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

Lessley, Brenton; Perciano, Talia; Heinemann, Colleen; Camp, David; Childs, Hank; Bethel, E. Wes

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

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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).

Authors:

Lessley, Brenton ^[1]; Perciano, Talia ^[2]; Heinemann, Colleen ^[2]; Camp, David ^[2]; Childs, Hank ^[1]; Bethel, E. Wes ^[2]

Univ. of Oregon, Eugene, OR (United States)
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

Publication Date:: Sun Oct 21 00:00:00 EDT 2018

Research Org.:: Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

Sponsoring Org.:: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)

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.

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                    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.

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                    Lessley, Brenton, Perciano, Talia, Heinemann, Colleen, Camp, David, Childs, Hank, and Bethel, E. Wes. 2018.  
        "DPP-PMRF: Rethinking Optimization for a Probabilistic Graphical Model Using Data-Parallel Primitives".  United States.  https://www.osti.gov/servlets/purl/1471048.

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@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          = {},

  url          = {https://www.osti.gov/biblio/1471048},
  journal      = {},
number       = ,

  volume       = ,

  place        = {United States},

  year         = {Sun Oct 21 00:00:00 EDT 2018},

  month        = {Sun Oct 21 00:00:00 EDT 2018}

}

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