Ensemble Consistency Testing for CESM: A new form of Quality Assurance
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1 Ensemble Consistency Testing for CESM: A new form of Quality Assurance Dorit Hammerling Institute for Mathematics Applied to Geosciences National Center for Atmospheric Research (NCAR) Joint work with Allison Baker (Application Scalability and Performance Group/NCAR) and Daniel Milroy (Department of Computer Science/CU Boulder)... and many other contributors April 5, 2016 Hammerling et al. (NCAR) Ensemble Consistency Testing April 5, / 34
2 Outline Outline 1 Motivation 2 Ensemble Consistency Testing 3 Recent developments 4 ECT for the ocean component 5 Ultra-fast version 6 Summary Hammerling et al. (NCAR) Ensemble Consistency Testing April 5, / 34
3 Motivation Outline 1 Motivation 2 Ensemble Consistency Testing 3 Recent developments 4 ECT for the ocean component 5 Ultra-fast version 6 Summary Hammerling et al. (NCAR) Ensemble Consistency Testing April 5, / 34
4 Motivation NCAR s Community Earth System Model a virtual laboratory to study past, present and future climate states describes interactions of the atmosphere, land, river runoff, land-ice, oceans and sea-ice complex! Large code base: approx. 1.5 Millions lines of code Hammerling et al. (NCAR) Ensemble Consistency Testing April 5, / 34
5 Motivation Need for Software Quality Assurance Code ported to new environment (different institution) To ensure that changes during the CESM development life cycle do not adversely effect the results. Code modifications New machine architectures Compiler changes Exascale-computing technologies... Hammerling et al. (NCAR) Ensemble Consistency Testing April 5, / 34
6 Motivation Bit-for-Bit reproducibility CESM is deterministic and results are bit-for-bit reproducible IF exact same code is run, with same parameter settings, same initial conditions, on same hardware architecture, using the same compiler, same implementation of MPI, same random number generator,... not the case in most applications Hammerling et al. (NCAR) Ensemble Consistency Testing April 5, / 34
7 Motivation Bit-for-Bit reproducibility CESM is deterministic and results are bit-for-bit reproducible IF exact same code is run, with same parameter settings, same initial conditions, on same hardware architecture, using the same compiler, same implementation of MPI, same random number generator,... not the case in most applications Hammerling et al. (NCAR) Ensemble Consistency Testing April 5, / 34
8 Motivation How to assess difference if not bit-for-bit? X : Original accepted data X new : New data Key question: If X X new is the code/implementation still correct? Does the new data still represent the same climate? Or is it climate-changing? Hammerling et al. (NCAR) Ensemble Consistency Testing April 5, / 34
9 Motivation How to assess difference if not bit-for-bit? X : Original accepted data X new : New data Key question: If X X new is the code/implementation still correct? Does the new data still represent the same climate? Or is it climate-changing? Hammerling et al. (NCAR) Ensemble Consistency Testing April 5, / 34
10 Motivation Evaluating the difference How to assess whether the difference between X and X new is climate-changing? Main problem: There is no clear definition of climate-changing. What was done in the past: Climate scientists compare 400-year simulations. computationally intensive time-consuming subjective... Hammerling et al. (NCAR) Ensemble Consistency Testing April 5, / 34
11 Motivation Evaluating the difference How to assess whether the difference between X and X new is climate-changing? Main problem: There is no clear definition of climate-changing. What was done in the past: Climate scientists compare 400-year simulations. computationally intensive time-consuming subjective... Hammerling et al. (NCAR) Ensemble Consistency Testing April 5, / 34
12 Motivation Evaluating the difference How to assess whether the difference between X and X new is climate-changing? Main problem: There is no clear definition of climate-changing. What was done in the past: Climate scientists compare 400-year simulations. computationally intensive time-consuming subjective... Hammerling et al. (NCAR) Ensemble Consistency Testing April 5, / 34
13 Ensemble Consistency Testing Outline 1 Motivation 2 Ensemble Consistency Testing 3 Recent developments 4 ECT for the ocean component 5 Ultra-fast version 6 Summary Hammerling et al. (NCAR) Ensemble Consistency Testing April 5, / 34
