SetupSpectralMethods(jg)
jg.runtime.timestep_size = 2 # second #jg.runtime.output_timestep_size/100.0
jg.runtime.timestepping_method = tsm[0]
jg.runtime.timestepping_order = tsm[1]
jg.runtime.timestepping_order2 = tsm[2]
jg.runtime.space_res_physical = -1
#jg.runtime.space_res_spectral = 1024
jg.runtime.space_res_spectral = phys_res_list[0]
# Tag this as a reference job
jg.reference_job = True
jg.gen_jobscript_directory()
jg.reference_job = False
# Use this one as the reference solution!
jg.reference_job_unique_id = jg.job_unique_id
#
# Use only 2 iterations for Semi-Lagrangian methods
#
unique_id_filter.append('runtime.semi_lagrangian')
jg.runtime.semi_lagrangian_iterations = 2
jg.runtime.semi_lagrangian_convergence_threshold = -1
jg.parallelization.max_wallclock_seconds = max_wallclock_seconds
for tsm in ts_methods[1:]:
if 'ln_erk' in tsm[0]:
SetupFDCMethods(jg)
else:
if verbose:
pspace.print()
ptime.print()
p.parallelization.print()
if len(tsm) > 4:
s = tsm[4]
p.load_from_dict(tsm[4])
p.parallelization.max_wallclock_seconds = estimateWallclockTime(
p)
p.gen_jobscript_directory('job_benchref_' + p.getUniqueID())
p.reference_job_unique_id = p.job_unique_id
#
# Create job scripts
#
for tsm in ts_methods[1:]:
p.runtime.timestepping_method = tsm[0]
p.runtime.timestepping_order = tsm[1]
p.runtime.timestepping_order2 = tsm[2]
if len(tsm) > 4:
s = tsm[4]
p.runtime.load_from_dict(tsm[4])
tsm_name = tsm[0]
if 'ln_erk' in tsm_name:
