We present a new parallel algorithm for probabilistic graphical model optimization. The algorithm relies on data-parallel primitives (DPPs), which provide portable performance over hardware architecture. We evaluate results on CPUs and GPUs for an image segmentation problem. Compared to a serial baseline, we observe runtime speedups of up to 13X (CPU) and 44X (GPU). We also compare our performance to a reference, OpenMP-based algorithm, and find speedups of up to 7X (CPU).
@article{arxiv.1809.05018,
title = {DPP-PMRF: Rethinking Optimization for a Probabilistic Graphical Model Using Data-Parallel Primitives},
author = {Brenton Lessley and Talita Perciano and Colleen Heinemann and David Camp and Hank Childs and E. Wes Bethel},
journal= {arXiv preprint arXiv:1809.05018},
year = {2018}
}