14 Ensemble Consistency Testing New approach: Ensemble Consistency Testing Leverage climate system s natural variability (or rather it s chaotic nature)! Evaluate new data in the context of an ensemble of CESM runs. Use accepted machine and accepted software stack One-year CESM simulations with O(10 14 ) perturbations in initial atmospheric temperature 1-deg atmosphere model (F-case): 134 variables Creates an accepted ensemble distribution that can be used to evaluate new runs. Hammerling et al. (NCAR) Ensemble Consistency Testing April 5, / 34
15 Ensemble Consistency Testing New approach: Ensemble Consistency Testing Leverage climate system s natural variability (or rather it s chaotic nature)! Evaluate new data in the context of an ensemble of CESM runs. Use accepted machine and accepted software stack One-year CESM simulations with O(10 14 ) perturbations in initial atmospheric temperature 1-deg atmosphere model (F-case): 134 variables Creates an accepted ensemble distribution that can be used to evaluate new runs. Hammerling et al. (NCAR) Ensemble Consistency Testing April 5, / 34
16 Ensemble Consistency Testing New approach: Ensemble Consistency Testing Leverage climate system s natural variability (or rather it s chaotic nature)! Evaluate new data in the context of an ensemble of CESM runs. Use accepted machine and accepted software stack One-year CESM simulations with O(10 14 ) perturbations in initial atmospheric temperature 1-deg atmosphere model (F-case): 134 variables Creates an accepted ensemble distribution that can be used to evaluate new runs. Hammerling et al. (NCAR) Ensemble Consistency Testing April 5, / 34
17 Ensemble Consistency Testing New approach: Ensemble Consistency Testing Leverage climate system s natural variability (or rather it s chaotic nature)! Evaluate new data in the context of an ensemble of CESM runs. Use accepted machine and accepted software stack One-year CESM simulations with O(10 14 ) perturbations in initial atmospheric temperature 1-deg atmosphere model (F-case): 134 variables Creates an accepted ensemble distribution that can be used to evaluate new runs. Hammerling et al. (NCAR) Ensemble Consistency Testing April 5, / 34
18 Ensemble Consistency Testing Ensemble features Variable dependencies: Many of the variables are highly correlated (>.9) Difficult to make pass/fail choices based on number of variables because of dependencies. Principal Component Analysis (PCA) Projection into orthogonal space Linear combinations of variables ( scores ) instead of individual variables are used as the ensemble distribution and evaluated Hammerling et al. (NCAR) Ensemble Consistency Testing April 5, / 34
19 Ensemble Consistency Testing Ensemble features Variable dependencies: Many of the variables are highly correlated (>.9) Difficult to make pass/fail choices based on number of variables because of dependencies. Principal Component Analysis (PCA) Projection into orthogonal space Linear combinations of variables ( scores ) instead of individual variables are used as the ensemble distribution and evaluated Hammerling et al. (NCAR) Ensemble Consistency Testing April 5, / 34
20 Ensemble Consistency Testing Ensemble features Variable dependencies: Many of the variables are highly correlated (>.9) Difficult to make pass/fail choices based on number of variables because of dependencies. Principal Component Analysis (PCA) Projection into orthogonal space Linear combinations of variables ( scores ) instead of individual variables are used as the ensemble distribution and evaluated Hammerling et al. (NCAR) Ensemble Consistency Testing April 5, / 34
21 Ensemble Consistency Testing PCA-based Ensemble Consistency Testing setup Step 1: Create ensemble Step 2: Standardize variables and determine transformation matrix ( loadings ) Step 3: Determine distribution of scores Step 4: Create new runs, apply transformation and determine new scores Step 5: Compare new scores to ensemble scores and determine pass or fail Main idea: compare scores from new runs to scores from ensemble Hammerling et al. (NCAR) Ensemble Consistency Testing April 5, / 34
22 Ensemble Consistency Testing PCA-based Ensemble Consistency Testing setup Step 1: Create ensemble Step 2: Standardize variables and determine transformation matrix ( loadings ) Step 3: Determine distribution of scores Step 4: Create new runs, apply transformation and determine new scores Step 5: Compare new scores to ensemble scores and determine pass or fail Main idea: compare scores from new runs to scores from ensemble Hammerling et al. (NCAR) Ensemble Consistency Testing April 5, / 34
