Coverage for pySDC/projects/Resilience/work_precision.py: 0%
358 statements
« prev ^ index » next coverage.py v7.6.7, created at 2024-11-16 14:51 +0000
1from mpi4py import MPI
2import numpy as np
3import matplotlib.pyplot as plt
4import pickle
5import logging
6from time import perf_counter
7import copy
9from pySDC.projects.Resilience.strategies import merge_descriptions
10from pySDC.projects.Resilience.Lorenz import run_Lorenz
11from pySDC.projects.Resilience.vdp import run_vdp
12from pySDC.projects.Resilience.Schroedinger import run_Schroedinger
13from pySDC.projects.Resilience.quench import run_quench
14from pySDC.projects.Resilience.AC import run_AC
15from pySDC.projects.Resilience.RBC import run_RBC
17from pySDC.helpers.stats_helper import get_sorted, filter_stats
18from pySDC.helpers.plot_helper import setup_mpl, figsize_by_journal
20setup_mpl(reset=True)
21LOGGER_LEVEL = 25
22LOG_TO_FILE = False
24logging.getLogger('matplotlib.texmanager').setLevel(90)
27def std_log(x):
28 return np.std(np.log(x))
31MAPPINGS = {
32 'e_global': ('e_global_post_run', max, False),
33 'e_global_rel': ('e_global_rel_post_run', max, False),
34 't': ('timing_run', max, False),
35 # 'e_local_max': ('e_local_post_step', max, False),
36 'k_SDC': ('k', sum, None),
37 'k_SDC_no_restart': ('k', sum, False),
38 'k_Newton': ('work_newton', sum, None),
39 'k_linear': ('work_linear', sum, None),
40 'k_Newton_no_restart': ('work_newton', sum, False),
41 'k_rhs': ('work_rhs', sum, None),
42 'k_factorizations': ('work_factorizations', sum, None),
43 'num_steps': ('dt', len, None),
44 'restart': ('restart', sum, None),
45 'dt_mean': ('dt', np.mean, False),
46 'dt_max': ('dt', max, False),
47 'dt_min': ('dt', min, False),
48 'dt_sigma': ('dt', std_log, False),
49 'e_embedded_max': ('error_embedded_estimate', max, False),
50 'u0_increment_max': ('u0_increment', max, None),
51 'u0_increment_mean': ('u0_increment', np.mean, None),
52 'u0_increment_max_no_restart': ('u0_increment', max, False),
53 'u0_increment_mean_no_restart': ('u0_increment', np.mean, False),
54}
56logger = logging.getLogger('WorkPrecision')
57logger.setLevel(LOGGER_LEVEL)
60def get_forbidden_combinations(problem, strategy, **kwargs):
61 """
62 Check if the combination of strategy and problem is forbidden
64 Args:
65 problem (function): A problem to run
66 strategy (Strategy): SDC strategy
67 """
68 if strategy.name == 'ERK':
69 if problem.__name__ in ['run_quench', 'run_Schroedinger', 'run_AC']:
70 return True
72 return False
75Tends = {
76 'run_RBC': 16,
77}
78t0s = {
79 'run_RBC': 0.2,
80}
83def single_run(
84 problem,
85 strategy,
86 data,
87 custom_description,
88 num_procs=1,
89 comm_world=None,
90 problem_args=None,
91 hooks=None,
92 Tend=None,
93 num_procs_sweeper=1,
94):
95 """
96 Make a single run of a particular problem with a certain strategy.
98 Args:
99 problem (function): A problem to run
100 strategy (Strategy): SDC strategy
101 data (dict): Put the results in here
102 custom_description (dict): Overwrite presets
103 num_procs (int): Number of processes for the time communicator
104 comm_world (mpi4py.MPI.Intracomm): Communicator that is available for the entire script
105 hooks (list): List of additional hooks
106 num_procs_sweeper (int): Number of processes for the sweeper
108 Returns:
109 dict: Stats generated by the run
110 """
111 from pySDC.implementations.hooks.log_errors import LogGlobalErrorPostRun
112 from pySDC.implementations.hooks.log_work import LogWork
113 from pySDC.projects.Resilience.hook import LogData
115 hooks = hooks if hooks else []
117 t_last = perf_counter()
119 num_procs_tot = num_procs * num_procs_sweeper
120 comm = comm_world.Split(comm_world.rank < num_procs_tot)
121 if comm_world.rank >= num_procs_tot:
122 comm.Free()
123 return None
125 # make communicators for time and sweepers
126 comm_time = comm.Split(comm.rank // num_procs)
127 comm_sweep = comm.Split(comm_time.rank)
129 if comm_time.size < num_procs:
130 raise Exception(f'Need at least {num_procs*num_procs_sweeper} processes, got only {comm.size}')
132 strategy_description = strategy.get_custom_description(problem, num_procs)
133 description = merge_descriptions(strategy_description, custom_description)
134 if comm_sweep.size > 1:
135 description['sweeper_params']['comm'] = comm_sweep
137 controller_params = {
138 'logger_level': LOGGER_LEVEL,
139 'log_to_file': LOG_TO_FILE,
140 'fname': 'out.txt',
141 **strategy.get_controller_params(),
142 }
143 problem_args = {} if problem_args is None else problem_args
145 Tend = Tends.get(problem.__name__, None) if Tend is None else Tend
146 t0 = t0s.get(problem.__name__, None)
148 stats, controller, crash = problem(
149 custom_description=description,
150 Tend=strategy.get_Tend(problem, num_procs) if Tend is None else Tend,
151 hook_class=[LogData, LogWork, LogGlobalErrorPostRun] + hooks,
152 custom_controller_params=controller_params,
153 use_MPI=True,
154 t0=t0,
155 comm=comm_time,
156 **problem_args,
157 )
159 t_now = perf_counter()
160 logger.debug(f'Finished run in {t_now - t_last:.2e} s')
161 t_last = perf_counter()
163 # record all the metrics
164 stats_all = filter_stats(stats, comm=comm_sweep)
165 comm_sweep.Free()
167 for key, mapping in MAPPINGS.items():
168 if crash:
169 data[key] += [np.nan]
170 continue
171 me = get_sorted(stats_all, comm=comm_time, type=mapping[0], recomputed=mapping[2])
172 if len(me) == 0:
173 data[key] += [np.nan]
174 else:
175 data[key] += [mapping[1]([you[1] for you in me])]
177 t_now = perf_counter()
178 logger.debug(f'Recorded all data after {t_now - t_last:.2e} s')
179 t_last = perf_counter()
181 comm_time.Free()
182 comm.Free()
183 return stats
186def get_parameter(dictionary, where):
187 """
188 Get a parameter at a certain position in a dictionary of dictionaries.
190 Args:
191 dictionary (dict): The dictionary
192 where (list): The list of keys leading to the value you want
194 Returns:
195 The value of the dictionary
196 """
197 if len(where) == 1:
198 return dictionary[where[0]]
199 else:
200 return get_parameter(dictionary[where[0]], where[1:])
203def set_parameter(dictionary, where, parameter):
204 """
205 Set a parameter at a certain position in a dictionary of dictionaries
207 Args:
208 dictionary (dict): The dictionary
209 where (list): The list of keys leading to the value you want to set
210 parameter: Whatever you want to set the parameter to
212 Returns:
213 None
214 """
215 if len(where) == 1:
216 dictionary[where[0]] = parameter
217 else:
218 set_parameter(dictionary[where[0]], where[1:], parameter)
221def get_path(problem, strategy, num_procs, handle='', base_path='data/work_precision', num_procs_sweeper=1, mode=''):
222 """
