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

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).

Authors:
 [1];  [2];  [2];  [2];  [1];  [2]
  1. Univ. of Oregon, Eugene, OR (United States)
  2. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
OSTI Identifier:
1471048
DOE Contract Number:  
AC02-05CH11231
Resource Type:
Conference
Resource Relation:
Conference: 8. IEEE Symposium on Large Data Analysis and Visualization (LDAV 2018), Berlin (Germany), 21 Oct 2018
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING

Citation Formats

Lessley, Brenton, Perciano, Talia, Heinemann, Colleen, Camp, David, Childs, Hank, and Bethel, E. Wes. DPP-PMRF: Rethinking Optimization for a Probabilistic Graphical Model Using Data-Parallel Primitives. United States: N. p., 2018. Web.
Lessley, Brenton, Perciano, Talia, Heinemann, Colleen, Camp, David, Childs, Hank, & Bethel, E. Wes. DPP-PMRF: Rethinking Optimization for a Probabilistic Graphical Model Using Data-Parallel Primitives. United States.
Lessley, Brenton, Perciano, Talia, Heinemann, Colleen, Camp, David, Childs, Hank, and Bethel, E. Wes. Sun . "DPP-PMRF: Rethinking Optimization for a Probabilistic Graphical Model Using Data-Parallel Primitives". United States. https://www.osti.gov/servlets/purl/1471048.
@article{osti_1471048,
title = {DPP-PMRF: Rethinking Optimization for a Probabilistic Graphical Model Using Data-Parallel Primitives},
author = {Lessley, Brenton and Perciano, Talia and Heinemann, Colleen and Camp, David and Childs, Hank and Bethel, E. Wes},
abstractNote = {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).},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {10}
}

Conference:
Other availability
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