On January 4, 2020 8:51:07 PM EST, Richard Eisenberg <rae@richarde.dev> wrote:

Hi Ben,

This sounds great. Is there a place on the wiki to catalog tools like this?

Thanks for telling us about it!
Richard

On Jan 4, 2020, at 7:37 PM, Ben Gamari <ben@well-typed.com> wrote:

Hi everyone,

I have recently been doing a fair amount of performance characterisation
and have long wanted a convenient means of collecting GHC runtime
statistics for later analysis. For this I quickly developed a small
wrapper utility [1].

To see what it does, let's consider an example. Say we made a change to
GHC which we believe might affect the runtime performance of Program.hs.
We could quickly check this by running,

   $ ghc-before/_build/stage1/bin/ghc -O Program.hs
   $ ghc_perf.py -o before.json ./Program
   $ ghc-before/_build/stage1/bin/ghc -O Program.hs
   $ ghc_perf.py -o after.json ./Program

This will produce two files, before.json and after.json, which contain
the various runtime statistics emitted by +RTS -s --machine-readable.
These files are in the same format as is used by my nofib branch [2] and
therefore can be compared using `nofib-compare` from that branch.

In addition to being able to collect runtime metrics, ghc_perf is also
able to collect performance counters (on Linux only) using perf. For
instance,

   $ ghc_perf.py -o program.json \
       -e instructions,cycles,cache-misses ./Program

will produce program.json containing not only RTS statistics but also
event counts from the perf instructions, cycles, and cache-misses
events. Alternatively, passing simply `ghc_perf.py --perf` enables a
reasonable default set of events (namely instructions, cycles,
cache-misses, branches, and branch-misses).

Finally, ghc_perf can also handle repeated runs. For instance,

   $ ghc_perf.py -o program.json -r 5 --summarize \
        -e instructions,cycles,cache-misses ./Program

will run Program 5 times, emit all of the collected samples to
program.json, and produce a (very basic) statistical summary of what it
collected on stdout.

Note that there are a few possible TODOs that I've been considering:

* I chose JSON as the output format to accomodate structured data (e.g.
  capture experimental parameters in a structured way). However, in
  practice this choice has lead to significantly more inconvenience
  than I would like, especially given that so far I've only used the
  format to capture basic key/value pairs. Perhaps reverting to CSV
  would be preferable.

* It might be nice to also add support for cachegrind.

Anyways, I hope that others find this as useful as I have.

Cheers,

- Ben


[1] https://gitlab.haskell.org/bgamari/ghc-utils/blob/master/ghc_perf.py
[2] https://gitlab.haskell.org/ghc/nofib/merge_requests/24
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