A small but useful tool for performance characterisation

[Richard Eisenberg]

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 at 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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