257 lines
9.3 KiB
Python
257 lines
9.3 KiB
Python
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#!/usr/bin/env python
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# Copyright (c) 2013, The Linux Foundation. All rights reserved.
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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:
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# * 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
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# copyright notice, this list of conditions and the following
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# disclaimer in the documentation and/or other materials provided
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# with the distribution.
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# * Neither the name of The Linux Foundation nor the names of its
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# contributors may be used to endorse or promote products derived
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# from this software without specific prior written permission.
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#
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# THIS SOFTWARE IS PROVIDED "AS IS" AND ANY EXPRESS OR IMPLIED
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# WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
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# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT
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# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS
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# BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
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# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR
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# BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
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# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE
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# OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN
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# IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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# Quick and very dirty parser for the output of `memory_prof.sh -e'
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# This should be run on the host (not the target) with the output from
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# the target piped in (e.g. copy/paste/cat) or passed as a file on the
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# command line
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# Dependencies: matplotlib, numpy
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# NOTE: THIS HAS GOTTEN UNWIELDY. YOU HAVE BEEN WARNED.
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import sys
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import fileinput
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import operator
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from itertools import cycle
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from optparse import OptionParser
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import numpy as np
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import matplotlib.pyplot as plt
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ST_PREFIX_DATA_ROW = "=> "
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ST_PREFIX_PREALLOC_SIZE = "==> "
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ST_PREFIX_NUM_REPS = "===> "
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def get_data_lines(data):
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return [line.lstrip(ST_PREFIX_DATA_ROW).rstrip('\n') for line in data if line.startswith(ST_PREFIX_DATA_ROW)]
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def extract_data_for_size(data, target_sz):
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cached_timings = {
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'ION_IOC_ALLOC': [],
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'ION_IOC_FREE': [],
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}
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uncached_timings = {
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'ION_IOC_ALLOC': [],
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'ION_IOC_FREE': [],
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}
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heaps = []
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for line in get_data_lines(data):
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# for the format, see print_stats_results in memory_prof.c
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(ion_op, heap_id, caching, sz, lbl_av, average, lbl_std, std_dev) = line.split(' ')
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if sz != target_sz: continue
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av = max(float(average), 0)
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if heap_id not in heaps:
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heaps.append(heap_id)
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if caching == 'cached':
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cached_timings[ion_op].append(av)
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else:
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uncached_timings[ion_op].append(av)
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return (heaps, cached_timings, uncached_timings)
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def extract_all_data(data):
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timings = {
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'ION_IOC_ALLOC': {},
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'ION_IOC_FREE': {},
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}
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for line in get_data_lines(data):
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# for the format, see print_stats_results in memory_prof.c
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(ion_op, heap_id, caching, sz, lbl_av, average, lbl_std, std_dev) = line.split(' ')
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timings[ion_op].setdefault(heap_id, {})
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av = max(float(average), 0)
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timings[ion_op][heap_id].setdefault(caching, {})
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timings[ion_op][heap_id][caching][sz] = float(average)
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return timings
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def compare_heaps_for_a_size(data, target_sz, num_reps, pre_alloc_size, ion_op, text_only=False, target=None):
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(heaps, cached_timings, uncached_timings) = extract_data_for_size(data, target_sz)
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cached_timings = cached_timings[ion_op]
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uncached_timings = uncached_timings[ion_op]
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title = ('Ion %s times\n' % ion_op) \
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+ ('Target: %s' % ("%s\n" % target) if target is not None else "") \
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+ ('(%s with ION_IOC_ALLOC, average of %d reps, %dMB pre-allocation)' % (target_sz, num_reps, pre_alloc_size))
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print title
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print
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for (heap, cached, uncached) in zip(heaps, cached_timings, uncached_timings):
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print "%25s (cached): %f" % (heap, cached)
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print "%25s (uncached): %s" % (heap, str(uncached) if uncached != 0 else "N/A")
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if text_only:
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return
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ind = np.arange(len(heaps))
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width = .35
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fig = plt.figure()
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ax = fig.add_subplot(111)
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cached_rects = ax.bar(ind, cached_timings, width, color='r')
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uncached_rects = ax.bar(ind + width, uncached_timings, width, color='y')
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ax.set_title(title)
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ax.set_ylabel('Time (ms)')
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ax.set_xticks(ind + width)
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ax.set_xticklabels(heaps)
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ax.legend( (cached_rects[0], uncached_rects[0]), ('Cached', 'Uncached') )
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plt.show()
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def first_key_element(d):
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"Returns the first element found in the dict `d'."
