ylabel='Parallel efficiency w.r.t. %d cores' % nprocs[0],
xaxis='np',
xlabel=xlabel,
xticklabels=args.parallel,
groups=groups,
regions=regions,
kinds='semilogx',
axis='tight',
title='',
labels=labels,
transform=efficiency,
ymin=0)
if __name__ == '__main__':
p = parser(description="Performance benchmark for 2D eigenmode test.")
p.add_argument('mode',
choices=('strong', 'error', 'comparison'),
nargs='?',
default='scaling',
help='determines type of plot to generate')
p.add_argument('--dim',
type=int,
default=3,
help='problem dimension to plot')
p.add_argument('-N',
'--size',
type=int,
nargs='+',
default=[32],
help='mesh sizes to plot')
self.plot(figsize=self.figsize, format='pdf', figname='PropCC_%s'%region,
xaxis='opt_level', xvals=opt_level, xticklabels=opt_str,
xlabel='Compiler configuration', groups=groups, regions=[region],
kinds='bar', title='Performance: %s'%region, legend={'loc': 'best'})
def plot_parallel_scaling(self, nthreads):
groups = ['compiler', 'opt_level', 'affinity']
for region in self.profileregions:
self.plot(figsize=self.figsize, format='pdf', figname='PropOMP_%s'%region,
xaxis='nthreads', xticklabels=nthreads, xlabel='Number of threads',
regions=[region], groups=groups, xmax=nthreads[-1], trendline='Perfect speedup',
kinds='loglog', title='Performance: %s'%region, legend={'loc': 'best'})
if __name__ == '__main__':
p = parser(description="Performance benchmark plotter for 3D Propagator test.")
p.add_argument('--basename', type=str, nargs='+',
help='Name of test used for benchmarking')
p.add_argument('--compiler', type=str, nargs='+',
help='Compilers to compare')
p.add_argument('--opt_level', type=int, nargs='+',
help='Compiler optimisation levels to compare')
p.add_argument('--affinity', type=str, nargs='+',
help='Parallel thread affintiy setting used')
args = p.parse_args()
basename = args.basename or ['test3d']
compiler = args.compiler or ['g++', 'clang', 'icpc']
opt_level = args.opt_level or [2, 3]
nthreads = args.parallel or [1]
affinity = args.affinity or ['close']
'diffusion RHS': {
'marker': 'v'
},
'advection solve': {
'marker': 's'
},
'diffusion solve': {
'marker': 'D'
}
}
profilegraph = {'format': 'svg,pdf', 'node_threshold': 2.0}
profileregions = regions
if __name__ == '__main__':
p = parser(description="Plot results for advection-diffusion benchmark")
p.add_argument('-d',
'--degree',
type=int,
nargs='+',
help='polynomial degrees to plot')
p.add_argument('-m',
'--size',
type=int,
nargs='+',
help='mesh sizes to plot')
p.add_argument('-v', '--variant', nargs='+', help='variants to plot')
args = p.parse_args()
variants = args.variant or ['Firedrake', 'DOLFIN']
groups = ['variant'] if len(variants) > 1 else []
degrees = args.degree or [1, 2, 3]
from pybench import parser, Benchmark
import pylab as plt
class PlutoPlot(Benchmark):
"""
example: python tests/pluto_plot.py -ts "2 4 8" "4 4 1000" "default" "nopluto"
"""
figsize = (6, 4)
if __name__ == '__main__':
p = parser(description="pluto performance benchmark for different tile sizes ")
p.add_argument('-ts', '--tile_size', type=str, nargs='+',
help='tile sizes to plot')
args = p.parse_args()
ts = args.tile_size
b = PlutoPlot(benchmark='pluto_tile_bench', resultsdir=args.resultsdir, plotdir=args.plotdir)
b.combine_series([('tile_size', ts)], filename='pluto_tile_bench')
res = []
for data in b.result['timings'].values():
del(data['total'])
res.append(data.values()[0])
labels_ = b.result['timings'].keys()
labels = [x[0] for x in labels_]
class Poisson(Benchmark):
plotstyle = {'total': {'marker': '*'},
'mesh': {'marker': '+'},
'setup': {'marker': 'x'},
'matrix assembly': {'marker': '>'},
'rhs assembly': {'marker': '<'},
'solve': {'marker': 'D'}}
method = 'poisson'
benchmark = 'Poisson'
profilegraph = {'format': 'svg,pdf',
'node_threshold': 2.0}
profileregions = regions
if __name__ == '__main__':
p = parser(description="Plot results for Poisson benchmark")
p.add_argument('-d', '--degree', type=int, nargs='+',
help='polynomial degrees to plot')
p.add_argument('-m', '--size', type=int, nargs='+',
help='mesh sizes to plot')
p.add_argument('-v', '--variant', nargs='+',
help='variants to plot')
p.add_argument('-b', '--base', type=int,
help='index of size to use as base for parallel efficiency')
args = p.parse_args()
variants = args.variant or ['Firedrake', 'DOLFIN']
groups = ['variant'] if len(variants) > 1 else []
degrees = args.degree or [1, 2, 3]
