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arXiv · 1809.05018

DPP-PMRF: Rethinking Optimization for a Probabilistic Graphical Model Using Data-Parallel Primitives

Brenton Lessley·Talita Perciano·Colleen Heinemann·David Camp·Hank Childs·E. Wes Bethel
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Abstract

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).

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Brenton Lessley, Talita Perciano, Colleen Heinemann, David Camp, Hank Childs, E. Wes Bethel. 2018-09-13. DPP-PMRF: Rethinking Optimization for a Probabilistic Graphical Model Using Data-Parallel Primitives. https://arxiv.org/abs/1809.05018

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