase parallelism and hide scheduling overhead. To use VGC in our SCC algorithm, we also design an efficient data structure called the parallel hash bag. It uses parallel dynamic resizing to avoid redundant work in maintaining frontiers (vertices processed in a round).We implement the parallel SCC algorithm by Blelloch et al. (J. ACM, 2020) using our new parallel reachability algorithm. We compare our implementation to the state-of-the-art systems, including GBBS, iSpan, Multi-step, and our highly optimized Tarjan's (sequential) algorithm, on 18 graphs, including social, web, 𝑘-NN, and lattice graphs. On a machine with 96 cores, our implementation is the fastest on 16 out of 18 graphs. On average (geometric means) over all graphs, our SCC is 6.0× faster than the best previous parallel code (GBBS), 12.8× faster than Tarjan's sequential algorithms, and 2.7× faster than the best existing implementation on each graph.We believe that our techniques are of independent interest. We also apply our parallel hash bag and VGC scheme to other graph problems, including connectivity and least-element lists (LE-lists). Our implementations improve the performance of the state-of-theart parallel implementations for these two problems. CCS Concepts• Theory of computation → Shared memory algorithms; Graph algorithms analysis.

show abstract

Section: Preliminariesmentioning

confidence: 99%

Parallel Strong Connectivity Based on Faster Reachability

Wang¹,

Dong²,

Gu³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

Concurrent depth-first search algorithms based on Tarjan’s Algorithm

Lowe

2015

Int J Softw Tools Technol Transfer

View full text Add to dashboard Cite

We present concurrent algorithms, based on depth-first search, for three problems relevant to model checking: given a state graph, to find its strongly connected components, which states are in loops, and which states are in "lassos". Each algorithm is a variant of Tarjan's Algorithm. Our algorithms typically exhibit a three-or four-fold speed-up over the corresponding sequential algorithms on an eight-core machine.

show abstract

Parallel Longest Increasing Subsequence and van Emde Boas Trees

Men

Shen

et al. 2023

Proceedings of the 35th ACM Symposium on Parallelism in Algorithms and Architectures

View full text Add to dashboard Cite

SCCMulti

Cited by 4 publications

References 7 publications

Parallel Strong Connectivity Based on Faster Reachability

Parallel Strong Connectivity Based on Faster Reachability

Concurrent depth-first search algorithms based on Tarjan’s Algorithm

Parallel Longest Increasing Subsequence and van Emde Boas Trees

Contact Info

Product

Resources

About