if args.sequential:
b = Poisson(resultsdir=args.resultsdir, plotdir=args.plotdir)
b.combine_series([('np', [1]), ('variant', variants),
cells[0.026] = 62893912
vertices[0.577] = 66615
vertices[0.408] = 133342
vertices[0.316] = 223100
vertices[0.289] = 255050
vertices[0.204] = 498559
vertices[0.158] = 868981
vertices[0.144] = 1000124
vertices[0.102] = 2049232
vertices[0.072] = 4108380
vertices[0.051] = 8182936
vertices[0.036] = 16416447
vertices[0.026] = 31446948
if __name__ == '__main__':
p = parser(description="Plot results for explicit wave benchmark")
p.add_argument('-m', '--scale', type=float, nargs='+',
help='mesh scales to plot')
p.add_argument('-v', '--variant', nargs='+',
help='variants to plot')
p.add_argument('-b', '--base', type=int,
help='index of size to use as base for parallel efficiency')
args = p.parse_args()
variants = args.variant or ['Firedrake', 'DOLFIN']
groups = ['variant'] if len(variants) > 1 else []
if args.sequential:
b = Wave(resultsdir=args.resultsdir, plotdir=args.plotdir)
b.combine_series([('np', [1]), ('scale', args.scale or scale),
('variant', variants)])
b.plot(xaxis='scale', regions=regions, xlabel='mesh size (cells)',
xvalues=cells, kinds='plot,loglog', groups=groups,
format='pdf',
figname='PropOMP_%s' % region,
xaxis='nthreads',
xticklabels=nthreads,
xlabel='Number of threads',
regions=[region],
groups=groups,
xmax=nthreads[-1],
trendline='Perfect speedup',
kinds='loglog',
title='Performance: %s' % region,
legend={'loc': 'best'})
if __name__ == '__main__':
p = parser(
description="Performance benchmark plotter for 3D Propagator test.")
p.add_argument('--basename',
type=str,
nargs='+',
help='Name of test used for benchmarking')
p.add_argument('--compiler',
type=str,
nargs='+',
help='Compilers to compare')
p.add_argument('--opt_level',
type=int,
nargs='+',
help='Compiler optimisation levels to compare')
p.add_argument('--affinity',
type=str,
nargs='+',
class CahnHilliard(Benchmark):
method = 'cahn_hilliard'
benchmark = 'CahnHilliard'
plotstyle = {'total': {'marker': '*'},
'mesh': {'marker': 's'},
'initial condition': {'marker': 'D'},
'Assemble cells': {'marker': '^'},
'SNES solver execution': {'marker': 'o'}}
profilegraph = {'format': 'svg,eps',
'node_threshold': 2.0}
profileregions = ['setup', 'initial condition', 'timestepping']
if __name__ == '__main__':
p = parser(description="Plot results for CahnHilliard benchmark")
p.add_argument('-m', '--size', type=int, nargs='+',
help='mesh sizes to plot')
p.add_argument('-r', '--region', nargs='+', help='regions to plot')
p.add_argument('-v', '--variant', nargs='+', help='variants to plot')
p.add_argument('-b', '--base', type=int,
help='index of size to use as base for parallel efficiency')
args = p.parse_args()
regions = args.region or regions
variants = args.variant or ['Firedrake', 'DOLFIN']
groups = ['variant'] if len(variants) > 1 else []
aggregate = {'Assemble cells': ['Assemble cells', 'Apply (PETScVector)', 'Apply (PETScMatrix)']}
if args.sequential:
b = CahnHilliard(resultsdir=args.resultsdir, plotdir=args.plotdir)
b.combine_series([('np', [1]), ('variant', variants), ('size', args.size or sizes)],
aggregate=aggregate)
'mesh': {'marker': '+'},
'setup': {'marker': 'x'},
'matrix assembly': {'marker': 'p'},
'timestepping': {'marker': 'o'},
'tentative velocity RHS': {'marker': '^'},
'tentative velocity solve': {'marker': 'v'},
'pressure correction RHS': {'marker': 's'},
'pressure correction solve': {'marker': 'D'},
'velocity correction RHS': {'marker': '>'},
'velocity correction solve': {'marker': '<'}}
profilegraph = {'format': 'svg,pdf',
'node_threshold': 2.0}
profileregions = ['matrix assembly'] + map(' '.join, product(r1, r2))
if __name__ == '__main__':
p = parser(description="Plot results for Navier-Stokes benchmark")
p.add_argument('-m', '--scale', type=float, nargs='+',
help='mesh scales to plot')
p.add_argument('-v', '--variant', nargs='+', help='variants to plot')
p.add_argument('-b', '--base', type=int,
help='index of size to use as base for parallel efficiency')
p.add_argument('-r', '--region', nargs='+', help='regions to plot')
args = p.parse_args()
regions = args.region or map(' '.join, product(r1, r2))
variants = args.variant or ['Firedrake', 'DOLFIN']
groups = ['variant'] if len(variants) > 1 else []
scales = args.scale or scale
if args.sequential:
b = NavierStokes(resultsdir=args.resultsdir, plotdir=args.plotdir)