23 Ensemble Consistency Testing Manuscript and Code available Hammerling et al. (NCAR) Ensemble Consistency Testing April 5, / 34
24 Ensemble Consistency Testing Schematic of Ensemble Testing framework Hammerling et al. (NCAR) Ensemble Consistency Testing April 5, / 34
25 Ensemble Consistency Testing Highlights from the manuscript Defined testing framework to obtain a 0.5% false-positive rate (conservative!) Testing of purposefully constructed cases that should fail Testing of other machines Most passed, but some failed mira failed persistently Turned out to be an issue with FMA, but led us to scrutinize the approach and ensemble composition Hammerling et al. (NCAR) Ensemble Consistency Testing April 5, / 34
26 Ensemble Consistency Testing Highlights from the manuscript Defined testing framework to obtain a 0.5% false-positive rate (conservative!) Testing of purposefully constructed cases that should fail Testing of other machines Most passed, but some failed mira failed persistently Turned out to be an issue with FMA, but led us to scrutinize the approach and ensemble composition Hammerling et al. (NCAR) Ensemble Consistency Testing April 5, / 34
27 Ensemble Consistency Testing Highlights from the manuscript Defined testing framework to obtain a 0.5% false-positive rate (conservative!) Testing of purposefully constructed cases that should fail Testing of other machines Most passed, but some failed mira failed persistently Turned out to be an issue with FMA, but led us to scrutinize the approach and ensemble composition Hammerling et al. (NCAR) Ensemble Consistency Testing April 5, / 34
28 Ensemble Consistency Testing Highlights from the manuscript Defined testing framework to obtain a 0.5% false-positive rate (conservative!) Testing of purposefully constructed cases that should fail Testing of other machines Most passed, but some failed mira failed persistently Turned out to be an issue with FMA, but led us to scrutinize the approach and ensemble composition Hammerling et al. (NCAR) Ensemble Consistency Testing April 5, / 34
29 Ensemble Consistency Testing Highlights from the manuscript Defined testing framework to obtain a 0.5% false-positive rate (conservative!) Testing of purposefully constructed cases that should fail Testing of other machines Most passed, but some failed mira failed persistently Turned out to be an issue with FMA, but led us to scrutinize the approach and ensemble composition Hammerling et al. (NCAR) Ensemble Consistency Testing April 5, / 34
30 Recent developments Outline 1 Motivation 2 Ensemble Consistency Testing 3 Recent developments 4 ECT for the ocean component 5 Ultra-fast version 6 Summary Hammerling et al. (NCAR) Ensemble Consistency Testing April 5, / 34
31 Recent developments How well does CAM-ECT truly work? Modifications expected to be climate-changing fail, but is what *should* pass actually passing corresponding to our specified false positive rate? Properties of the test rely heavily on the accepted ensemble composition. Hammerling et al. (NCAR) Ensemble Consistency Testing April 5, / 34
32 Recent developments How well does CAM-ECT truly work? Modifications expected to be climate-changing fail, but is what *should* pass actually passing corresponding to our specified false positive rate? Properties of the test rely heavily on the accepted ensemble composition. Hammerling et al. (NCAR) Ensemble Consistency Testing April 5, / 34
33 Recent developments How well does CAM-ECT truly work? Modifications expected to be climate-changing fail, but is what *should* pass actually passing corresponding to our specified false positive rate? Properties of the test rely heavily on the accepted ensemble composition. Hammerling et al. (NCAR) Ensemble Consistency Testing April 5, / 34
34 Recent developments Revisiting the ensemble composition Is using only initial condition perturbations the right approach? How well do initial condition perturbations capture legitimate differences? How do runs from different compilers compare to initial perturbation runs? How large of an ensemble do we need to capture legitimate changes? We created very large ensembles using a combination of initial conditions and different compilers. Hammerling et al. (NCAR) Ensemble Consistency Testing April 5, / 34
