msparser 1.4

Valgrind massif.out parser

A parser for Valgrind massif.out files.

The msparser module offers a simple interface to parse the Valgrind massif.out file format, i.e. data files produced by the valgrind heap profiler and should be compatible with Python 2.5 and up (including 3.x and pypy).

How do I use it?

Import the module

As usual, import the module:

>>> import msparser

Parse a massif.out file

To extract the data from a massif.out file, you simply have to give its path to the parse_file function:

>>> data = msparser.parse_file('massif.out')

You could also use the msparser.parse function directly with a file descriptor.

Understand the data

The parsed data is returned as a dictionary which follow closely the massif.out format. It looks like this:

>>> from pprint import pprint
>>> pprint(data, depth=1)
{'cmd': './a.out',
 'desc': '--time-unit=ms',
 'detailed_snapshots_index': [...],
 'peak_snapshot_index': 16,
 'snapshots': [...],
 'time_unit': 'ms'}

The detailed_snapshots_index and peak_snapshot_index fields allow efficient localisation of the detailled and peak snapshots in the snapshots list. For example, to retrieve the peak snapshot from the snapshots list, we could do:

>>> peak_index = data['peak_snapshot_index']
>>> peak_snapshot = data['snapshots'][peak_index]

The snapshots list stores dictionaries representing each snapshot data:

>>> second_snapshot = data['snapshots'][1]
>>> pprint(second_snapshot)
{'heap_tree': None,
 'id': 1,
 'mem_heap': 1000,
 'mem_heap_extra': 8,
 'mem_stack': 0,
 'time': 183}

If the snapshot is detailled, the heap_tree field, instead of being None, will store a heap tree:

>>> peak_heap_tree = peak_snapshot['heap_tree']
>>> pprint(peak_heap_tree, depth=3)
{'children': [{'children': [...], 'details': {...}, 'nbytes': 12000},
              {'children': [], 'details': {...}, 'nbytes': 10000},
              {'children': [...], 'details': {...}, 'nbytes': 8000},
              {'children': [...], 'details': {...}, 'nbytes': 2000}],
 'details': None,
 'nbytes': 32000}

On the root node, the details field is always None, but on the children nodes it’s a dictionary which looks like this:

>>> first_child = peak_snapshot['heap_tree']['children'][0]
>>> pprint(first_child['details'], width=1)
{'address': '0x8048404',
 'file': 'prog.c',
 'function': 'h',
 'line': 4}

Obviously, if the node is below the massif threshold, the details field will be None.

Putting It All Together

From this data structure, it’s very easy to write a procedure that produce a data table ready for Gnuplot consumption:

print("# valgrind --tool=massif", data['desc'], data['cmd'])
print("# id", "time", "heap", "extra", "total", "stack", sep='\t')
for snapshot in data['snapshots']:
    id = snapshot['id']
    time = snapshot['time']
    heap = snapshot['mem_heap']
    extra = snapshot['mem_heap_extra']
    total = heap + extra
    stack = snapshot['mem_stack']
    print('  '+str(id), time, heap, extra, total, stack, sep='\t')

The output should looks like this:

# valgrind --tool=massif --time-unit=ms ./a.out
# id    time    heap    extra   total   stack
  0     0       0       0       0       0
  1     183     1000    8       1008    0
  2     184     2000    16      2016    0
  3     184     3000    24      3024    0
  4     184     4000    32      4032    0
  5     184     5000    40      5040    0
  6     184     6000    48      6048    0
  7     184     7000    56      7056    0
  8     184     8000    64      8064    0
  9     184     9000    72      9072    0

Tests

To run msparser’s test suite:

$ python msparser_test.py --verbose

The current build status on travis: http://travis-ci.org/#!/MathieuTurcotte/msparser

License

This code is free to use under the terms of the MIT license.