In high performance computing, performance is kind of a big deal. And the first step in performance analysis and performance improvement is profiling.

High performance computing almost always entails some form of parallelism. And parallel programs are plain hard. They're harder to write, harder to debug, and harder to profile.


gprof is pretty great. Just compile your code with -pg, and -g,

$ gcc -pg -g -O0 hello.c bye.c -o hibye.exe

run your code as usual,

$ ./hibye.exe

and you'll see gmon.out. Now,

$ gprof hibye.exe gmon.out

should summarize the performance of your code. Beware, gprof will not pick up on any calls to shared library functions. OK, that's a downer, and there's lots more. But it's easy to use, and gives me quick results. With the legacy code I work with, where there are no shared library calls, gprof is pretty awesome.

gprof + MPI

gprof isn't designed to work with MPI code. But, as is generally the case with these things, it's possible with sufficient abuse:

First, set the environment variable GMON_OUT_PREFIX:

$ export GMON_OUT_PREFIX=gmon.out-

Then, the usual business:

$ mpicc -pg -g -O0 hello.c bye.c -o hibye.exe
$ mpiexec -n 32 hibye.exe

You should see 32 (or however many processes) files, with names gmon.out-<pid>. This is an undocumented feature of glibc, and it really shouldn't be - it's massively useful.

Now you have a separate gmon.out file for every MPI process. Awesome. Sum them:

$ gprof -s hibye.exe gmon.out-*

And use the resulting gmon.sum to generate gprof output:

$ gprof hibye.exe gmon.sum

Credit where it's due. Now, I haven't figured out how to replace the pid with the MPI rank - this could be exponentially more useful to some users. And the method mentioned in the source doesn't really seem to be working. But I'm sure this is possible with some ingenuity.


mpiP is a neat little tool for profiling MPI applications. In particular, it's extremely useful in figuring out how much your application is spending time communicating relative to computing.

The documentation for setting up and using mpiP is complete (good), but small (better). Once you have mpiP set up, profiling your code is as easy as linking it with the mpiP library and some other stuff it needs:

$ mpicc -g -O0 hello.c bye.c -o hibye.exe -lmpiP -liberty -lbfd -lunwind

Running your code (mpiexec) will produce mpiP output.

I've found that while gprof and mpiP are great tools that do different things, using them both gives me a very good idea of where my programs are spending time and where I should focus optimization efforts.