223 Get the path to a certain data.
225 Args:
226 problem (function): A problem to run
227 strategy (Strategy): SDC strategy
228 num_procs (int): Number of processes for the time communicator
229 handle (str): The name of the configuration
230 base_path (str): Some path where all the files are stored
231 num_procs_sweeper (int): Number of processes for the sweeper
232 mode (str): The mode this was generated for
234 Returns:
235 str: The path to the data you are looking for
236 """
237 return f'{base_path}/{problem.__name__}-{strategy.__class__.__name__}-{handle}{"-wp" if handle else "wp"}-{num_procs}-{num_procs_sweeper}procs-{mode}.pickle'
240def record_work_precision(
241 problem,
242 strategy,
243 num_procs=1,
244 custom_description=None,
245 handle='',
246 runs=1,
247 comm_world=None,
248 problem_args=None,
249 param_range=None,
250 Tend=None,
251 hooks=None,
252 num_procs_sweeper=1,
253 mode='',
254):
255 """
256 Run problem with strategy and record the cost parameters.
258 Args:
259 problem (function): A problem to run
260 strategy (Strategy): SDC strategy
261 num_procs (int): Number of processes for the time communicator
262 custom_description (dict): Overwrite presets
263 handle (str): The name of the configuration
264 runs (int): Number of runs you want to do
265 comm_world (mpi4py.MPI.Intracomm): Communicator that is available for the entire script
266 num_procs_sweeper (int): Number of processes for the sweeper
268 Returns:
269 None
270 """
271 if get_forbidden_combinations(problem, strategy):
272 return None
274 data = {}
276 # prepare precision parameters
277 param = strategy.precision_parameter
278 description = merge_descriptions(
279 strategy.get_custom_description(problem, num_procs),
280 {} if custom_description is None else custom_description,
281 )
282 if param == 'e_tol':
283 power = 10.0
284 set_parameter(description, strategy.precision_parameter_loc[:-1] + ['dt_min'], 0)
285 exponents = [-3, -2, -1, 0, 1, 2, 3][::-1]
286 if problem.__name__ == 'run_vdp':
287 if type(strategy).__name__ in ["AdaptivityPolynomialError"]:
288 exponents = [0, 1, 2, 3, 5][::-1]
289 else:
290 exponents = [-3, -2, -1, 0, 0.2, 0.8, 1][::-1]
291 if problem.__name__ == 'run_RBC':
292 exponents = [1, 0, -1, -2, -3, -4]
293 elif param == 'dt':
294 power = 2.0
295 exponents = [-1, 0, 1, 2, 3][::-1]
296 elif param == 'restol':
297 power = 10.0
298 exponents = [-2, -1, 0, 1, 2, 3]
299 if problem.__name__ == 'run_vdp':
300 exponents = [-4, -3, -2, -1, 0, 1]
301 else:
302 raise NotImplementedError(f"I don't know how to get default value for parameter \"{param}\"")
304 where = strategy.precision_parameter_loc
305 default = get_parameter(description, where)
306 param_range = [default * power**i for i in exponents] if param_range is None else param_range
308 if problem.__name__ == 'run_quench':
309 if param == 'restol':
310 param_range = [1e-5, 1e-6, 1e-7, 1e-8, 1e-9]
311 elif param == 'dt':
312 param_range = [1.25, 2.5, 5.0, 10.0, 20.0][::-1]
313 if problem.__name__ == 'run_RBC':
314 if param == 'dt':
315 param_range = [2e-1, 1e-1, 8e-2, 6e-2]
317 # run multiple times with different parameters
318 for i in range(len(param_range)):
319 set_parameter(description, where, param_range[i])
321 data[param_range[i]] = {key: [] for key in MAPPINGS.keys()}
322 data[param_range[i]]['param'] = [param_range[i]]
323 data[param_range[i]][param] = [param_range[i]]
325 description = merge_descriptions(
326 descA=description, descB=strategy.get_description_for_tolerance(problem=problem, param=param_range[i])
327 )
328 for _j in range(runs):
329 if comm_world.rank == 0:
330 logger.log(
331 24,
332 f'Starting: {problem.__name__}: {strategy} {handle} {num_procs}-{num_procs_sweeper} procs, {param}={param_range[i]:.2e}',
333 )
334 single_run(
335 problem,
336 strategy,
337 data[param_range[i]],
338 custom_description=description,
339 comm_world=comm_world,
340 problem_args=problem_args,
341 num_procs=num_procs,
342 hooks=hooks,
343 Tend=Tend,
344 num_procs_sweeper=num_procs_sweeper,
345 )
347 comm_world.Barrier()
349 if comm_world.rank == 0:
350 if np.isfinite(data[param_range[i]]["k_linear"][-1]):
351 k_type = "k_linear"
352 elif np.isfinite(data[param_range[i]]["k_Newton"][-1]):
353 k_type = 'k_Newton'
354 else:
355 k_type = "k_SDC"
356 logger.log(
357 25,
358 f'{problem.__name__}: {strategy} {handle} {num_procs}-{num_procs_sweeper} procs, {param}={param_range[i]:.2e}: e={data[param_range[i]]["e_global"][-1]}, t={data[param_range[i]]["t"][-1]}, {k_type}={data[param_range[i]][k_type][-1]}',
359 )
361 if comm_world.rank == 0:
362 import socket
363 import time
365 data['meta'] = {
366 'hostname': socket.gethostname(),
367 'time': time.time,
368 'runs': runs,
369 }
370 path = get_path(problem, strategy, num_procs, handle, num_procs_sweeper=num_procs_sweeper, mode=mode)
371 with open(path, 'wb') as f:
372 logger.debug(f'Dumping file \"{path}\"')
373 pickle.dump(data, f)
374 return data
377def load(**kwargs):
378 """
379 Load stored data. Arguments are passed on to `get_path`
381 Returns:
382 dict: The data
383 """
384 path = get_path(**kwargs)
385 with open(path, 'rb') as f:
386 logger.debug(f'Loading file \"{path}\"')
387 data = pickle.load(f)
388 return data
391def extract_data(data, work_key, precision_key):
392 """
393 Get the work and precision from a data object.
395 Args:
396 data (dict): Data from work-precision measurements
397 work_key (str): Name of variable on x-axis
398 precision_key (str): Name of variable on y-axis
400 Returns:
401 numpy array: Work
402 numpy array: Precision
403 """
404 keys = [key for key in data.keys() if key not in ['meta']]
405 work = [np.nanmean(data[key][work_key]) for key in keys]
406 precision = [np.nanmean(data[key][precision_key]) for key in keys]
407 return np.array(work), np.array(precision)
410def get_order(work_key='e_global', precision_key='param', strategy=None, handle=None, **kwargs):
411 data = load(**kwargs, strategy=strategy, handle=handle)
412 work, precision = extract_data(data, work_key, precision_key)
414 order = [np.log(precision[i + 1] / precision[i]) / np.log(work[i + 1] / work[i]) for i in range(len(work) - 1)]
416 print(f'Order for {strategy} {handle}: {np.mean(order):.2f}')
419def plot_work_precision(
420 problem,
421 strategy,
422 num_procs,
423 ax,
424 work_key='k_SDC',
425 precision_key='e_global',
426 handle='',
427 plotting_params=None,
428 comm_world=None,
429 num_procs_sweeper=1,
430 mode='',
431): # pragma: no cover
432 """
