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Title: Trigger Detection for Adaptive Scientific Workflows Using Percentile Sampling

Journal Article · · SIAM Journal on Scientific Computing (Online)
DOI:https://doi.org/10.1137/15M1027942· OSTI ID:1512890
 [1];  [1];  [1];  [2];  [1];  [3]
  1. Sandia National Lab. (SNL-CA), Livermore, CA (United States)
  2. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  3. Univ. of California, Santa Cruz, CA (United States)
+ Show Author Affiliations

The increasing complexity of both scientific simulations and high-performance computing system architectures are driving the need for adaptive workflows, in which the composition and execution of computational and data manipulation steps dynamically depend on the evolutionary state of the simulation itself. Consider, for example, the frequency of data storage. Critical phases of the simulation should be captured with high frequency and with high fidelity for postanalysis; however, we cannot afford to retain the same frequency for the full simulation due to the high cost of data movement. We can instead look for triggers, indicators that the simulation will be entering a critical phase, and adapt the workflow accordingly. In this paper, we present a methodology for detecting triggers and demonstrate its use in the context of direct numerical simulations of turbulent combustion using S3D. We show that chemical explosive mode analysis (CEMA) can be used to devise a noise-tolerant indicator for rapid increase in heat release. However, exhaustive computation of CEMA values dominates the total simulation, and thus is prohibitively expensive. To overcome this computational bottleneck, we propose a quantile sampling approach. Our sampling-based algorithm comes with provable error/confidence bounds, as a function of the number of samples. Most importantly, the number of samples is independent of the problem size, and thus our proposed sampling algorithm offers perfect scalability. Our experiments on homogeneous charge compression ignition and reactivity controlled compression ignition simulations show that the proposed method can detect rapid increases in heat release, and its computational overhead is negligible. Our results will be used to make dynamic workflow decisions regarding data storage and mesh resolution in future combustion simulations.

Research Organization:
Sandia National Lab. (SNL-CA), Livermore, CA (United States)
Sponsoring Organization:
USDOE National Nuclear Security Administration (NNSA)
Grant/Contract Number:
AC04-94AL85000
OSTI ID:
1512890
Report Number(s):
SAND-2015-5112J; 664991
Journal Information:
SIAM Journal on Scientific Computing (Online), Vol. 38; ISSN 1095-7197
Publisher:
Society for Industrial and Applied Mathematics (SIAM)Copyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 9 works
Citation information provided by
Web of Science

References (13)

Direct numerical simulations of HCCI/SACI with ethanol journal July 2014
Terascale direct numerical simulations of turbulent combustion using S3D journal January 2009
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A Space-Efficient Streaming Algorithm for Estimating Transitivity and Triangle Counts Using the Birthday Paradox journal April 2015
Fuel reactivity controlled compression ignition (RCCI): a pathway to controlled high-efficiency clean combustion journal June 2011
Counting Triangles in Massive Graphs with MapReduce journal January 2014
Three-dimensional direct numerical simulation of a turbulent lifted hydrogen jet flame in heated coflow: a chemical explosive mode analysis journal May 2010
lgorithmic and Analysis Techniques in Property Testing journal January 2009
Robust Characterizations of Polynomials with Applications to Program Testing journal April 1996
Wedge sampling for computing clustering coefficients and triangle counts on large graphs † : Wedge Sampling for Computing Clustering Coefficients and Triangle Counts journal May 2014
Computational diagnostics for n-heptane flames with chemical explosive mode analysis journal October 2012
In Situ Visualization for Large-Scale Combustion Simulations journal May 2010

Cited By (1)

A flexible system for in situ triggers
  • Larsen, Matthew; Woods, Amy; Marsaglia, Nicole
  • Proceedings of the Workshop on In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization - ISAV '18 https://doi.org/10.1145/3281464.3281468
conference January 2018

Figures / Tables (16)

Fig. 1.1(p. 2)figure Fig. 1.1
Table 1.1(p. 3)table Table 1.1
Fig. 2.1(p. 7)figure Fig. 2.1
Fig. 2.2(p. 8)figure Fig. 2.2
Fig. 3.1(p. 9)figure Fig. 3.1
Fig. 3.2(p. 11)figure Fig. 3.2
Fig. 3.3(p. 12)figure Fig. 3.3
Table 4.1(p. 13)table Table 4.1
Fig. 3.4(p. 14)figure Fig. 3.4
Fig. 3.5(p. 15)figure Fig. 3.5
Fig. 4.1(p. 17)figure Fig. 4.1
Fig. 4.2(p. 18)figure Fig. 4.2
Fig. 4.3(p. 19)figure Fig. 4.3
Fig. 4.4(p. 20)figure Fig. 4.4
th percentiles of the heat release rate. For the RCCI cases, the trigger time ranges are based on the High Temperature Heat Release, i.e., the second peak in the Heat Release Rate profiles." data-ostiid="1512890">
Table 5.1(p. 20)table Table 5.1
Fig. 5.1(p. 21)figure Fig. 5.1

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Related Subjects

97 MATHEMATICS AND COMPUTING
sublinear algorithms
quantile sampling
in situ data analysis
chemical explosive mode analysis (CEMA)
S3D
adaptive workflow
judicious I/O