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

@article{Lessley2018DPPPMRFRO, title={DPP-PMRF: Rethinking Optimization for a Probabilistic Graphical Model Using Data-Parallel Primitives}, author={Brenton Lessley and T. Perciano and Colleen Heinemann and David Camp and Hank Childs and E. Wes Bethel}, journal={2018 IEEE 8th Symposium on Large Data Analysis and Visualization (LDAV)}, year={2018}, pages={34-44} }

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).

## 6 Citations

Shared-Memory Parallel Probabilistic Graphical Modeling Optimization: Comparison of Threads, OpenMP, and Data-Parallel Primitives

- Computer ScienceISC
- 2020

This study is the first of its type to do performance analysis using hardware counters for comparing methods based on VTK-m-based data-parallel primitives with those based on more traditional OpenMP or threads-based parallelism, as there is increasing awareness of the need for platform portability in light of increasing node-level parallelism and increasing device heterogeneity.

High Performance Computing: 35th International Conference, ISC High Performance 2020, Frankfurt/Main, Germany, June 22–25, 2020, Proceedings

- Computer ScienceISC
- 2020

FASTHash is developed, a “truly” high throughput parallel hash table implementation using FPGA on-chip SRAM and provides theoretical worst case bound on the number of erroneous queries (true negative search, duplicate inserts) due to relaxed eventual consistency.

Minimizing development costs for efficient many-core visualization using MCD3

- Computer ScienceParallel Comput.
- 2021

XVis: Visualization for the Extreme-Scale Scientific Computation Ecosystem, Final Report

- Computer Science
- 2019

The XVis project provides the necessary research and infrastructure for scientific discovery in this new computational ecosystem by addressing four interlocking challenges: emerging processor technology, in situ integration, usability, and proxy analysis.

Performance Tradeoffs in Shared-memory Platform Portable Implementations of a Stencil Kernel

- Art
- 2021

This story is based on a manuscript originally written by Matthew Larsen in 2015 and then edited by Colleen Heinemann and Talita Perciano in 2016.

Performance Analysis of Traditional and Data-Parallel Primitive Implementations of Visualization and Analysis Kernels

- Computer ScienceArXiv
- 2020

This work focuses on performance analysis on modern multi-core platforms of three different visualization and analysis kernels that are implemented in different ways: one is "traditional", using combinations of C++ and VTK, and the other uses a data-parallel approach using VTK-m.

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