433 Plot data from running a problem with a strategy.
435 Args:
436 problem (function): A problem to run
437 strategy (Strategy): SDC strategy
438 num_procs (int): Number of processes for the time communicator
439 ax (matplotlib.pyplot.axes): Somewhere to plot
440 work_key (str): The key in the recorded data you want on the x-axis
441 precision_key (str): The key in the recorded data you want on the y-axis
442 handle (str): The name of the configuration
443 plotting_params (dict): Will be passed when plotting
444 comm_world (mpi4py.MPI.Intracomm): Communicator that is available for the entire script
445 num_procs_sweeper (int): Number of processes for the sweeper
446 mode (str): The of the configurations you want to retrieve
448 Returns:
449 None
450 """
451 if comm_world.rank > 0 or get_forbidden_combinations(problem, strategy):
452 return None
454 data = load(
455 problem=problem,
456 strategy=strategy,
457 num_procs=num_procs,
458 handle=handle,
459 num_procs_sweeper=num_procs_sweeper,
460 mode=mode,
461 )
463 work, precision = extract_data(data, work_key, precision_key)
464 keys = [key for key in data.keys() if key not in ['meta']]
466 for key in [work_key, precision_key]:
467 rel_variance = [np.std(data[me][key]) / max([np.nanmean(data[me][key]), 1.0]) for me in keys]
468 if not all(me < 1e-1 or not np.isfinite(me) for me in rel_variance):
469 logger.warning(
470 f"Variance in \"{key}\" for {get_path(problem, strategy, num_procs, handle, num_procs_sweeper=num_procs_sweeper, mode=mode)} too large! Got {rel_variance}"
471 )
473 style = merge_descriptions(
474 {**strategy.style, 'label': f'{strategy.style["label"]}{f" {handle}" if handle else ""}'},
475 plotting_params if plotting_params else {},
476 )
478 mask = np.logical_and(np.isfinite(work), np.isfinite(precision))
479 ax.loglog(work[mask], precision[mask], **style)
481 # get_order(
482 # problem=problem,
483 # strategy=strategy,
484 # num_procs=num_procs,
485 # handle=handle,
486 # work_key=work_key,
487 # precision_key=precision_key,
488 # )
490 if 't' in [work_key, precision_key]:
491 meta = data.get('meta', {})
493 if meta.get('hostname', None) in ['thomas-work']:
494 ax.text(0.1, 0.1, "Laptop timings!", transform=ax.transAxes)
495 if meta.get('runs', None) == 1:
496 ax.text(0.1, 0.2, "No sampling!", transform=ax.transAxes)
499def plot_parallel_efficiency_diagonalSDC(
500 ax, work_key, precision_key, num_procs_sweeper, num_procs=1, **kwargs
501): # pragma: no cover
502 serial_data = load(
503 num_procs=num_procs,
504 num_procs_sweeper=1,
505 **kwargs,
506 )
507 parallel_data = load(
508 num_procs=num_procs,
509 num_procs_sweeper=num_procs_sweeper,
510 **kwargs,
511 )
512 serial_work, serial_precision = extract_data(serial_data, work_key, precision_key)
513 parallel_work, parallel_precision = extract_data(parallel_data, work_key, precision_key)
514 # assert np.allclose(serial_precision, parallel_precision)
516 serial_work = np.asarray(serial_work)
517 parallel_work = np.asarray(parallel_work)
519 # ax.loglog(serial_work, serial_precision)
520 # ax.loglog(parallel_work, parallel_precision)
522 speedup = serial_work / parallel_work
523 parallel_efficiency = np.median(speedup) / num_procs_sweeper
524 ax.plot(serial_precision, speedup)
525 ax.set_xscale('log')
526 ax.set_ylabel('speedup')
528 if 't' in [work_key, precision_key]:
529 meta = parallel_data.get('meta', {})
531 if meta.get('hostname', None) in ['thomas-work']:
532 ax.text(0.1, 0.1, "Laptop timings!", transform=ax.transAxes)
533 if meta.get('runs', None) == 1:
534 ax.text(0.1, 0.2, "No sampling!", transform=ax.transAxes)
536 return np.median(speedup), parallel_efficiency
539def decorate_panel(ax, problem, work_key, precision_key, num_procs=1, title_only=False): # pragma: no cover
540 """
541 Decorate a plot
543 Args:
544 ax (matplotlib.pyplot.axes): Somewhere to plot
545 problem (function): A problem to run
546 work_key (str): The key in the recorded data you want on the x-axis
547 precision_key (str): The key in the recorded data you want on the y-axis
548 num_procs (int): Number of processes for the time communicator
549 title_only (bool): Put only the title on top, or do the whole shebang
551 Returns:
552 None
553 """
554 labels = {
555 'k_SDC': 'SDC iterations',
556 'k_SDC_no_restart': 'SDC iterations (restarts excluded)',
557 'k_Newton': 'Newton iterations',
558 'k_Newton_no_restart': 'Newton iterations (restarts excluded)',
559 'k_rhs': 'right hand side evaluations',
560 'k_factorizations': 'matrix factorizations',
561 't': 'wall clock time / s',
562 'e_global': 'global error',
563 'e_global_rel': 'relative global error',
564 'e_local_max': 'max. local error',
565 'restart': 'restarts',
566 'dt_max': r'$\Delta t_\mathrm{max}$',
567 'dt_min': r'$\Delta t_\mathrm{min}$',
568 'dt_sigma': r'$\sigma(\Delta t)$',
569 'dt_mean': r'$\bar{\Delta t}$',
570 'param': 'accuracy parameter',
571 'u0_increment_max': r'$\| \Delta u_0 \|_{\infty} $',
572 'u0_increment_mean': r'$\bar{\Delta u_0}$',
573 'u0_increment_max_no_restart': r'$\| \Delta u_0 \|_{\infty} $ (restarts excluded)',
574 'u0_increment_mean_no_restart': r'$\bar{\Delta u_0}$ (restarts excluded)',
575 'k_linear': 'Linear solver iterations',
576 'speedup': 'Speedup',
577 'nprocs': r'$N_\mathrm{procs}$',
578 '': '',
579 }
581 if not title_only:
582 ax.set_xlabel(labels.get(work_key, 'work'))
583 ax.set_ylabel(labels.get(precision_key, 'precision'))
584 # ax.legend(frameon=False)
586 titles = {
587 'run_vdp': 'Van der Pol',
588 'run_Lorenz': 'Lorenz attractor',
589 'run_Schroedinger': r'Schr\"odinger',
590 'run_quench': 'Quench',
591 'run_AC': 'Allen-Cahn',
592 }
593 ax.set_title(titles.get(problem.__name__, ''))
596def execute_configurations(
597 problem,
598 configurations,
599 work_key,
600 precision_key,
601 num_procs,
602 ax,
603 decorate,
604 record,
605 runs,
606 comm_world,
607 plotting,
608 Tend=None,
609 num_procs_sweeper=1,
610 mode='',
611):
612 """
