Valgrind massif.out parser
Project description
A parser for Valgrind massif.out files.
Massif Parser
- Author:
Mathieu Turcotte
The msparser module offers a simple interface to parse the Valgrind massif.out file format, i.e. data files produced the valgrind heap profiler.
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
Changelog
1.0 [2011-01-11]
initial release
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