Dear colleagues,
If it's of interest, we have released a new version of our open-source
framework to share compiler optimization knowledge across diverse
workloads and hardware. We would like to thank all the volunteers who
ran this framework and shared some results for GCC 4.9 .. 6.0 in the
public repository here: http://cTuning.org/crowdtuning-results-gcc
Here is a brief note how this framework for crowdtuning compiler
optimization heuristics works (for more details, please see
https://github.com/ctuning/ck/wiki/Crowdsource_Experiments): you just
install a small Android app
(https://play.google.com/store/apps/details?id=openscience.crowdsource.experiments)
or python-based Collective Knowledge framework
(http://github.com/ctuning/ck). This program sends system properties to
a public server. The server compiles a random shared workload using some
flag combinations that have been found to work well on similar machines,
as well as some new random ones. The client executes the compiled
workload several times to account for variability etc, and sends the
results back to the server.
If a combination of compiler flags is found that improves performance
over the combinations found so far, it gets reduced (by removing flags
that do now affect the performance) and uploaded to a public repository.
Importantly, if a combination significantly degrades performance for a
particular workload, it gets recorded as well. This potentially points
to a problem with optimization heuristics for a particular target, which
may be worth investigating and improving.
At the moment, only global GCC compiler flags are exposed for
collaborative optimization. Longer term, it can be useful to cover
finer-grain transformation decisions (vectorization, unrolling, etc) via
plugin interface. Please, note that this is a prototype framework and
much more can be done! Please get in touch if you are interested to know
more or contribute!
Take care,
Grigori
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Grigori Fursin, CTO, dividiti, UK
