Is there a way to avoid Google Performance Tools listing files as "??:?", that is, failing to locate which file contains the function it is reporting on? How can I work out which library contains the function being called?
$ env LD_PRELOAD="/usr/lib/libprofiler.so.0" \
CPUPROFILE=output.prof python script.py
$ google-pprof --text --files /usr/bin/python output.prof
Using local file /usr/bin/python.
Using local file output.prof.
Removing _L_unlock_13 from all stack traces.
Total: 433 samples
362 83.6% 83.6% 362 83.6% dtrsm_ ??:?
58 13.4% 97.0% 58 13.4% dgemm_ ??:?
1 0.2% 97.2% 1 0.2% PyDict_GetItem /.../Objects/dictobject.c
1 0.2% 97.5% 1 0.2% PyParser_AddToken /.../Parser/parser.c
...
I am aiming to be able to profile the C code in a python package that has many compiled C extension modules. In the toy example above, what would I do to track down where "dtrsm_" is defined? If there are multiple loaded libraries that contain functions with that same name, is there any way to tell which version is being called?
C/C++ won't compile if the same pre-processed sourcefile (e.g. with #includes expanded) contains duplicate definitions for the same symbol. (Note that in the case of C++, symbols are mangled, according to compiler-specific schemes, to incorporate the argument signature so as to facilitate overloaded functions, which could not otherwise be differentiated.)
The linker is only concerned with unresolved symbols (so there ought be nothings preventing multiple libraries concurrently calling their own respective internally-defined functions with coincident names). If a file invokes a declared but undefined function, and multiple available libraries implement that symbol, then the linker is free to choose (say by precedence in a search-path) which version gets substituted in. (Incidentally, this is the same mechanism by which profilers such as gperftools or hpctoolkit are able to inject themselves and alter the normal behaviour of another application.)
Since different libraries are mapped to separate pages of memory, it ought to be possible to identify (from memory addresses) which library contains the executing version of a function. Indeed, the GNU debugger can identify the library that code is contained by, even when it fails to name a function.
$ gdb python
(gdb) run -c "from numpy import *; linalg.inv(random.random((1000,1000)))"
CTRL-C
(gdb) backtrace
#0 0x00007ffff5ba9df8 in dtrsm_ () from /usr/lib/libblas.so.3
...
#3 0x00007ffff420df83 in ?? () from /.../numpy/linalg/_umath_linalg.so
Linux (or rather the GNU C library) provides the "backtrace" call (for getting a list of pointers from the call stack), and the "backtrace_symbols" call for automatically converting each of those pointers to a descriptive string such as:
"/lib/x86_64-linux-gnu/libc.so.6(__libc_start_main+0xf5) [0x7fc429929ec5]"
Gperftools can (judging from a query on the github mirror) call the generic "backtrace", but instead of "backtrace_symbols" it "forks out to pprof to do the actual symbolizing". This is a fairly-epic perl script, and looks likely where the "??" comes from.
Crucially, google-pprof is trying to report on the source-file (and line-number) which defines the function, not the binary-file containing the machine-code (that is typically quoted in stack traces). It invokes the "nm" utility. On my system it appears (by running "nm -l -D") that libblas, unlike libc and the python binary, has been stripped of such debugging symbols (presumably for optimisation), explaining the result.
To answer the original question: the call-stack samples should definitively and explicitly specify which version is being called. These can probably be dumped using an option which was added in google-pprof several months ago, or (for time-intensive functions) can be roughly ascertained by manual resampling using gdb. (It's even conceivable that g-pprof can be adjusted to explicitly identify the binaries paths in its output summaries.) Alternatively one can run "nm" (and grep) on the candidate binaries/libraries (of which a short-list can be obtained by running "strings" on the profiler's raw output, among other methods). If the source is accessible (to grep) or the libraries are popular (on the web) then of course (and per Mike Dunlavey) it may be easiest to just query for the function name. In theory the "??:?" may be addressed by carefully recompiling the offending objects.