def bench(problem, **kwargs):
"""
Complete benchmark with multiple simulation and performance parameters.
"""
try:
from opescibench import Benchmark, Executor
except:
raise ImportError(
'Could not import opescibench utility package.\n'
'Please install https://github.com/opesci/opescibench')
run = tti_run if problem == 'tti' else acoustic_run
resultsdir = kwargs.pop('resultsdir')
repeats = kwargs.pop('repeats')
class BenchExecutor(Executor):
"""Executor class that defines how to run the benchmark"""
def run(self, *args, **kwargs):
gflopss, oi, timings, _ = run(*args, **kwargs)
for key in timings.keys():
self.register(gflopss[key], measure="gflopss", event=key)
self.register(oi[key], measure="oi", event=key)
self.register(timings[key], measure="timings", event=key)
clear_cache()
bench = Benchmark(name=problem, resultsdir=resultsdir, parameters=kwargs)
bench.execute(BenchExecutor(), warmups=0, repeats=repeats)
bench.save()
class DiffusionExecutor(Executor):
"""Executor class that defines how to run the benchmark"""
def setup(self, **kwargs):
self.ui = ring_initial(spacing=kwargs['spacing'])
def run(self, *args, **kwargs):
u, time = exec_func[kwargs['mode']](
self.ui,
spacing=kwargs['spacing'],
timesteps=kwargs['timesteps'])
self.register(time)
# Run benchmark across parameters and save the result
bench = Benchmark(name='Diffusion',
resultsdir=args.resultsdir,
parameters=parameters)
bench.execute(DiffusionExecutor(), warmups=0, repeats=1)
bench.save()
elif args.execmode == 'plot':
# Load previously generated benchmark data
bench = Benchmark(name='Diffusion',
resultsdir=args.resultsdir,
parameters=parameters)
bench.load()
# Generate the plot from loaded benchmark data
plotter = Plotter()
plotter.plot_comparison('DiffusionModes.pdf', args.mode,
bench.lookup())
elif args.execmode == 'bench':
if Benchmark is None:
raise ImportError("Could not find opescibench utility package.\n"
"Please install from https://github.com/opesci/opescibench")
class DiffusionExecutor(Executor):
"""Executor class that defines how to run the benchmark"""
def setup(self, **kwargs):
self.ui = ring_initial(spacing=kwargs['spacing'])
def run(self, *args, **kwargs):
u, time = exec_func[kwargs['mode']](self.ui, spacing=kwargs['spacing'],
timesteps=kwargs['timesteps'])
self.register(time)
# Run benchmark across parameters and save the result
bench = Benchmark(name='Diffusion', resultsdir=args.resultsdir,
parameters=parameters)
bench.execute(DiffusionExecutor(), warmups=0, repeats=1)
bench.save()
elif args.execmode == 'plot':
# Load previously generated benchmark data
bench = Benchmark(name='Diffusion', resultsdir=args.resultsdir,
parameters=parameters)
bench.load()
# Generate the plot from loaded benchmark data
plotter = Plotter()
plotter.plot_comparison('DiffusionModes.pdf', args.mode, bench.lookup())