1) Interactive Compilation Interface aka ICI - a plugin based framework to expose or change various information and optimization decisions inside compilers at fine-grain level via external plugins. I originally developed it for Open64 and later collaborated with Zbigniew Chamski and colleagues from Google and Mozilla to make it a standard plugin framework for GCC.
2) Feature extractor developed by Mircea Namolaru from IBM as an ICI plugin to expose low-level program features at a function level (see available features here). It was also extended by Jeremy Singer (ft57–65).
However, since it was still taking really too long to train models (my PhD students, Yuriy Kashnikov and Abdul Memon, spent 5 months preparing experiments in 2010 for our MILEPOST GCC paper), we decided to crowdsource autotuning via a common repository across diverse hardware provided by volunteers and thus dramatically speed up training process. Accelerating training process and improving the diversity of a training set is the main practical reason why my autotuning frameworks use crowdtuning mode by default nowadays ;) …
You can also take advantage of integrated and cross-platform CK package manager which can prepare your workflow and install missing dependencies on Linux, Windows, MacOS and Android.
For example, see highest ranked artifact from CGO’17 shared as a customizable and portable CK workflow at GitHub.
To conclude my nostalgic overview of the MILEPOST project and CK ;) — MILEPOST GCC is now added to the CK as a unified workflow while taking advantage of a growing number of shared benchmarks, data sets, and optimization statistics (see CK GitHub repo).
I just didn’t have time to provide all the ML gluing, i.e. building models from all optimization statistics and features shared by the community at cKnowledge.org/repo . But it should be quite straightforward, so I hope our community will eventually help implement it. We are now particularly interested to check the prediction accuracy from different models (SVM, KNN, DNN, etc) or to find extra features which improve optimization prediction.