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Entangled Monte Carlo
Seong-Hwan Jun · Liangliang Wang · Alexandre Bouchard-Côté

Tue Dec 04 11:48 AM -- 11:52 AM (PST) @ Harveys Convention Center Floor, CC

We propose a novel method for scalable parallelization of SMC algorithms, Entangled Monte Carlo simulation (EMC). EMC avoids the transmission of particles between nodes, and instead reconstructs them from the particle genealogy. In particular, we show that we can reduce the communication to the particle weights for each machine while efficiently maintaining implicit global coherence of the parallel simulation. We explain methods to efficiently maintain a genealogy of particles from which any particle can be reconstructed. We demonstrate using examples from Bayesian phylogenetic that the computational gain from parallelization using EMC significantly outweighs the cost of particle reconstruction. The timing experiments show that reconstruction of particles is indeed much more efficient as compared to transmission of particles.

Author Information

Seong-Hwan Jun (UBC)
Liangliang Wang (Simon Fraser University)
Alexandre Bouchard-Côté (UBC)

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