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collaborative tuning of GCC optimization heuristic

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:

Here is a brief note how this framework for crowdtuning compiler optimization heuristics works (for more details, please see you just install a small Android app ( or python-based Collective Knowledge framework ( 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 Fursin, CTO, dividiti, UK

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