# MODULE: grass.benchmark
#
# AUTHOR(S): Vaclav Petras <wenzeslaus gmail com>
#
# PURPOSE: Benchmarking for GRASS GIS modules
#
# COPYRIGHT: (C) 2021 Vaclav Petras, and by the GRASS Development Team
#
# This program is free software under the GNU General Public
# License (>=v2). Read the file COPYING that comes with GRASS
# for details.
"""Plotting functionality for benchmark results"""
[docs]def get_pyplot(to_file):
"""Get pyplot from matplotlib
Lazy import to easily run code importing this function on limited installations.
Only actual call to this function requires matplotlib.
The *to_file* parameter can be set to True to avoid tkinter dependency
if the interactive show method is not needed.
"""
import matplotlib as mpl # pylint: disable=import-outside-toplevel
backend = "agg" if to_file else None
if backend:
mpl.use(backend)
import matplotlib.pyplot as plt # pylint: disable=import-outside-toplevel
return plt
[docs]def nprocs_plot(results, filename=None, title=None, metric="time"):
"""Plot results from a multiple nprocs (thread) benchmarks.
*results* is a list of individual results from separate benchmarks.
One result is required to have attributes: *nprocs*, *times*, *label*.
The *nprocs* attribute is a list of all processing elements
(cores, threads, processes) used in the benchmark.
The *times* attribute is a list of corresponding times for each value
from the *nprocs* list.
The *label* attribute identifies the benchmark in the legend.
*metric* can be "time", "speedup", or "efficiency".
This function plots a corresponding figure based on the chosen metric.
Optionally, result can have an *all_times* attribute which is a list
of lists. One sublist is all times recorded for each value of nprocs.
Each result can come with a different list of nprocs, i.e., benchmarks
which used different values for nprocs can be combined in one plot.
"""
ylabel = ""
plt = get_pyplot(to_file=bool(filename))
_, axes = plt.subplots()
x_ticks = set() # gather x values
for result in results:
x = result.nprocs
x_ticks.update(x)
if metric == "time":
mins = [min(i) for i in result.all_times]
maxes = [max(i) for i in result.all_times]
plt.plot(x, result.times, label=result.label)
plt.fill_between(x, mins, maxes, color="gray", alpha=0.3)
ylabel = "Time [s]"
elif metric in {"speedup", "efficiency"}:
ylabel = metric.title()
plt.plot(x, getattr(result, metric), label=result.label)
else:
msg = f"Invalid metric '{metric}' in result, it should be:\
'time', 'speedup' or 'efficiency'"
raise ValueError(msg)
plt.legend()
# If there is not many x values, show ticks for each, but use default
# ticks when there is a lot of x values.
if len(x_ticks) < 10:
axes.set(xticks=sorted(x_ticks))
else:
from matplotlib.ticker import ( # pylint: disable=import-outside-toplevel
MaxNLocator,
)
axes.xaxis.set_major_locator(MaxNLocator(integer=True))
plt.xlabel("Number of processing elements (cores, threads, processes)")
plt.ylabel(ylabel)
if title:
plt.title(title)
elif metric == "times":
plt.title("Execution time by processing elements")
elif metric in {"speedup", "efficiency"}:
plt.title(f"{metric.title()} by processing elements")
if filename:
plt.savefig(filename)
else:
plt.show()
[docs]def num_cells_plot(results, filename=None, title=None, show_resolution=False):
"""Plot results from a multiple raster grid size benchmarks.
*results* is a list of individual results from separate benchmarks
with one result being similar to the :func:`nprocs_plot` function.
The result is required to have *times* and *label* attributes
and may have an *all_times* attribute.
Further, it is required to have *cells* attribute, or,
when ``show_resolution=True``, it needs to have a *resolutions* attribute.
Each result can come with a different list of nprocs, i.e., benchmarks
which used different values for nprocs can be combined in one plot.
"""
plt = get_pyplot(to_file=bool(filename))
axes = plt.gca()
if show_resolution:
axes.invert_xaxis()
x_ticks = set()
for result in results:
x = result.resolutions if show_resolution else result.cells
x_ticks.update(x)
plt.plot(x, result.times, label=result.label)
if hasattr(result, "all_times"):
mins = [min(i) for i in result.all_times]
maxes = [max(i) for i in result.all_times]
plt.fill_between(x, mins, maxes, color="gray", alpha=0.3)
plt.legend()
axes.set(xticks=sorted(x_ticks))
if not show_resolution:
axes.ticklabel_format(axis="x", style="scientific", scilimits=(0, 0))
if show_resolution:
plt.xlabel("Resolution [map units]")
else:
plt.xlabel("Number of cells")
plt.ylabel("Time [s]")
if title:
plt.title(title)
elif show_resolution:
plt.title("Execution time by resolution")
else:
plt.title("Execution time by cell count")
if filename:
plt.savefig(filename)
else:
plt.show()