613 Run for multiple configurations.
615 Args:
616 problem (function): A problem to run
617 configurations (dict): The configurations you want to run with
618 work_key (str): The key in the recorded data you want on the x-axis
619 precision_key (str): The key in the recorded data you want on the y-axis
620 num_procs (int): Number of processes for the time communicator
621 ax (matplotlib.pyplot.axes): Somewhere to plot
622 decorate (bool): Whether to decorate fully or only put the title
623 record (bool): Whether to only plot or also record the data first
624 runs (int): Number of runs you want to do
625 comm_world (mpi4py.MPI.Intracomm): Communicator that is available for the entire script
626 plotting (bool): Whether to plot something
627 num_procs_sweeper (int): Number of processes for the sweeper
628 mode (str): What you want to look at
630 Returns:
631 None
632 """
633 for _, config in configurations.items():
634 for strategy in config['strategies']:
635 shared_args = {
636 'problem': problem,
637 'strategy': strategy,
638 'handle': config.get('handle', ''),
639 'num_procs': config.get('num_procs', num_procs),
640 'num_procs_sweeper': config.get('num_procs_sweeper', num_procs_sweeper),
641 }
642 if record:
643 logger.debug('Recording work precision')
644 record_work_precision(
645 **shared_args,
646 custom_description=config.get('custom_description', {}),
647 runs=runs,
648 comm_world=comm_world,
649 problem_args=config.get('problem_args', {}),
650 param_range=config.get('param_range', None),
651 hooks=config.get('hooks', None),
652 Tend=config.get('Tend') if Tend is None else Tend,
653 mode=mode,
654 )
655 if plotting and comm_world.rank == 0:
656 logger.debug('Plotting')
657 plot_work_precision(
658 **shared_args,
659 work_key=work_key,
660 precision_key=precision_key,
661 ax=ax,
662 plotting_params=config.get('plotting_params', {}),
663 comm_world=comm_world,
664 mode=mode,
665 )
667 if comm_world.rank == 0:
668 decorate_panel(
669 ax=ax,
670 problem=problem,
671 work_key=work_key,
672 precision_key=precision_key,
673 num_procs=num_procs,
674 title_only=not decorate,
675 )
678def get_configs(mode, problem):
679 """
680 Get configurations for work-precision plots. These are dictionaries containing strategies and handles and so on.
682 Args:
683 mode (str): The of the configurations you want to retrieve
684 problem (function): A problem to run
686 Returns:
687 dict: Configurations
688 """
689 configurations = {}
690 if mode == 'regular':
691 from pySDC.projects.Resilience.strategies import AdaptivityStrategy, BaseStrategy, IterateStrategy
693 handle = 'regular'
694 configurations[0] = {
695 'handle': handle,
696 'strategies': [AdaptivityStrategy(useMPI=True), BaseStrategy(useMPI=True), IterateStrategy(useMPI=True)],
697 }
698 elif mode == 'step_size_limiting':
699 from pySDC.implementations.convergence_controller_classes.step_size_limiter import StepSizeLimiter
700 from pySDC.projects.Resilience.strategies import AdaptivityStrategy, ESDIRKStrategy
702 limits = [
703 25.0,
704 50.0,
705 ]
706 colors = ['teal', 'magenta']
707 markers = ['v', 'x']
708 markersize = 9
709 for i in range(len(limits)):
710 configurations[i] = {
711 'custom_description': {'convergence_controllers': {StepSizeLimiter: {'dt_max': limits[i]}}},
712 'handle': f'steplimiter{limits[i]:.0f}',
713 'strategies': [AdaptivityStrategy(useMPI=True)],
714 'plotting_params': {
715 'color': colors[i],
716 'marker': markers[i],
717 'label': rf'$\Delta t \leq { {limits[i]:.0f}} $',
718 # 'ls': '',
719 'markersize': markersize,
720 },
721 'num_procs': 1,
722 }
723 configurations[99] = {
724 'custom_description': {},
725 'handle': 'no limits',
726 'plotting_params': {
727 'label': 'no limiter',
728 # 'ls': '',
729 'markersize': markersize,
730 },
731 'strategies': [AdaptivityStrategy(useMPI=True)],
732 'num_procs': 1,
733 }
734 elif mode == 'dynamic_restarts':
735 """
736 Compare Block Gauss-Seidel SDC with restarting the first step in the block or the first step that exceeded the error threshold.
737 """
738 from pySDC.projects.Resilience.strategies import AdaptivityStrategy, AdaptivityRestartFirstStep
740 desc = {}
741 desc['sweeper_params'] = {'num_nodes': 3, 'QI': 'IE'}
742 desc['step_params'] = {'maxiter': 5}
744 ls = {
745 1: '-',
746 2: '--',
747 3: '-.',
748 4: ':',
749 5: ':',
750 }
752 configurations[-1] = {
753 'strategies': [AdaptivityStrategy(useMPI=True)],
754 'num_procs': 1,
755 }
757 for num_procs in [4, 2]:
758 plotting_params = {'ls': ls[num_procs], 'label': f'adaptivity {num_procs} procs'}
759 configurations[num_procs] = {
760 'strategies': [AdaptivityStrategy(useMPI=True), AdaptivityRestartFirstStep(useMPI=True)],
761 'custom_description': desc,
762 'num_procs': num_procs,
763 'plotting_params': plotting_params,
764 }
766 elif mode == 'compare_strategies':
767 """
768 Compare the different SDC strategies.
769 """
770 from pySDC.projects.Resilience.strategies import (
771 AdaptivityStrategy,
772 kAdaptivityStrategy,
773 AdaptivityPolynomialError,
774 BaseStrategy,
775 )
777 newton_inexactness = False if problem.__name__ in ['run_vdp'] else True
779 configurations[1] = {
780 'strategies': [AdaptivityPolynomialError(useMPI=True, newton_inexactness=newton_inexactness)],
781 }
782 configurations[2] = {
783 'strategies': [kAdaptivityStrategy(useMPI=True)],
784 }
785 configurations[0] = {
786 'custom_description': {
787 'step_params': {'maxiter': 5},
788 'sweeper_params': {'num_nodes': 3, 'quad_type': 'RADAU-RIGHT'},
789 },
790 'strategies': [
791 AdaptivityStrategy(useMPI=True),