35 Recent developments Revisiting the ensemble composition Is using only initial condition perturbations the right approach? How well do initial condition perturbations capture legitimate differences? How do runs from different compilers compare to initial perturbation runs? How large of an ensemble do we need to capture legitimate changes? We created very large ensembles using a combination of initial conditions and different compilers. Hammerling et al. (NCAR) Ensemble Consistency Testing April 5, / 34
36 Recent developments Revisiting the ensemble composition Is using only initial condition perturbations the right approach? How well do initial condition perturbations capture legitimate differences? How do runs from different compilers compare to initial perturbation runs? How large of an ensemble do we need to capture legitimate changes? We created very large ensembles using a combination of initial conditions and different compilers. Hammerling et al. (NCAR) Ensemble Consistency Testing April 5, / 34
37 Recent developments Revisiting the ensemble composition Is using only initial condition perturbations the right approach? How well do initial condition perturbations capture legitimate differences? How do runs from different compilers compare to initial perturbation runs? How large of an ensemble do we need to capture legitimate changes? We created very large ensembles using a combination of initial conditions and different compilers. Hammerling et al. (NCAR) Ensemble Consistency Testing April 5, / 34
38 Recent developments Revisiting the ensemble composition Is using only initial condition perturbations the right approach? How well do initial condition perturbations capture legitimate differences? How do runs from different compilers compare to initial perturbation runs? How large of an ensemble do we need to capture legitimate changes? We created very large ensembles using a combination of initial conditions and different compilers. Hammerling et al. (NCAR) Ensemble Consistency Testing April 5, / 34
39 Recent developments Two-way ANOVA Hammerling et al. (NCAR) Ensemble Consistency Testing April 5, / 34
40 Recent developments Two-way ANOVA... in more detail Hammerling et al. (NCAR) Ensemble Consistency Testing April 5, / 34
41 Recent developments How large of an ensemble? Hammerling et al. (NCAR) Ensemble Consistency Testing April 5, / 34
42 Recent developments Purposeful-code-changes experiments Modification to preq_omega_ps subroutine of prim_si_mod.f90: Original code: omega_p(i,j,1)=vgrad_p(i,j,1)/p(i,j,1) omega_p(i,j,1)=omega_p(i,j,1)-0.5d0/p(i,j,1)*divdp(i,j,1) Modified code: omega_p(i,j,1)=(vgrad_p(i,j,1)-&0.5d0*divdp(i,j,1))/p(i,j,1) Hammerling et al. (NCAR) Ensemble Consistency Testing April 5, / 34
43 Recent developments Purposeful-code-changes experiments Modification to preq_omega_ps subroutine of prim_si_mod.f90: Original code: omega_p(i,j,1)=vgrad_p(i,j,1)/p(i,j,1) omega_p(i,j,1)=omega_p(i,j,1)-0.5d0/p(i,j,1)*divdp(i,j,1) Modified code: omega_p(i,j,1)=(vgrad_p(i,j,1)-&0.5d0*divdp(i,j,1))/p(i,j,1) Hammerling et al. (NCAR) Ensemble Consistency Testing April 5, / 34
44 Recent developments Purposeful code changes... indeed have purpose! Hammerling et al. (NCAR) Ensemble Consistency Testing April 5, / 34
45 ECT for the ocean component Outline 1 Motivation 2 Ensemble Consistency Testing 3 Recent developments 4 ECT for the ocean component 5 Ultra-fast version 6 Summary Hammerling et al. (NCAR) Ensemble Consistency Testing April 5, / 34
46 ECT for the ocean component POP-ECT: a version for the ocean component of CESM Hammerling et al. (NCAR) Ensemble Consistency Testing April 5, / 34
47 ECT for the ocean component Ensemble spread in the ocean model component Hammerling et al. (NCAR) Ensemble Consistency Testing April 5, / 34
48 ECT for the ocean component POP-ECT is conceptually very different Only a few variables (compared to over 100 for CAM) Variability is spatially heterogeneous, too much lost in using global mean (no sensitivity!) We evaluate each location as a function of the spatially-varying point-wise ensemble variability and look at their combined exceedances above a threshold. Hammerling et al. (NCAR) Ensemble Consistency Testing April 5, / 34
49 ECT for the ocean component POP-ECT is conceptually very different Only a few variables (compared to over 100 for CAM) Variability is spatially heterogeneous, too much lost in using global mean (no sensitivity!) We evaluate each location as a function of the spatially-varying point-wise ensemble variability and look at their combined exceedances above a threshold. Hammerling et al. (NCAR) Ensemble Consistency Testing April 5, / 34
