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‎docs/source/projects/doc_node_failure_grayscott.rst‎

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Full code: `projects/node_failure/grayscott_example.py <https://github.com/Parallel-in-Time/pySDC/blob/pySDC_v2/projects/node_failure/grayscott_example.py>`_
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.. literalinclude:: ../../../projects/node_failure/grayscott_example.py
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Results:
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.. image:: ../../../data/GRAYSCOTT_steps_vs_iteration_hf_SPREAD.png
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:width: 19%
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.. image:: ../../../data/GRAYSCOTT_steps_vs_iteration_hf_SPREAD_PREDICT.png
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:width: 19%
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.. image:: ../../../data/GRAYSCOTT_steps_vs_iteration_hf_INTERP.png
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:width: 19%
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.. image:: ../../../data/GRAYSCOTT_steps_vs_iteration_hf_INTERP_PREDICT.png
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:width: 19%
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.. image:: ../../../data/GRAYSCOTT_steps_vs_iteration_hf_INTERP_PREDICT.png
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:width: 19%
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.. image:: ../../../data/GRAYSCOTT_Kadd_vs_NOFAULT_hf.png
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:width: 33%

‎projects/node_failure/README.rst‎

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@@ -7,7 +7,7 @@ Failure injectation as well as the different strategies are contained in ``emula
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We test our ideas for two simple toy problems and two more complex show cases.
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This project contains the code for the publication `Toward fault-tolerant parallel-in-time integration with PFASST <https://arxiv.org/abs/1510.08334>`_ of pySDC v2,
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while the original code can be found under `pySDC: Fault-tolerant PFASST <https://doi.org/10.5281/zenodo.32765>`_.
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Note that due to the long runtime, the results are not generated via Travis. Only the visualization is tested.
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Note that due to the long runtime, the results are not generated via Travis. Only the visualization (and therefore the existence of the data files) is tested.
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Propagation of a single node failure
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------------------------------------
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The Boussinesq test case
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------------------------
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A first, more complex test case is the semi-implicit 2D Boussinesq system (order-coarsening only).
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A first, more complex test case is the semi-implicit 2D Boussinesq system (order-coarsening only), see ``boussinesq_example``.
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We inject faults randomly with a rate of 3%, i.e. in 3% of all fine sweeps a node fails.
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To ensure comparability, we define these fails a priori, so that each run has to deal with the same failures.
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The ``postproc_boussinesq`` script visualizes the results.
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.. include:: doc_node_failure_boussinesq.rst
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The Gray-Scott test case
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------------------------
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Another test case is the 1D Gray-Scott reaction diffusion model, see ``grayscott_example``.
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With spatial coarsening enabled, this application plays well with PFASST and the goal of this part is to see how fault injections and recovery impact the convergence.
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We again inject faults randomly, but in a pre-defined way with a rate of 3%.
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The ``postproc_grayscott`` script visualizes the results.
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.. include:: doc_node_failure_grayscott.rst

