gem5/util/dram_sweep_plot.py
Andreas Hansson 40fcb59c54 util: Fix minor issues in DRAM sweep scripts
This patch fixes a few issues in the sweep scripts, bringing them
up-to-date with the latest memory configs and options.
2015-09-25 06:45:50 -04:00

195 lines
6.6 KiB
Python
Executable file

#!/usr/bin/env python
# Copyright (c) 2014 ARM Limited
# All rights reserved
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# modified or unmodified, in source code or in binary form.
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# modification, are permitted provided that the following conditions are
# met: redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer;
# redistributions in binary form must reproduce the above copyright
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# neither the name of the copyright holders nor the names of its
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# this software without specific prior written permission.
#
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# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
# OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
# Authors: Andreas Hansson
try:
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
from matplotlib.ticker import LinearLocator, FormatStrFormatter
import matplotlib.pyplot as plt
import numpy as np
except ImportError:
print "Failed to import matplotlib and numpy"
exit(-1)
import sys
import re
# Determine the parameters of the sweep from the simout output, and
# then parse the stats and plot the 3D surface corresponding to the
# different combinations of parallel banks, and stride size, as
# generated by the config/dram/sweep.py script
def main():
if len(sys.argv) != 3:
print "Usage: ", sys.argv[0], "-u|p|e <simout directory>"
exit(-1)
if len(sys.argv[1]) != 2 or sys.argv[1][0] != '-' or \
not sys.argv[1][1] in "upe":
print "Choose -u (utilisation), -p (total power), or -e " \
"(power efficiency)"
exit(-1)
# Choose the appropriate mode, either utilisation, total power, or
# efficiency
mode = sys.argv[1][1]
try:
stats = open(sys.argv[2] + '/stats.txt', 'r')
except IOError:
print "Failed to open ", sys.argv[1] + '/stats.txt', " for reading"
exit(-1)
try:
simout = open(sys.argv[2] + '/simout', 'r')
except IOError:
print "Failed to open ", sys.argv[1] + '/simout', " for reading"
exit(-1)
# Get the burst size, number of banks and the maximum stride from
# the simulation output
got_sweep = False
for line in simout:
match = re.match("DRAM sweep with "
"burst: (\d+), banks: (\d+), max stride: (\d+)", line)
if match:
burst_size = int(match.groups(0)[0])
banks = int(match.groups(0)[1])
max_size = int(match.groups(0)[2])
got_sweep = True
simout.close()
if not got_sweep:
print "Failed to establish sweep details, ensure simout is up-to-date"
exit(-1)
# Now parse the stats
peak_bw = []
bus_util = []
avg_pwr = []
for line in stats:
match = re.match(".*busUtil\s+(\d+\.\d+)\s+#.*", line)
if match:
bus_util.append(float(match.groups(0)[0]))
match = re.match(".*peakBW\s+(\d+\.\d+)\s+#.*", line)
if match:
peak_bw.append(float(match.groups(0)[0]))
match = re.match(".*averagePower\s+(\d+\.?\d*)\s+#.*", line)
if match:
avg_pwr.append(float(match.groups(0)[0]))
stats.close()
# Sanity check
if not (len(peak_bw) == len(bus_util) and len(bus_util) == len(avg_pwr)):
print "Peak bandwidth, bus utilisation, and average power do not match"
exit(-1)
# Collect the selected metric as our Z-axis, we do this in a 2D
# grid corresponding to each iteration over the various stride
# sizes.
z = []
zs = []
i = 0
for j in range(len(peak_bw)):
if mode == 'u':
z.append(bus_util[j])
elif mode == 'p':
z.append(avg_pwr[j])
elif mode == 'e':
# avg_pwr is in mW, peak_bw in MiByte/s, bus_util in percent
z.append(avg_pwr[j] / (bus_util[j] / 100.0 * peak_bw[j] / 1000.0))
else:
print "Unexpected mode %s" % mode
exit(-1)
i += 1
# If we have completed a sweep over the stride sizes,
# start anew
if i == max_size / burst_size:
zs.append(z)
z = []
i = 0
# We should have a 2D grid with as many columns as banks
if len(zs) != banks:
print "Unexpected number of data points in stats output"
exit(-1)
fig = plt.figure()
ax = fig.gca(projection='3d')
X = np.arange(burst_size, max_size + 1, burst_size)
Y = np.arange(1, banks + 1, 1)
X, Y = np.meshgrid(X, Y)
# the values in the util are banks major, so we see groups for each
# stride size in order
Z = np.array(zs)
surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.coolwarm,
linewidth=0, antialiased=False)
# Change the tick frequency to 64
start, end = ax.get_xlim()
ax.xaxis.set_ticks(np.arange(start, end + 1, 64))
ax.set_xlabel('Bytes per activate')
ax.set_ylabel('Banks')
if mode == 'u':
ax.set_zlabel('Utilisation (%)')
elif mode == 'p':
ax.set_zlabel('Power (mW)')
elif mode == 'e':
ax.set_zlabel('Power efficiency (mW / GByte / s)')
# Add a colorbar
fig.colorbar(surf, shrink=0.5, pad=.1, aspect=10)
plt.show()
if __name__ == "__main__":
main()