50 ECT for the ocean component Empirical evaluation of ensemble size for POP-ECT Hammerling et al. (NCAR) Ensemble Consistency Testing April 5, / 34
51 Ultra-fast version Outline 1 Motivation 2 Ensemble Consistency Testing 3 Recent developments 4 ECT for the ocean component 5 Ultra-fast version 6 Summary Hammerling et al. (NCAR) Ensemble Consistency Testing April 5, / 34
52 Ultra-fast version How fast does the ensemble spread? Hammerling et al. (NCAR) Ensemble Consistency Testing April 5, / 34
53 Ultra-fast version Ultra-fast Ensemble Consistency Testing We only use the first 9 time steps (corresponds to about 5 hours model time) Early results very promising: we catch errors and runs that should pass do Early results also indicate that we might need a large ensemble size Runs a very cheap And we don t need to store the ensemble long term, just the (very small!) summary file Idea is to use Ultra-fast ECT in combination with yearly ECT as a first (necessary, but non-sufficient) test Might be ideal for quick check after code changes. Hammerling et al. (NCAR) Ensemble Consistency Testing April 5, / 34
54 Ultra-fast version Ultra-fast Ensemble Consistency Testing We only use the first 9 time steps (corresponds to about 5 hours model time) Early results very promising: we catch errors and runs that should pass do Early results also indicate that we might need a large ensemble size Runs a very cheap And we don t need to store the ensemble long term, just the (very small!) summary file Idea is to use Ultra-fast ECT in combination with yearly ECT as a first (necessary, but non-sufficient) test Might be ideal for quick check after code changes. Hammerling et al. (NCAR) Ensemble Consistency Testing April 5, / 34
55 Ultra-fast version Ultra-fast Ensemble Consistency Testing We only use the first 9 time steps (corresponds to about 5 hours model time) Early results very promising: we catch errors and runs that should pass do Early results also indicate that we might need a large ensemble size Runs a very cheap And we don t need to store the ensemble long term, just the (very small!) summary file Idea is to use Ultra-fast ECT in combination with yearly ECT as a first (necessary, but non-sufficient) test Might be ideal for quick check after code changes. Hammerling et al. (NCAR) Ensemble Consistency Testing April 5, / 34
56 Ultra-fast version Ultra-fast Ensemble Consistency Testing We only use the first 9 time steps (corresponds to about 5 hours model time) Early results very promising: we catch errors and runs that should pass do Early results also indicate that we might need a large ensemble size Runs a very cheap And we don t need to store the ensemble long term, just the (very small!) summary file Idea is to use Ultra-fast ECT in combination with yearly ECT as a first (necessary, but non-sufficient) test Might be ideal for quick check after code changes. Hammerling et al. (NCAR) Ensemble Consistency Testing April 5, / 34
57 Ultra-fast version Ultra-fast Ensemble Consistency Testing We only use the first 9 time steps (corresponds to about 5 hours model time) Early results very promising: we catch errors and runs that should pass do Early results also indicate that we might need a large ensemble size Runs a very cheap And we don t need to store the ensemble long term, just the (very small!) summary file Idea is to use Ultra-fast ECT in combination with yearly ECT as a first (necessary, but non-sufficient) test Might be ideal for quick check after code changes. Hammerling et al. (NCAR) Ensemble Consistency Testing April 5, / 34
58 Summary Outline 1 Motivation 2 Ensemble Consistency Testing 3 Recent developments 4 ECT for the ocean component 5 Ultra-fast version 6 Summary Hammerling et al. (NCAR) Ensemble Consistency Testing April 5, / 34
59 Summary Summary Ensemble Consistency testing is a subjective and user-friendly way for quality assurance Estimating a high dimensional covariance matrix well demands a large ensemble Ultra-fast testing very promising so far Ocean testing demands a conceptually different approach than atmosphere Future work: Fine-grained testing: what is the source of the inconsistency More thorough study of ensemble composition and size Hammerling et al. (NCAR) Ensemble Consistency Testing April 5, / 34
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