mem: Add utility script to plot DRAM efficiency sweep
This patch adds basic functionality to quickly visualise the output from the DRAM efficiency script. There are some unfortunate hacks needed to communicate the needed information from one script to the other, and we fall back on (ab)using the simout to do this. As part of this patch we also trim the efficiency sweep to stop at 512 bytes as this should be sufficient for all forseeable DRAMs.
This commit is contained in:
Andreas Hansson
parent
77c28cc395
commit
0756406739
2 changed files with 161 additions and 3 deletions
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@ -124,12 +124,16 @@ itt = system.mem_ctrls[0].tBURST.value * 1000000000000
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# assume we start at 0
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max_addr = mem_range.end
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# use min of the page size and 512 bytes as that should be more than
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# enough
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max_stride = min(512, page_size)
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# now we create the state by iterating over the stride size from burst
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# size to min of the page size and 1 kB, and from using only a single
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# bank up to the number of banks available
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# size to the max stride, and from using only a single bank up to the
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# number of banks available
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nxt_state = 0
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for bank in range(1, nbr_banks + 1):
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for stride_size in range(burst_size, min(1024, page_size) + 1, burst_size):
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for stride_size in range(burst_size, max_stride + 1, burst_size):
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cfg_file.write("STATE %d %d DRAM 100 0 %d "
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"%d %d %d %d %d %d %d %d 1\n" %
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(nxt_state, period, max_addr, burst_size, itt, itt, 0,
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@ -168,3 +172,6 @@ root.system.mem_mode = 'timing'
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m5.instantiate()
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m5.simulate(nxt_state * period)
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print "DRAM sweep with burst: %d, banks: %d, max stride: %d" % \
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(burst_size, nbr_banks, max_stride)
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151
util/dram_sweep_plot.py
Executable file
151
util/dram_sweep_plot.py
Executable file
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@ -0,0 +1,151 @@
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#!/usr/bin/env python
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# Copyright (c) 2014 ARM Limited
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# All rights reserved
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#
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# The license below extends only to copyright in the software and shall
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# not be construed as granting a license to any other intellectual
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# property including but not limited to intellectual property relating
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# to a hardware implementation of the functionality of the software
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# licensed hereunder. You may use the software subject to the license
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# terms below provided that you ensure that this notice is replicated
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# unmodified and in its entirety in all distributions of the software,
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# modified or unmodified, in source code or in binary form.
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#
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# Redistribution and use in source and binary forms, with or without
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# modification, are permitted provided that the following conditions are
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# met: redistributions of source code must retain the above copyright
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# notice, this list of conditions and the following disclaimer;
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# redistributions in binary form must reproduce the above copyright
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# notice, this list of conditions and the following disclaimer in the
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# documentation and/or other materials provided with the distribution;
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# neither the name of the copyright holders nor the names of its
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# contributors may be used to endorse or promote products derived from
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# this software without specific prior written permission.
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#
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
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# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
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# OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
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# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
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# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
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# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
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# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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#
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# Authors: Andreas Hansson
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try:
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from mpl_toolkits.mplot3d import Axes3D
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from matplotlib import cm
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from matplotlib.ticker import LinearLocator, FormatStrFormatter
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import matplotlib.pyplot as plt
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import numpy as np
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except ImportError:
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print "Failed to import matplotlib and numpy"
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exit(-1)
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import sys
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import re
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# Determine the parameters of the sweep from the simout output, and
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# then parse the stats and plot the 3D surface corresponding to the
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# different combinations of parallel banks, and stride size, as
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# generated by the config/dram/sweep.py script
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def main():
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if len(sys.argv) != 2:
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print "Usage: ", sys.argv[0], " <simout directory>"
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exit(-1)
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try:
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stats = open(sys.argv[1] + '/stats.txt', 'r')
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except IOError:
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print "Failed to open ", sys.argv[1] + '/stats.txt', " for reading"
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exit(-1)
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try:
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simout = open(sys.argv[1] + '/simout', 'r')
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except IOError:
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print "Failed to open ", sys.argv[1] + '/simout', " for reading"
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exit(-1)
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# Get the burst size, number of banks and the maximum stride from
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# the simulation output
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got_sweep = False
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for line in simout:
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match = re.match("DRAM sweep with "
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"burst: (\d+), banks: (\d+), max stride: (\d+)", line)
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if match:
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burst_size = int(match.groups(0)[0])
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banks = int(match.groups(0)[1])
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max_size = int(match.groups(0)[2])
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got_sweep = True
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simout.close()
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if not got_sweep:
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print "Failed to establish sweep details, ensure simout is up-to-date"
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exit(-1)
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# Collect the bus utilisation as our Z-axis, we do this in a 2D
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# grid corresponding to each iteration over the various stride
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# sizes.
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z = []
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zs = []
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i = 0
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# Now parse the stats
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for line in stats:
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match = re.match(".*busUtil\s+(\d+\.\d+)\s+#.*", line)
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if match:
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bus_util = float(match.groups(0)[0])
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z.append(bus_util)
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i += 1
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# If we have completed a sweep over the stride sizes,
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# start anew
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if i == max_size / burst_size:
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zs.append(z)
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z = []
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i = 0
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stats.close()
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# We should have a 2D grid with as many columns as banks
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if len(zs) != banks:
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print "Unexpected number of data points in stats output"
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exit(-1)
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fig = plt.figure()
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ax = fig.gca(projection='3d')
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X = np.arange(burst_size, max_size + 1, burst_size)
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Y = np.arange(1, banks + 1, 1)
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X, Y = np.meshgrid(X, Y)
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# the values in the util are banks major, so we see groups for each
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# stride size in order
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Z = np.array(zs)
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surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.coolwarm,
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linewidth=0, antialiased=False)
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# Change the tick frequency to 64
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start, end = ax.get_xlim()
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ax.xaxis.set_ticks(np.arange(start, end + 1, 64))
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ax.set_xlabel('Bytes per activate')
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ax.set_ylabel('Banks')
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ax.set_zlabel('Efficiency (%)')
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# Add a colorbar
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fig.colorbar(surf, shrink=0.5, pad=.1, aspect=10)
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plt.show()
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if __name__ == "__main__":
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main()
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