‎projects/node_failure/grayscott_example.py‎

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from pySDC.core import CollocationClasses as collclass
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from examples.fenics_grayscott.ProblemClass import fenics_grayscott
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from pySDC.core.datatype_classes.fenics_mesh import fenics_mesh
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from examples.fenics_grayscott.TransferClass import mesh_to_mesh_fenics
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# from examples.fenics_grayscott.HookClass import fenics_output
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from pySDC.core.sweeper_classes.generic_LU import generic_LU
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# import pySDC.PFASST_blockwise as mp
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import pySDC.core.PFASST_stepwise as mp
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from pySDC.core import Log
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from pySDC.core.Stats import grep_stats, sort_stats
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import dolfin as df
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import numpy as np
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import pySDC.helpers.fault_tolerance as ft
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if __name__ == "__main__":
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# set global logger (remove this if you do not want the output at all)
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logger = Log.setup_custom_logger('root')
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num_procs = 32
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# assert num_procs == 1,'turn on predictor!'
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# This comes as read-in for the level class
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lparams = {}
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lparams['restol'] = 1E-07
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sparams = {}
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sparams['maxiter'] = 50
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sparams['fine_comm'] = True
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# This comes as read-in for the problem class
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pparams = {}
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# pparams['Du'] = 1.0
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# pparams['Dv'] = 0.01
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# pparams['A'] = 0.01
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# pparams['B'] = 0.10
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# splitting pulses until steady state
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# pparams['Du'] = 1.0
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# pparams['Dv'] = 0.01
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# pparams['A'] = 0.02
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# pparams['B'] = 0.079
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# splitting pulses until steady state
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pparams['Du'] = 1.0
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pparams['Dv'] = 0.01
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pparams['A'] = 0.09
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pparams['B'] = 0.086
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pparams['t0'] = 0.0 # ugly, but necessary to set up ProblemClass
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# pparams['c_nvars'] = [(16,16)]
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pparams['c_nvars'] = [512]
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pparams['family'] = 'CG'
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pparams['order'] = [4]
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pparams['refinements'] = [1,0]
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# This comes as read-in for the transfer operations
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tparams = {}
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tparams['finter'] = True
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# Fill description dictionary for easy hierarchy creation
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description = {}
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description['problem_class'] = fenics_grayscott
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description['problem_params'] = pparams
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description['dtype_u'] = fenics_mesh
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description['dtype_f'] = fenics_mesh
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description['collocation_class'] = collclass.CollGaussRadau_Right
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description['num_nodes'] = 3
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description['sweeper_class'] = generic_LU
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description['level_params'] = lparams
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description['transfer_class'] = mesh_to_mesh_fenics
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description['transfer_params'] = tparams
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# description['hook_class'] = fenics_output
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ft.hard_random = 0.03
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# strategies = ['NOFAULT']
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# strategies = ['SPREAD','INTERP','INTERP_PREDICT','SPREAD_PREDICT']
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strategies = ['INTERP', 'INTERP_PREDICT']
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for strategy in strategies:
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print('------------------------------------------ working on strategy ',strategy)
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ft.strategy = strategy
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# read in reference data from clean run, will provide reproducable locations for faults
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if not strategy is 'NOFAULT':
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reffile = np.load('PFASST_GRAYSCOTT_stats_hf_NOFAULT_P32.npz')
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ft.refdata = reffile['hard_stats']
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# quickly generate block of steps
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MS = mp.generate_steps(num_procs,sparams,description)
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# setup parameters "in time"
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t0 = MS[0].levels[0].prob.t0
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dt = 2.0
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Tend = 1280.0
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# get initial values on finest level
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P = MS[0].levels[0].prob
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uinit = P.u_exact(t0)
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# call main function to get things done...
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uend,stats = mp.run_pfasst(MS,u0=uinit,t0=t0,dt=dt,Tend=Tend)
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# stats magic: get all residuals
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extract_stats = grep_stats(stats,level=-1,type='residual')
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# find boundaries
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maxsteps = 0
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maxiter = 0
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minres = 0
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maxres = -99
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for k,v in extract_stats.items():
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maxsteps = max(maxsteps,getattr(k,'step'))
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maxiter = max(maxiter,getattr(k,'iter'))
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minres = min(minres,np.log10(v))
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maxres = max(maxres,np.log10(v))
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# fill residual array
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residual = np.zeros((maxiter,maxsteps+1))
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residual[:] = 0
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for k,v in extract_stats.items():
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step = getattr(k,'step')
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iter = getattr(k,'iter')
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if iter is not -1:
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residual[iter-1,step] = v
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# stats magic: get niter (probably redundant with maxiter)
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extract_stats = grep_stats(stats,iter=-1,type='niter')
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iter_count = np.zeros(maxsteps+1)
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for k,v in extract_stats.items():
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step = getattr(k,'step')
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iter_count[step] = v
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print(iter_count)
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np.savez('PFASST_GRAYSCOTT_stats_hf_'+ft.strategy+'_P'+str(num_procs)+'_cN512',residual=residual,iter_count=iter_count,hard_stats=ft.hard_stats)
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‎projects/node_failure/postproc_grayscott.py‎