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return d[d.keys()[0]]
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def compare_times_for_heaps(data, num_reps, pre_alloc_size, ion_op, text_only=False, target=None):
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timings = extract_all_data(data)[ion_op]
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# we need to sort the size strings, which are a few levels in
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# (through some keys that might or might not exist)
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first_heap_timing = first_key_element(timings)
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sizes_for_heap_timing = first_key_element(first_heap_timing)
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sorted_keys = sorted(
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sizes_for_heap_timing.keys(),
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key=lambda v: float(v.split('MB')[0])
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)
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for target_heap in timings.keys():
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heap_timings = timings[target_heap]
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print '%s times for %s (%d reps, %dMB pre-allocation)\n' \
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% (ion_op, target_heap, num_reps, pre_alloc_size)
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format_str = '%6s %10s %10s'
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print format_str % (
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'Size', 'Cached', 'Uncached',
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)
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for k in sorted_keys:
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print format_str % (
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k,
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('%5.2f' % heap_timings['cached'][k]) if 'cached' in heap_timings else 'NA',
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('%5.2f' % heap_timings['uncached'][k]) if 'uncached' in heap_timings else 'NA',
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)
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print '\n'
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if text_only:
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return
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fig = plt.figure()
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ax = fig.add_subplot(111)
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sorted_keys_numbers = [float(s.split('MB')[0]) for s in sorted_keys]
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shapes_cycler = cycle(["o", "v", "^" , "<", ">"])
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for target_heap in timings.keys():
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for caching in ('cached', 'uncached'):
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heap_timings = timings[target_heap]
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if caching in heap_timings:
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lbl = "%s (%s)" % (target_heap, caching)
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ax.plot(sorted_keys_numbers,
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[heap_timings[caching][k] for k in sorted_keys],
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next(shapes_cycler) + '-',
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label=lbl)
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title = ("Ion %s times\n" % ion_op) \
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+ ('%s' % ("Target: %s\n" % target) if target is not None else "") \
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+ ("(average of %d reps)" % num_reps)
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ax.set_ylabel("Time (ms)")
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ax.set_xlabel("Allocation size (MB)")
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ax.set_title(title)
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ax.legend(loc="upper left")
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plt.grid(True)
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plt.show()
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if __name__ == "__main__":
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target_sz = None
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parser = OptionParser()
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parser.add_option("-c", "--compare-heaps", action="store_true",
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help="Compare same-sized allocations across heaps")
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parser.add_option("-z", "--compare-alloc-sizes", action="store_true",
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help="Compare same-heap allocations across sizes")
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parser.add_option("-s", "--size", metavar="SIZE",
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help="Allocation size to plot (e.g. '8MB'). Only used with -c")
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# parser.add_option("-e", "--heap", metavar="HEAP",
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# help="Heap to plot (e.g. 'ION_CP_MM_HEAP_ID'), or 'ALL'. Only used with -z")
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parser.add_option("-t", "--text-only", action="store_true")
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parser.add_option("--target", help="Name of the device (used for plot titles)")
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parser.add_option("-o", "--ion-op",
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default="ION_IOC_ALLOC",
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help="Ion operation to display (currently supported: ION_IOC_ALLOC, ION_IOC_FREE)")
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(options, args) = parser.parse_args()
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if options.compare_heaps and not options.size:
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print "You must provide a size (-s) when comparing same-sized allocations across heaps (-c)"
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sys.exit(1)
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if not options.compare_heaps and not options.compare_alloc_sizes:
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print "You must specify either -c or -z"
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sys.exit(1)
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# snarf:
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data = [line for line in fileinput.input(args)]
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# get the num reps:
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repsline = [line for line in data if line.startswith(ST_PREFIX_NUM_REPS)][0]
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num_reps = int(repsline.split(' ')[-1])
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# get the pre-alloc size:
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prealloclines = [line for line in data if line.startswith(ST_PREFIX_PREALLOC_SIZE)]
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if len(prealloclines) > 0:
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pre_alloc_size = int(prealloclines[0].split(' ')[-1])
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else:
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pre_alloc_size = 0
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if options.compare_heaps:
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compare_heaps_for_a_size(data, options.size, num_reps,
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pre_alloc_size,
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options.ion_op,
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text_only=options.text_only,
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target=options.target)
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if options.compare_alloc_sizes:
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compare_times_for_heaps(data, num_reps,
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pre_alloc_size,
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options.ion_op,
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text_only=options.text_only,
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target=options.target)
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