Embedded ensemble propagation for improving performance, portability, and scalability of uncertainty quantification on emerging computational architectures
Abstract
In this study, quantifying simulation uncertainties is a critical component of rigorous predictive simulation. A key component of this is forward propagation of uncertainties in simulation input data to output quantities of interest. Typical approaches involve repeated sampling of the simulation over the uncertain input data, and can require numerous samples when accurately propagating uncertainties from large numbers of sources. Often simulation processes from sample to sample are similar and much of the data generated from each sample evaluation could be reused. We explore a new method for implementing sampling methods that simultaneously propagates groups of samples together in an embedded fashion, which we call embedded ensemble propagation. We show how this approach takes advantage of properties of modern computer architectures to improve performance by enabling reuse between samples, reducing memory bandwidth requirements, improving memory access patterns, improving opportunities for fine-grained parallelization, and reducing communication costs. We describe a software technique for implementing embedded ensemble propagation based on the use of C++ templates and describe its integration with various scientific computing libraries within Trilinos. We demonstrate improved performance, portability and scalability for the approach applied to the simulation of partial differential equations on a variety of CPU, GPU, and acceleratormore »
- Authors:
-
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Sandia National Lab. (SNL-CA), Livermore, CA (United States)
- Publication Date:
- Research Org.:
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF); Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Sandia National Lab. (SNL-CA), Livermore, CA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC); USDOE National Nuclear Security Administration (NNSA)
- OSTI Identifier:
- 1369442
- Report Number(s):
- SAND-2015-9921J
Journal ID: ISSN 1064-8275; 608080
- Grant/Contract Number:
- AC04-94AL85000
- Resource Type:
- Accepted Manuscript
- Journal Name:
- SIAM Journal on Scientific Computing
- Additional Journal Information:
- Journal Volume: 39; Journal Issue: 2; Journal ID: ISSN 1064-8275
- Publisher:
- SIAM
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING; uncertainty quantification; partial differential equations; sparse linear algebra; parallel architectures; vectorization
Citation Formats
Phipps, Eric T., D'Elia, Marta, Edwards, Harold C., Hoemmen, Mark Frederick, Hu, Jonathan J., and Rajamanickam, Sivasankaran. Embedded ensemble propagation for improving performance, portability, and scalability of uncertainty quantification on emerging computational architectures. United States: N. p., 2017.
Web. doi:10.1137/15M1044679.
Phipps, Eric T., D'Elia, Marta, Edwards, Harold C., Hoemmen, Mark Frederick, Hu, Jonathan J., & Rajamanickam, Sivasankaran. Embedded ensemble propagation for improving performance, portability, and scalability of uncertainty quantification on emerging computational architectures. United States. https://doi.org/10.1137/15M1044679
Phipps, Eric T., D'Elia, Marta, Edwards, Harold C., Hoemmen, Mark Frederick, Hu, Jonathan J., and Rajamanickam, Sivasankaran. Tue .
"Embedded ensemble propagation for improving performance, portability, and scalability of uncertainty quantification on emerging computational architectures". United States. https://doi.org/10.1137/15M1044679. https://www.osti.gov/servlets/purl/1369442.
@article{osti_1369442,
title = {Embedded ensemble propagation for improving performance, portability, and scalability of uncertainty quantification on emerging computational architectures},
author = {Phipps, Eric T. and D'Elia, Marta and Edwards, Harold C. and Hoemmen, Mark Frederick and Hu, Jonathan J. and Rajamanickam, Sivasankaran},
abstractNote = {In this study, quantifying simulation uncertainties is a critical component of rigorous predictive simulation. A key component of this is forward propagation of uncertainties in simulation input data to output quantities of interest. Typical approaches involve repeated sampling of the simulation over the uncertain input data, and can require numerous samples when accurately propagating uncertainties from large numbers of sources. Often simulation processes from sample to sample are similar and much of the data generated from each sample evaluation could be reused. We explore a new method for implementing sampling methods that simultaneously propagates groups of samples together in an embedded fashion, which we call embedded ensemble propagation. We show how this approach takes advantage of properties of modern computer architectures to improve performance by enabling reuse between samples, reducing memory bandwidth requirements, improving memory access patterns, improving opportunities for fine-grained parallelization, and reducing communication costs. We describe a software technique for implementing embedded ensemble propagation based on the use of C++ templates and describe its integration with various scientific computing libraries within Trilinos. We demonstrate improved performance, portability and scalability for the approach applied to the simulation of partial differential equations on a variety of CPU, GPU, and accelerator architectures, including up to 131,072 cores on a Cray XK7 (Titan).},
doi = {10.1137/15M1044679},
journal = {SIAM Journal on Scientific Computing},
number = 2,
volume = 39,
place = {United States},
year = {Tue Apr 18 00:00:00 EDT 2017},
month = {Tue Apr 18 00:00:00 EDT 2017}
}
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