Affiliation:
1. University of Maryland
2. MIT CSAIL
3. Google Research
Abstract
We study fundamental graph problems such as
graph connectivity, minimum spanning forest
(MSF), and approximate
maximum (weight) matching
in a distributed setting. In particular, we focus on the
Adaptive Massively Parallel Computation
(AMPC) model, which is a theoretical model that captures MapReduce-like computation augmented with a distributed hash table.
We show the first AMPC algorithms for all of the studied problems that run in a constant number of rounds and use only
O
(
n
ϵ
) space per machine, where 0 < ϵ < 1. Our results improve both upon the previous results in the AMPC model, as well as the best-known results in the MPC model, which is the theoretical model underpinning many popular distributed computation frameworks, such as MapReduce, Hadoop, Beam, Pregel and Giraph.
Finally, we provide an empirical comparison of the algorithms in the MPC and AMPC models in a fault-tolerant distributed computation environment. We empirically evaluate our algorithms on a set of large real-world graphs and show that our AMPC algorithms can achieve improvements in both running time and round-complexity over optimized MPC baselines.
Subject
General Earth and Planetary Sciences,Water Science and Technology,Geography, Planning and Development
Cited by
8 articles.
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1. Adaptive Massively Parallel Connectivity in Optimal Space;Proceedings of the 35th ACM Symposium on Parallelism in Algorithms and Architectures;2023-06-17
2. Massively Parallel Tree Embeddings for High Dimensional Spaces;Proceedings of the 35th ACM Symposium on Parallelism in Algorithms and Architectures;2023-06-17
3. Engineering Massively Parallel MST Algorithms;2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS);2023-05
4. Adaptive Massively Parallel Algorithms for Cut Problems;Proceedings of the 34th ACM Symposium on Parallelism in Algorithms and Architectures;2022-07-11
5. Time-Optimal Sublinear Algorithms for Matching and Vertex Cover;2021 IEEE 62nd Annual Symposium on Foundations of Computer Science (FOCS);2022-02