Massively Parallel Algorithms for Distance Approximation and Spanners

Amartya Shankha Biswas; Michal Dory; Mohsen Ghaffari; Slobodan Mitrović; Yasamin Nazari

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arXiv · 2003.01254

Massively Parallel Algorithms for Distance Approximation and Spanners

Amartya Shankha Biswas·Michal Dory·Mohsen Ghaffari·Slobodan Mitrović·Yasamin Nazari

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Abstract

Over the past decade, there has been increasing interest in distributed/parallel algorithms for processing large-scale graphs. By now, we have quite fast algorithms -- usually sublogarithmic-time and often $poly(\log\log n)$-time, or even faster -- for a number of fundamental graph problems in the massively parallel computation (MPC) model. This model is a widely-adopted theoretical abstraction of MapReduce style settings, where a number of machines communicate in an all-to-all manner to process large-scale data. Contributing to this line of work on MPC graph algorithms, we present $poly(\log k) \in poly(\log\log n)$ round MPC algorithms for computing $O(k^{1+{o(1)}})$-spanners in the strongly sublinear regime of local memory. To the best of our knowledge, these are the first sublogarithmic-time MPC algorithms for spanner construction. As primary applications of our spanners, we get two important implications, as follows: -For the MPC setting, we get an $O(\log^2\log n)$-round algorithm for $O(\log^{1+o(1)} n)$ approximation of all pairs shortest paths (APSP) in the near-linear regime of local memory. To the best of our knowledge, this is the first sublogarithmic-time MPC algorithm for distance approximations. -Our result above also extends to the Congested Clique model of distributed computing, with the same round complexity and approximation guarantee. This gives the first sub-logarithmic algorithm for approximating APSP in weighted graphs in the Congested Clique model.

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Amartya Shankha Biswas, Michal Dory, Mohsen Ghaffari, Slobodan Mitrović, Yasamin Nazari. 2020-03-02. Massively Parallel Algorithms for Distance Approximation and Spanners. https://arxiv.org/abs/2003.01254

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