b.combine_series([('np', [1]), ('scale', scales), ('variant', variants)])
b.plot(xaxis='scale', regions=regions, xlabel='DOFs', groups=groups,
from pybench import parser, Benchmark
import pylab as plt
class PlutoPlot(Benchmark):
"""
example: python tests/pluto_plot.py -ts "2 4 8" "4 4 1000" "default" "nopluto"
"""
figsize = (6, 4)
if __name__ == '__main__':
p = parser(
description="pluto performance benchmark for different tile sizes ")
p.add_argument('-ts',
'--tile_size',
type=str,
nargs='+',
help='tile sizes to plot')
args = p.parse_args()
ts = args.tile_size
b = PlutoPlot(benchmark='pluto_tile_bench',
resultsdir=args.resultsdir,
plotdir=args.plotdir)
b.combine_series([('tile_size', ts)], filename='pluto_tile_bench')
res = []
for data in b.result['timings'].values():
'pressure correction solve': {
'marker': 'D'
},
'velocity correction RHS': {
'marker': '>'
},
'velocity correction solve': {
'marker': '<'
}
}
profilegraph = {'format': 'svg,pdf', 'node_threshold': 2.0}
profileregions = ['matrix assembly'] + map(' '.join, product(r1, r2))
if __name__ == '__main__':
p = parser(description="Plot results for Navier-Stokes benchmark")
p.add_argument('-m',
'--scale',
type=float,
nargs='+',
help='mesh scales to plot')
p.add_argument('-v', '--variant', nargs='+', help='variants to plot')
p.add_argument('-b',
'--base',
type=int,
help='index of size to use as base for parallel efficiency')
p.add_argument('-r', '--region', nargs='+', help='regions to plot')
args = p.parse_args()
regions = args.region or map(' '.join, product(r1, r2))
variants = args.variant or ['Firedrake', 'DOLFIN']
groups = ['variant'] if len(variants) > 1 else []
},
'rhs assembly': {
'marker': '<'
},
'solve': {
'marker': 'D'
}
}
method = 'poisson'
benchmark = 'Poisson'
profilegraph = {'format': 'svg,pdf', 'node_threshold': 2.0}
profileregions = regions
if __name__ == '__main__':
p = parser(description="Plot results for Poisson benchmark")
p.add_argument('-d',
'--degree',
type=int,
nargs='+',
help='polynomial degrees to plot')
p.add_argument('-m',
'--size',
type=int,
nargs='+',
help='mesh sizes to plot')
p.add_argument('-v', '--variant', nargs='+', help='variants to plot')
p.add_argument('-b',
'--base',
type=int,
help='index of size to use as base for parallel efficiency')
class CahnHilliard(Benchmark):
method = 'cahn_hilliard'
benchmark = 'CahnHilliard'
plotstyle = {'total': {'marker': '*'},
'mesh': {'marker': 's'},
'initial condition': {'marker': 'D'},
'Assemble cells': {'marker': '^'},
'SNES solver execution': {'marker': 'o'}}
profilegraph = {'format': 'svg,pdf',
'node_threshold': 2.0}
profileregions = ['setup', 'initial condition', 'timestepping']
if __name__ == '__main__':
p = parser(description="Plot results for CahnHilliard benchmark")
p.add_argument('-m', '--size', type=int, nargs='+',
help='mesh sizes to plot')
p.add_argument('-r', '--region', nargs='+', help='regions to plot')
p.add_argument('-v', '--variant', nargs='+', help='variants to plot')
p.add_argument('-b', '--base', type=int,
help='index of size to use as base for parallel efficiency')
args = p.parse_args()
regions = args.region or regions
variants = args.variant or ['Firedrake', 'DOLFIN']
groups = ['variant'] if len(variants) > 1 else []
aggregate = {'Assemble cells': ['Assemble cells', 'Apply (PETScVector)', 'Apply (PETScMatrix)']}
if args.sequential:
b = CahnHilliard(resultsdir=args.resultsdir, plotdir=args.plotdir)
b.combine_series([('np', [1]), ('variant', variants), ('size', args.size or sizes)],
aggregate=aggregate)
cells[0.026] = 62893912
vertices[0.577] = 66615
vertices[0.408] = 133342
vertices[0.316] = 223100
vertices[0.289] = 255050
vertices[0.204] = 498559
vertices[0.158] = 868981
vertices[0.144] = 1000124
vertices[0.102] = 2049232
vertices[0.072] = 4108380
vertices[0.051] = 8182936
vertices[0.036] = 16416447
vertices[0.026] = 31446948
if __name__ == '__main__':
p = parser(description="Plot results for explicit wave benchmark")
p.add_argument('-m',
'--scale',
type=float,
nargs='+',
help='mesh scales to plot')
p.add_argument('-v', '--variant', nargs='+', help='variants to plot')
p.add_argument('-b',
'--base',
type=int,
help='index of size to use as base for parallel efficiency')
args = p.parse_args()
variants = args.variant or ['Firedrake', 'DOLFIN']
groups = ['variant'] if len(variants) > 1 else []
if args.sequential:
b = Wave(resultsdir=args.resultsdir, plotdir=args.plotdir)