792 BaseStrategy(useMPI=True),
793 ],
794 }
796 elif mode == 'RK_comp':
797 """
798 Compare parallel adaptive SDC to Runge-Kutta
799 """
800 from pySDC.projects.Resilience.strategies import (
801 AdaptivityStrategy,
802 ERKStrategy,
803 ESDIRKStrategy,
804 ARKStrategy,
805 AdaptivityPolynomialError,
806 )
808 if problem.__name__ in ['run_Schroedinger', 'run_AC', 'run_RBC']:
809 from pySDC.implementations.sweeper_classes.imex_1st_order_MPI import imex_1st_order_MPI as parallel_sweeper
810 else:
811 from pySDC.implementations.sweeper_classes.generic_implicit_MPI import (
812 generic_implicit_MPI as parallel_sweeper,
813 )
815 newton_inexactness = False if problem.__name__ in ['run_vdp', 'run_RBC'] else True
817 desc = {}
818 desc['sweeper_params'] = {'num_nodes': 3, 'QI': 'IE', 'QE': "EE"}
819 desc['step_params'] = {'maxiter': 5}
821 desc_poly = {}
822 desc_poly['sweeper_class'] = parallel_sweeper
824 ls = {
825 1: '-',
826 2: '--',
827 3: '-.',
828 4: ':',
829 5: ':',
830 }
831 RK_strategies = []
832 if problem.__name__ in ['run_Lorenz']:
833 RK_strategies.append(ERKStrategy(useMPI=True))
834 if problem.__name__ in ['run_Schroedinger', 'run_AC', 'run_RBC']:
835 RK_strategies.append(ARKStrategy(useMPI=True))
836 else:
837 RK_strategies.append(ESDIRKStrategy(useMPI=True))
839 configurations[3] = {
840 'custom_description': desc_poly,
841 'strategies': [AdaptivityPolynomialError(useMPI=True, newton_inexactness=newton_inexactness)],
842 'num_procs': 1,
843 'num_procs_sweeper': 3,
844 'plotting_params': {'label': r'$\Delta t$-$k$-adaptivity $N$=1x3'},
845 }
846 configurations[-1] = {
847 'strategies': RK_strategies,
848 'num_procs': 1,
849 }
850 configurations[2] = {
851 'strategies': [AdaptivityStrategy(useMPI=True)],
852 'custom_description': desc,
853 'num_procs': 4,
854 'plotting_params': {'label': r'$\Delta t$-adaptivity $N$=4x1'},
855 }
857 elif mode == 'parallel_efficiency':
858 """
859 Compare parallel runs of the step size adaptive SDC
860 """
861 from pySDC.projects.Resilience.strategies import AdaptivityStrategy, AdaptivityPolynomialError
863 if problem.__name__ in ['run_Schroedinger', 'run_AC']:
864 from pySDC.implementations.sweeper_classes.imex_1st_order_MPI import imex_1st_order_MPI as parallel_sweeper
865 else:
866 from pySDC.implementations.sweeper_classes.generic_implicit_MPI import (
867 generic_implicit_MPI as parallel_sweeper,
868 )
870 desc = {}
871 desc['sweeper_params'] = {'num_nodes': 3, 'QI': 'IE', 'QE': 'EE'}
872 desc['step_params'] = {'maxiter': 5}
874 ls = {
875 1: '-',
876 2: '--',
877 3: '-.',
878 4: '--',
879 5: ':',
880 12: ':',
881 }
883 newton_inexactness = False if problem.__name__ in ['run_vdp'] else True
885 for num_procs in [4, 1]:
886 plotting_params = (
887 {'ls': ls[num_procs], 'label': fr'$\Delta t$-adaptivity $N$={num_procs}x1'} if num_procs > 1 else {}
888 )
889 configurations[num_procs] = {
890 'strategies': [AdaptivityStrategy(useMPI=True)],
891 'custom_description': desc.copy(),
892 'num_procs': num_procs,
893 'plotting_params': plotting_params.copy(),
894 }
895 configurations[num_procs * 100 + 79] = {
896 'custom_description': {'sweeper_class': parallel_sweeper},
897 'strategies': [
898 AdaptivityPolynomialError(
899 useMPI=True, newton_inexactness=newton_inexactness, linear_inexactness=True
900 )
901 ],
902 'num_procs_sweeper': 3,
903 'num_procs': num_procs,
904 'plotting_params': {
905 'ls': ls.get(num_procs * 3, '-'),
906 'label': rf'$\Delta t$-$k$-adaptivity $N$={num_procs}x3',
907 },
908 }
910 configurations[num_procs * 200 + 79] = {
911 'strategies': [
912 AdaptivityPolynomialError(useMPI=True, newton_inexactness=newton_inexactness, linear_inexactness=True)
913 ],
914 'num_procs': 1,
915 }
917 elif mode == 'interpolate_between_restarts':
918 """
919 Compare adaptivity with interpolation between restarts and without
920 """
921 from pySDC.projects.Resilience.strategies import AdaptivityPolynomialError
923 i = 0
924 for interpolate_between_restarts, handle, ls in zip(
925 [True, False], ['Interpolation between restarts', 'regular'], ['--', '-']
926 ):
927 configurations[i] = {
928 'strategies': [
929 AdaptivityPolynomialError(interpolate_between_restarts=interpolate_between_restarts, useMPI=True)
930 ],
931 'plotting_params': {'ls': ls},
932 'handle': handle,
933 }
934 i += 1
935 elif mode == 'diagonal_SDC':
936 """
937 Run diagonal SDC with different number of nodes and ranks. You can use this to compute a speedup, but it's not strong scaling!
938 """
939 from pySDC.projects.Resilience.strategies import AdaptivityPolynomialError
941 if problem.__name__ in ['run_Schroedinger']:
942 from pySDC.implementations.sweeper_classes.imex_1st_order_MPI import imex_1st_order_MPI as parallel_sweeper
943 else:
944 from pySDC.implementations.sweeper_classes.generic_implicit_MPI import (
945 generic_implicit_MPI as parallel_sweeper,
946 )
948 for parallel in [False, True]:
949 desc = {'sweeper_class': parallel_sweeper} if parallel else {}
950 for num_nodes, ls in zip([3, 4, 2], ['-', '--', ':', '-.']):
951 configurations[num_nodes + (99 if parallel else 0)] = {
952 'custom_description': {**desc, 'sweeper_params': {'num_nodes': num_nodes}},
953 'strategies': [
954 AdaptivityPolynomialError(useMPI=True, newton_inexactness=True, linear_inexactness=True)
955 ],
956 'num_procs_sweeper': num_nodes if parallel else 1,
957 'num_procs': 1,
958 'handle': f'{num_nodes} nodes',
959 'plotting_params': {
960 'ls': ls,
961 'label': f'{num_nodes} procs',
962 # **{'color': 'grey' if parallel else None},
963 },
964 }
966 elif mode[:13] == 'vdp_stiffness':
967 """