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import numpy as np
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# import os
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import matplotlib.pyplot as plt
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from pylab import rcParams
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axis_font = {'fontname': 'Arial', 'size': '8', 'family': 'serif'}
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fs = 8
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ms = 8
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lw = 2
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def create_plots(cwd=''):
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"""
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Function to visualize the results of the Gray-Scott show case
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Args:
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cwd: current working directory
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"""
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ref = 'PFASST_GRAYSCOTT_stats_hf_NOFAULT_P32.npz'
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list = [('PFASST_GRAYSCOTT_stats_hf_SPREAD_P32.npz', 'SPREAD', '1-sided', 'red', 's'),
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('PFASST_GRAYSCOTT_stats_hf_INTERP_P32.npz', 'INTERP', '2-sided', 'orange', 'o'),
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('PFASST_GRAYSCOTT_stats_hf_SPREAD_PREDICT_P32.npz', 'SPREAD_PREDICT', '1-sided+corr', 'blue', '^'),
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('PFASST_GRAYSCOTT_stats_hf_INTERP_PREDICT_P32.npz', 'INTERP_PREDICT', '2-sided+corr', 'green', 'd'),
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('PFASST_GRAYSCOTT_stats_hf_NOFAULT_P32.npz', 'NOFAULT', 'no fault', 'black', 'v')]
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# list = [('PFASST_GRAYSCOTT_stats_hf_INTERP_P32_cN512.npz', 'INTERP', '2-sided', 'orange', 'o'),
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# ('PFASST_GRAYSCOTT_stats_hf_INTERP_PREDICT_P32_cN512.npz', 'INTERP_PREDICT', '2-sided+corr', 'green', 'd'),
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# ('PFASST_GRAYSCOTT_stats_hf_NOFAULT_P32.npz', 'NOFAULT', 'no fault', 'black', 'v')]
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nprocs = 32
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xtick_dist = 16
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minstep = 288
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maxstep = 384
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# minstep = 0
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# maxstep = 640
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nblocks = int(640 / nprocs)
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# maxiter = 14
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nsteps = 0
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maxiter = 0
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vmax = -99
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for file, strategy, label, color, marker in list:
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data = np.load(cwd + 'data/' + file)
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iter_count = data['iter_count'][minstep:maxstep]
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residual = data['residual'][:, minstep:maxstep]
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residual[residual <= 0] = 1E-99
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residual = np.log10(residual)
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vmin = -9
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vmax = max(vmax, int(np.amax(residual)))
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maxiter = max(maxiter, int(max(iter_count)))
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nsteps = max(nsteps, len(iter_count))
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data = np.load(cwd + 'data/' + ref)
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ref_iter_count = data['iter_count'][nprocs - 1::nprocs]
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rcParams['figure.figsize'] = 6.0, 2.5
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fig, ax = plt.subplots()
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plt.plot(range(nblocks), [0] * nblocks, 'k-', linewidth=2)
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ymin = 99
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ymax = 0
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for file, strategy, label, color, marker in list:
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if file is not ref:
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data = np.load(cwd + 'data/' + file)
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iter_count = data['iter_count'][nprocs - 1::nprocs]
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ymin = min(ymin, min(iter_count - ref_iter_count))
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ymax = max(ymax, max(iter_count - ref_iter_count))
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plt.plot(range(nblocks), iter_count - ref_iter_count, color=color, label=label, marker=marker, linestyle='',
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linewidth=lw, markersize=ms)
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plt.xlabel('block', **axis_font)
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plt.ylabel('$K_\\mathrm{add}$', **axis_font)
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plt.title('ALL')
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plt.xlim(-1, nblocks)
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plt.ylim(-1 + ymin, ymax + 1)
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plt.legend(loc=2, numpoints=1, fontsize=fs)
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plt.tick_params(axis='both', which='major', labelsize=fs)
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ax.xaxis.labelpad = -0.5
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ax.yaxis.labelpad = -1
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# plt.tight_layout()
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fname = 'data/GRAYSCOTT_Kadd_vs_NOFAULT_hf.png'
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plt.savefig(fname, bbox_inches='tight', rasterize=True)
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# os.system('pdfcrop ' + fname + ' ' + fname)
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for file, strategy, label, color, marker in list:
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data = np.load(cwd + 'data/' + file)
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residual = data['residual'][:, minstep:maxstep]
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stats = data['hard_stats']
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residual[residual <= 0] = 1E-99
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residual = np.log10(residual)
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rcParams['figure.figsize'] = 8.0, 2.0
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fig, ax = plt.subplots()
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cmap = plt.get_cmap('Reds', vmax - vmin + 1)
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pcol = plt.pcolor(residual, cmap=cmap, vmin=vmin, vmax=vmax)
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pcol.set_edgecolor('face')
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if file is not ref:
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for item in stats:
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if item[0] in range(minstep, maxstep):
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plt.text(item[0] + 0.5 - (maxstep - nsteps), item[1] - 1 + 0.5, 'x', horizontalalignment='center',
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verticalalignment='center')
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plt.axis([0, nsteps, 0, maxiter])
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ticks = np.arange(vmin, vmax + 1, 2)
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tickpos = np.linspace(ticks[0] + 0.5, ticks[-1] - 0.5, len(ticks))
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cax = plt.colorbar(pcol, ticks=tickpos, pad=0.02)
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cax.set_ticklabels(ticks)
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cax.ax.tick_params(labelsize=fs)
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cax.set_label('log10(residual)', **axis_font)
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plt.tick_params(axis='both', which='major', labelsize=fs)
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ax.xaxis.labelpad = -0.5
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ax.yaxis.labelpad = -0.5
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ax.set_xlabel('step', **axis_font)
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ax.set_ylabel('iteration', **axis_font)
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ax.set_yticks(np.arange(1, maxiter, 2) + 0.5, minor=False)
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ax.set_xticks(np.arange(0, nsteps, xtick_dist) + 0.5, minor=False)
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ax.set_yticklabels(np.arange(1, maxiter, 2) + 1, minor=False)
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ax.set_xticklabels(np.arange(minstep, maxstep, xtick_dist), minor=False)
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plt.title(strategy)
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# plt.tight_layout()
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fname = 'data/GRAYSCOTT_steps_vs_iteration_hf_' + strategy + '.png'
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plt.savefig(fname, bbox_inches='tight', rasterize=True)
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# os.system('pdfcrop ' + fname + ' ' + fname)
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if __name__ == "__main__":
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create_plots()

‎tests/test_projects/test_node_failure/test_grayscott_vis.py‎

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from projects.node_failure.postproc_grayscott import create_plots
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def test_create_plots():
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create_plots(cwd='projects/node_failure/')

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