Abstract
Triangle counting is a fundamental graph analytic operation that is used extensively in network science and graph mining. As the size of the graphs that needs to be analyzed continues to grow, there is a requirement in developing scalable algorithms for distributed-memory parallel systems. To this end, we present a distributed-memory triangle counting algorithm, which uses a 2D cyclic decomposition to balance the computations and reduce the communication overheads. The algorithm structures its communication and computational steps such that it reduces its memory overhead and includes key optimizations that leverage the sparsity of the graph and the way the computations are structured. Experiments on synthetic and real-world graphs show that our algorithm obtains an average relative speedup range between 3.24 to 7.22 out of 10.56 across the datasets using 169 MPI ranks over the performance achieved by 16 MPI ranks. Moreover, we obtain an average speedup of 10.2 times on comparison with previously developed distributed-memory parallel algorithms.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings of the 48th International Conference on Parallel Processing, ICPP 2019 |
| Publisher | Association for Computing Machinery |
| ISBN (Electronic) | 9781450362955 |
| DOIs | |
| State | Published - Aug 5 2019 |
| Event | 48th International Conference on Parallel Processing, ICPP 2019 - Kyoto, Japan Duration: Aug 5 2019 → Aug 8 2019 |
Publication series
| Name | ACM International Conference Proceeding Series |
|---|
Conference
| Conference | 48th International Conference on Parallel Processing, ICPP 2019 |
|---|---|
| Country | Japan |
| City | Kyoto |
| Period | 8/5/19 → 8/8/19 |
Fingerprint
Keywords
- Distributed-memory
- Graph analytics
- Triangle counting
Cite this
A 2D parallel triangle counting algorithm for distributed-memory architectures. / Tom, Ancy Sarah; Karypis, George.
Proceedings of the 48th International Conference on Parallel Processing, ICPP 2019. Association for Computing Machinery, 2019. a45 (ACM International Conference Proceeding Series).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - A 2D parallel triangle counting algorithm for distributed-memory architectures
AU - Tom, Ancy Sarah
AU - Karypis, George
PY - 2019/8/5
Y1 - 2019/8/5
N2 - Triangle counting is a fundamental graph analytic operation that is used extensively in network science and graph mining. As the size of the graphs that needs to be analyzed continues to grow, there is a requirement in developing scalable algorithms for distributed-memory parallel systems. To this end, we present a distributed-memory triangle counting algorithm, which uses a 2D cyclic decomposition to balance the computations and reduce the communication overheads. The algorithm structures its communication and computational steps such that it reduces its memory overhead and includes key optimizations that leverage the sparsity of the graph and the way the computations are structured. Experiments on synthetic and real-world graphs show that our algorithm obtains an average relative speedup range between 3.24 to 7.22 out of 10.56 across the datasets using 169 MPI ranks over the performance achieved by 16 MPI ranks. Moreover, we obtain an average speedup of 10.2 times on comparison with previously developed distributed-memory parallel algorithms.
AB - Triangle counting is a fundamental graph analytic operation that is used extensively in network science and graph mining. As the size of the graphs that needs to be analyzed continues to grow, there is a requirement in developing scalable algorithms for distributed-memory parallel systems. To this end, we present a distributed-memory triangle counting algorithm, which uses a 2D cyclic decomposition to balance the computations and reduce the communication overheads. The algorithm structures its communication and computational steps such that it reduces its memory overhead and includes key optimizations that leverage the sparsity of the graph and the way the computations are structured. Experiments on synthetic and real-world graphs show that our algorithm obtains an average relative speedup range between 3.24 to 7.22 out of 10.56 across the datasets using 169 MPI ranks over the performance achieved by 16 MPI ranks. Moreover, we obtain an average speedup of 10.2 times on comparison with previously developed distributed-memory parallel algorithms.
KW - Distributed-memory
KW - Graph analytics
KW - Triangle counting
UR - http://www.scopus.com/inward/record.url?scp=85071096572&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85071096572&partnerID=8YFLogxK
U2 - 10.1145/3337821.3337853
DO - 10.1145/3337821.3337853
M3 - Conference contribution
T3 - ACM International Conference Proceeding Series
BT - Proceedings of the 48th International Conference on Parallel Processing, ICPP 2019
PB - Association for Computing Machinery
ER -