968 Run van der Pol with different parameter for the nonlinear term, which controls the stiffness.
969 """
970 from pySDC.projects.Resilience.strategies import (
971 AdaptivityStrategy,
972 ERKStrategy,
973 ESDIRKStrategy,
974 AdaptivityPolynomialError,
975 )
976 from pySDC.implementations.sweeper_classes.generic_implicit_MPI import (
977 generic_implicit_MPI as parallel_sweeper,
978 )
980 Tends = {
981 1000: 2000,
982 100: 200,
983 10: 20,
984 0: 2,
985 }
986 mu = float(mode[14:])
987 Tend = Tends[mu]
989 problem_desc = {'problem_params': {'mu': mu}}
991 desc = {}
992 desc['sweeper_params'] = {'num_nodes': 3, 'QI': 'IE'}
993 desc['step_params'] = {'maxiter': 5}
994 desc['problem_params'] = problem_desc['problem_params']
996 ls = {
997 1: '-',
998 2: '--',
999 3: '-.',
1000 4: ':',
1001 5: ':',
1002 'MIN-SR-S': '-',
1003 'MIN-SR-NS': '--',
1004 'MIN-SR-FLEX': '-.',
1005 }
1007 if mu < 100:
1008 configurations[2] = {
1009 'strategies': [ERKStrategy(useMPI=True)],
1010 'num_procs': 1,
1011 'handle': mode,
1012 'plotting_params': {'label': 'CP5(4)'},
1013 'custom_description': problem_desc,
1014 'Tend': Tend,
1015 }
1016 configurations[1] = {
1017 'strategies': [AdaptivityStrategy(useMPI=True)],
1018 'custom_description': desc,
1019 'num_procs': 4,
1020 'plotting_params': {'ls': ls[1], 'label': 'SDC $N$=4x1'},
1021 'handle': mode,
1022 'Tend': Tend,
1023 }
1024 configurations[4] = {
1025 'strategies': [ESDIRKStrategy(useMPI=True)],
1026 'num_procs': 1,
1027 'handle': mode,
1028 'plotting_params': {'label': 'ESDIRK5(3)'},
1029 'custom_description': problem_desc,
1030 'Tend': Tend,
1031 }
1032 for QI, i in zip(
1033 [
1034 'MIN-SR-S',
1035 # 'MIN-SR-FLEX',
1036 ],
1037 [9991, 12123127391, 1231723109247102731092],
1038 ):
1039 configurations[i] = {
1040 'custom_description': {
1041 'sweeper_params': {'num_nodes': 3, 'QI': QI},
1042 'problem_params': desc["problem_params"],
1043 'sweeper_class': parallel_sweeper,
1044 },
1045 'strategies': [
1046 AdaptivityPolynomialError(
1047 useMPI=True, newton_inexactness=False, linear_inexactness=False, max_slope=4
1048 )
1049 ],
1050 'num_procs_sweeper': 3,
1051 'num_procs': 1,
1052 'plotting_params': {
1053 'ls': ls.get(QI, '-'),
1054 'label': rf'$\Delta t$-$k$-adaptivity $N$={1}x3',
1055 },
1056 'handle': f'{mode}-{QI}',
1057 'Tend': Tend,
1058 }
1060 elif mode == 'inexactness':
1061 """
1062 Compare inexact SDC to exact SDC
1063 """
1064 from pySDC.projects.Resilience.strategies import (
1065 AdaptivityPolynomialError,
1066 )
1068 if problem.__name__ in ['run_Schroedinger']:
1069 from pySDC.implementations.sweeper_classes.imex_1st_order_MPI import imex_1st_order_MPI as parallel_sweeper
1070 else:
1071 from pySDC.implementations.sweeper_classes.generic_implicit_MPI import (
1072 generic_implicit_MPI as parallel_sweeper,
1073 )
1075 strategies = [
1076 AdaptivityPolynomialError,
1077 ]
1079 inexactness = {
1080 'newton_inexactness': True,
1081 'linear_inexactness': True,
1082 }
1083 no_inexactness = {
1084 'newton_inexactness': False,
1085 'linear_inexactness': False,
1086 'SDC_maxiter': 99,
1087 'use_restol_rel': False,
1088 }
1090 configurations[1] = {
1091 'custom_description': {'sweeper_class': parallel_sweeper},
1092 'strategies': [me(useMPI=True, **no_inexactness) for me in strategies],
1093 'num_procs_sweeper': 3,
1094 'handle': 'exact',
1095 'plotting_params': {'ls': '--'},
1096 }
1097 configurations[0] = {
1098 'custom_description': {'sweeper_class': parallel_sweeper},
1099 'strategies': [me(useMPI=True, **inexactness) for me in strategies],
1100 'handle': 'inexact',
1101 'num_procs_sweeper': 3,
1102 }
1103 elif mode == 'compare_adaptivity':
1104 """
1105 Compare various modes of adaptivity
1106 """
1107 # TODO: configurations not final!
1108 from pySDC.projects.Resilience.strategies import (
1109 # AdaptivityCollocationTypeStrategy,
1110 # AdaptivityCollocationRefinementStrategy,
1111 AdaptivityStrategy,
1112 # AdaptivityExtrapolationWithinQStrategy,
1113 ESDIRKStrategy,
1114 ARKStrategy,
1115 AdaptivityPolynomialError,
1116 )
1118 if problem.__name__ in ['run_Schroedinger']:
1119 from pySDC.implementations.sweeper_classes.imex_1st_order_MPI import imex_1st_order_MPI as parallel_sweeper
1120 else:
1121 from pySDC.implementations.sweeper_classes.generic_implicit_MPI import (
1122 generic_implicit_MPI as parallel_sweeper,
1123 )
1125 inexactness_params = {
1126 # 'double_adaptivity': True,
1127 'newton_inexactness': True,
1128 'linear_inexactness': True,
1129 }
1131 strategies = [
1132 AdaptivityPolynomialError,
1133 # AdaptivityCollocationTypeStrategy,
1134 # AdaptivityExtrapolationWithinQStrategy,
1135 ]
1137 # restol = None
1138 # for strategy in strategies:
1139 # strategy.restol = restol
1141 configurations[1] = {
1142 'custom_description': {'sweeper_class': parallel_sweeper},
1143 'strategies': [me(useMPI=True, **inexactness_params) for me in strategies],
1144 'handle': 'parallel',
1145 'num_procs_sweeper': 3,
1146 'plotting_params': {'ls': '-', 'label': '3 procs'},
1147 }
1148 configurations[2] = {
1149 'strategies': [me(useMPI=True, **inexactness_params) for me in strategies],
1150 'plotting_params': {'ls': '--'},
1151 }
1152 configurations[4] = {
1153 'custom_description': {'step_params': {'maxiter': 5}},
1154 'strategies': [AdaptivityStrategy(useMPI=True)],
1155 }
1157 desc_RK = {}
1158 configurations[-1] = {
1159 'strategies': [
1160 ARKStrategy(useMPI=True) if problem.__name__ == 'run_Schroedinger' else ESDIRKStrategy(useMPI=True),
1161 ],
1162 'num_procs': 1,
1163 'custom_description': desc_RK,
1164 }
1166 elif mode == 'preconditioners':
1167 """
1168 Compare different preconditioners
1169 """
1170 from pySDC.projects.Resilience.strategies import (
1171 AdaptivityStrategy,
1172 IterateStrategy,
1173 BaseStrategy,
1174 ESDIRKStrategy,
1175 ERKStrategy,
1176 AdaptivityPolynomialError,
1177 )
1179 inexacness = True
1180 strategies = [
1181 AdaptivityPolynomialError(
1182 useMPI=True, SDC_maxiter=29, newton_inexactness=inexacness, linear_inexactness=inexacness
1183 ),
1184 BaseStrategy(useMPI=True),
1185 ]
1187 desc = {}
1188 desc['sweeper_params'] = {
1189 'num_nodes': 3,
1190 }
1191 # desc['step_params'] = {'maxiter': 5}
1193 precons = ['IE', 'LU']
1194 ls = ['-.', '--', '-', ':']
1195 for i in range(len(precons) + 1):
1196 if i < len(precons):
1197 desc['sweeper_params']['QI'] = precons[i]
1198 handle = precons[i]
1199 else:
1200 handle = None
1201 configurations[i] = {
1202 'strategies': strategies,
1203 'custom_description': copy.deepcopy(desc),
1204 'handle': handle,
1205 'plotting_params': {'ls': ls[i]},
1206 }
1207 elif mode == 'RK_comp_high_order':
1208 """
1209 Compare higher order SDC than we can get with RKM to RKM
1210 """
1211 from pySDC.projects.Resilience.strategies import (
1212 AdaptivityStrategy,
1213 ERKStrategy,
1214 ESDIRKStrategy,
1215 ARKStrategy,
1216 AdaptivityPolynomialError,
1217 )
1219 if problem.__name__ in ['run_Schroedinger']:
1220 from pySDC.implementations.sweeper_classes.imex_1st_order_MPI import imex_1st_order_MPI as parallel_sweeper
1221 else:
1222 from pySDC.implementations.sweeper_classes.generic_implicit_MPI import (
1223 generic_implicit_MPI as parallel_sweeper,
1224 )
1226 desc = {}
1227 desc['sweeper_params'] = {'num_nodes': 4, 'QI': 'IE', 'QE': "EE"}
1228 desc['step_params'] = {'maxiter': 7}
1230 desc_poly = {}
1231 desc_poly['sweeper_class'] = parallel_sweeper
1232 desc_poly['sweeper_params'] = {'num_nodes': 4}
1234 ls = {
1235 1: '-',
1236 2: '--',
1237 3: '-.',
1238 4: ':',
1239 5: ':',
1240 }
1242 desc_RK = {}
1243 if problem.__name__ in ['run_Schroedinger']:
1244 desc_RK['problem_params'] = {'imex': True}
1246 configurations[3] = {
1247 'custom_description': desc_poly,
1248 'strategies': [AdaptivityPolynomialError(useMPI=True)],
1249 'num_procs': 1,
1250 'num_procs_sweeper': 4,
1251 }
1252 configurations[-1] = {
1253 'strategies': [
1254 ERKStrategy(useMPI=True),
1255 ARKStrategy(useMPI=True) if problem.__name__ in ['run_Schroedinger'] else ESDIRKStrategy(useMPI=True),
1256 ],
1257 'num_procs': 1,
1258 'custom_description': desc_RK,
1259 }
1261 configurations[2] = {
1262 'strategies': [AdaptivityStrategy(useMPI=True)],
1263 'custom_description': desc,
1264 'num_procs': 4,
1265 }
1266 elif mode == 'avoid_restarts':
1267 """
