Skip to main content
eScholarship
Open Access Publications from the University of California
Refine Search

Lawrence Berkeley National Laboratory

LBL Publications bannerLawrence Berkeley National Laboratory

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

Published Web Location

https://doi.org/10.1109/ldav.2018.8739239
Creative Commons 'BY' version 4.0 license
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).

Many UC-authored scholarly publications are freely available on this site because of the UC's open access policies. Let us know how this access is important for you.


Top