1268 Test how well avoiding restarts works.
1269 """
1270 from pySDC.projects.Resilience.strategies import (
1271 AdaptivityStrategy,
1272 AdaptivityAvoidRestartsStrategy,
1273 AdaptivityPolynomialStrategy,
1274 )
1276 desc = {'sweeper_params': {'QI': 'IE'}, 'step_params': {'maxiter': 3}}
1277 param_range = [1e-3, 1e-5]
1278 configurations[0] = {
1279 'strategies': [AdaptivityPolynomialStrategy(useMPI=True)],
1280 'plotting_params': {'ls': '--'},
1281 'custom_description': desc,
1282 'param_range': param_range,
1283 }
1284 configurations[1] = {
1285 'strategies': [AdaptivityAvoidRestartsStrategy(useMPI=True)],
1286 'plotting_params': {'ls': '-.'},
1287 'custom_description': desc,
1288 'param_range': param_range,
1289 }
1290 configurations[2] = {
1291 'strategies': [AdaptivityStrategy(useMPI=True)],
1292 'custom_description': desc,
1293 'param_range': param_range,
1294 }
1295 else:
1296 raise NotImplementedError(f'Don\'t know the mode "{mode}"!')
1298 return configurations
1301def get_fig(x=1, y=1, **kwargs): # pragma: no cover
1302 """
1303 Get a figure to plot in.
1305 Args:
1306 x (int): How many panels in horizontal direction you want
1307 y (int): How many panels in vertical direction you want
1309 Returns:
1310 matplotlib.pyplot.Figure
1311 """
1312 width = 1.0
1313 ratio = 1.0 if y == 2 else 0.5
1314 keyword_arguments = {
1315 'figsize': figsize_by_journal('Springer_Numerical_Algorithms', width, ratio),
1316 'layout': 'constrained',
1317 **kwargs,
1318 }
1319 return plt.subplots(y, x, **keyword_arguments)
1322def save_fig(
1323 fig, name, work_key, precision_key, legend=True, format='pdf', base_path='data', squares=True, ncols=None, **kwargs
1324): # pragma: no cover
1325 """
1326 Save a figure with a legend on the bottom.
1328 Args:
1329 fig (matplotlib.pyplot.Figure): Figure you want to save
1330 name (str): Name of the plot to put in the path
1331 work_key (str): The key in the recorded data you want on the x-axis
1332 precision_key (str): The key in the recorded data you want on the y-axis
1333 legend (bool): Put a legend or not
1334 format (str): Format to store the figure with
1335 base_path (str): Some path where all the files are stored
1336 squares (bool): Adjust aspect ratio to squares if true
1338 Returns:
1339 None
1340 """
1341 handles = []
1342 labels = []
1343 for ax in fig.get_axes():
1344 h, l = ax.get_legend_handles_labels()
1345 handles += [h[i] for i in range(len(h)) if l[i] not in labels]
1346 labels += [me for me in l if me not in labels]
1347 if squares:
1348 ax.set_box_aspect(1)
1349 # order = np.argsort([me[0] for me in labels])
1350 order = np.arange(len(labels))
1351 fig.legend(
1352 [handles[i] for i in order],
1353 [labels[i] for i in order],
1354 loc='outside lower center',
1355 ncols=ncols if ncols else 3 if len(handles) % 3 == 0 else 4,
1356 frameon=False,
1357 fancybox=True,
1358 handlelength=2.2,
1359 )
1361 path = f'{base_path}/wp-{name}-{work_key}-{precision_key}.{format}'
1362 fig.savefig(path, bbox_inches='tight', **kwargs)
1363 print(f'Stored figure \"{path}\"')
1366def all_problems(mode='compare_strategies', plotting=True, base_path='data', **kwargs): # pragma: no cover
1367 """
1368 Make a plot comparing various strategies for all problems.
1370 Args:
1371 work_key (str): The key in the recorded data you want on the x-axis
1372 precision_key (str): The key in the recorded data you want on the y-axis
1374 Returns:
1375 None
1376 """
1378 fig, axs = get_fig(2, 2)
1380 shared_params = {
1381 'work_key': 'k_SDC',
1382 'precision_key': 'e_global',
1383 'num_procs': 1,
1384 'runs': 1,
1385 'comm_world': MPI.COMM_WORLD,
1386 'record': False,
1387 'plotting': plotting,
1388 **kwargs,
1389 }
1391 problems = [run_vdp, run_quench, run_Schroedinger, run_AC]
1393 logger.log(26, f"Doing for all problems {mode}")
1394 for i in range(len(problems)):
1395 execute_configurations(
1396 **shared_params,
1397 problem=problems[i],
1398 ax=axs.flatten()[i],
1399 decorate=True,
1400 configurations=get_configs(mode, problems[i]),
1401 mode=mode,
1402 )
1404 if plotting and shared_params['comm_world'].rank == 0:
1405 ncols = {
1406 'parallel_efficiency': 2,
1407 'RK_comp': 2,
1408 }
1409 y_right_dt_fixed = [1e18, 4e1, 5, 1e8]
1410 y_right_dt = [1e-1, 1e4, 1, 2e-2]
1411 y_right_dtk = [1e-4, 1e4, 1e-2, 1e-3]
1413 if shared_params['work_key'] == 'param':
1414 for ax, yRfixed, yRdt, yRdtk in zip(fig.get_axes(), y_right_dt_fixed, y_right_dt, y_right_dtk):
1415 add_order_line(ax, 1, '--', yRdt, marker=None)
1416 add_order_line(ax, 5 / 4, ':', yRdtk, marker=None)
1417 add_order_line(ax, 5, '-.', yRfixed, marker=None)
1418 save_fig(
1419 fig=fig,
1420 name=mode,
1421 work_key=shared_params['work_key'],
1422 precision_key=shared_params['precision_key'],
1423 legend=True,
1424 base_path=base_path,
1425 ncols=ncols.get(mode, None),
1426 )
1429def ODEs(mode='compare_strategies', plotting=True, base_path='data', **kwargs): # pragma: no cover
1430 """
1431 Make a plot comparing various strategies for the two ODEs.
1433 Args:
1434 work_key (str): The key in the recorded data you want on the x-axis
1435 precision_key (str): The key in the recorded data you want on the y-axis
1437 Returns:
1438 None
1439 """
1441 fig, axs = get_fig(x=2, y=1)
1443 shared_params = {
1444 'work_key': 'k_SDC',
1445 'precision_key': 'e_global',
1446 'num_procs': 1,
1447 'runs': 1,
1448 'comm_world': MPI.COMM_WORLD,
1449 'record': False,
1450 'plotting': plotting,
1451 **kwargs,
1452 }
1454 problems = [run_vdp, run_Lorenz]
1456 for i in range(len(problems)):
1457 execute_configurations(
1458 **shared_params,
1459 problem=problems[i],
1460 ax=axs.flatten()[i],
1461 decorate=i == 0,
1462 configurations=get_configs(mode, problems[i]),
1463 )
1465 if plotting and shared_params['comm_world'].rank == 0:
1466 save_fig(
1467 fig=fig,
1468 name=f'ODEs-{mode}',
1469 work_key=shared_params['work_key'],
1470 precision_key=shared_params['precision_key'],
1471 legend=True,
1472 base_path=base_path,
1473 )
1476def single_problem(mode, problem, plotting=True, base_path='data', **kwargs): # pragma: no cover
1477 """
1478 Make a plot for a single problem
1480 Args:
1481 mode (str): What you want to look at
1482 problem (function): A problem to run
1483 """
1484 fig, ax = get_fig(1, 1, figsize=figsize_by_journal('Springer_Numerical_Algorithms', 1, 0.8))
1486 params = {
1487 'work_key': 'k_SDC',
1488 'precision_key': 'e_global',
1489 'num_procs': 1,
1490 'runs': 1,
1491 'comm_world': MPI.COMM_WORLD,
1492 'record': False,
1493 'plotting': plotting,
1494 **kwargs,
1495 }
1497 logger.log(26, f"Doing single problem {mode}")
1498 execute_configurations(
1499 **params, problem=problem, ax=ax, decorate=True, configurations=get_configs(mode, problem), mode=mode
1500 )
1502 if plotting:
1503 save_fig(
1504 fig=fig,
1505 name=f'{problem.__name__}-{mode}',
1506 work_key=params['work_key'],
1507 precision_key=params['precision_key'],
1508 legend=False,
1509 base_path=base_path,
1510 )
1513def vdp_stiffness_plot(base_path='data', format='pdf', **kwargs): # pragma: no cover
1514 fig, axs = get_fig(3, 1, sharex=False, sharey=False)
1516 mus = [10, 100, 1000]
1518 for i in range(len(mus)):
1519 params = {
1520 'runs': 1,
1521 'problem': run_vdp,
1522 'record': False,
1523 'work_key': 't',
1524 'precision_key': 'e_global',
1525 'comm_world': MPI.COMM_WORLD,
1526 **kwargs,
1527 }
1528 params['num_procs'] = min(params['comm_world'].size, 5)
1529 params['plotting'] = params['comm_world'].rank == 0
1531 mode = f'vdp_stiffness-{mus[i]}'
1532 configurations = get_configs(mode=mode, problem=run_vdp)
1533 execute_configurations(**params, ax=axs.flatten()[i], decorate=True, configurations=configurations, mode=mode)
1534 axs.flatten()[i].set_title(rf'$\mu={ {mus[i]}} $')
1536 fig.suptitle('Van der Pol')
1537 if params['comm_world'].rank == 0:
1538 save_fig(
1539 fig=fig,
1540 name='vdp-stiffness',
1541 work_key=params['work_key'],
1542 precision_key=params['precision_key'],
1543 legend=False,
1544 base_path=base_path,
1545 format=format,
1546 )
1549def add_order_line(ax, order, ls, y_right=1.0, marker='.'):
1550 x_min = min([min(line.get_xdata()) for line in ax.get_lines()])
1551 x_max = max([max(line.get_xdata()) for line in ax.get_lines()])
1552 y_min = min([min(line.get_ydata()) for line in ax.get_lines()])
1553 y_max = max([max(line.get_ydata()) for line in ax.get_lines()])
1554 x = np.logspace(np.log10(x_min), np.log10(x_max), 100)
1555 y = y_right * (x / x_max) ** order
1556 mask = np.logical_and(y > y_min, y < y_max)
1557 ax.loglog(x[mask], y[mask], ls=ls, color='black', label=f'Order {order}', marker=marker, markevery=5)
1560def aggregate_parallel_efficiency_plot(): # pragma: no cover
1561 """
1562 Make a "weak scaling" plot for diagonal SDC
1563 """
1564 from pySDC.projects.Resilience.strategies import AdaptivityPolynomialError
1566 fig, axs = plt.subplots(2, 2)
1568 _fig, _ax = plt.subplots(1, 1)
1569 num_procs = 1
1570 num_procs_sweeper = 2
1571 problem = run_quench
1573 num_procs_sweeper_list = [2, 3, 4]
1575 for problem, ax in zip([run_vdp, run_Lorenz, run_quench], axs.flatten()):
1576 speedup = []
1577 for num_procs_sweeper in num_procs_sweeper_list:
1578 s, e = plot_parallel_efficiency_diagonalSDC(
1579 ax=_ax,
1580 work_key='t',
1581 precision_key='e_global_rel',
1582 num_procs=num_procs,
1583 num_procs_sweeper=num_procs_sweeper,
1584 problem=problem,
1585 strategy=AdaptivityPolynomialError(),
1586 mode='diagonal_SDC',
1587 handle=f'{num_procs_sweeper} nodes',
1588 )
1589 speedup += [s]
1590 decorate_panel(ax, problem, work_key='nprocs', precision_key='')
1592 ax.plot(num_procs_sweeper_list, speedup, label='speedup')
1593 ax.plot(
1594 num_procs_sweeper_list,
1595 [speedup[i] / num_procs_sweeper_list[i] for i in range(len(speedup))],
1596 label='parallel efficiency',
1597 )
1599 fig.tight_layout()
1600 save_fig(fig, 'parallel_efficiency', 'nprocs', 'speedup')
1603if __name__ == "__main__":
1604 comm_world = MPI.COMM_WORLD
1606 record = comm_world.size > 1
1607 for mode in [
1608 # 'compare_strategies',
1609 # 'RK_comp',
1610 # 'parallel_efficiency',
1611 ]:
1612 params = {
1613 'mode': mode,
1614 'runs': 1,
1615 'plotting': comm_world.rank == 0,
1616 }
1617 params_single = {
1618 **params,
1619 'problem': run_RBC,
1620 }
1621 single_problem(**params_single, work_key='t', precision_key='e_global_rel', record=record)
1623 all_params = {
1624 'record': True,
1625 'runs': 5,
1626 'work_key': 't',
1627 'precision_key': 'e_global_rel',
1628 'plotting': comm_world.rank == 0,
1629 }
1631 for mode in [
1632 'RK_comp',
1633 'parallel_efficiency',
1634 'compare_strategies',
1635 ]:
1636 all_problems(**all_params, mode=mode)
1637 comm_world.Barrier()
1639 if comm_world.rank == 0:
1640 plt.show()