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Universal, customizable and multi-objective software and hardware autotuning

Status

This is a relatively stable repository for universal, customizable, multi-dimensional, multi-objective SW/HW autotuning.

Prerequisites

Description

During many years of research on machine learning based autotuning we spent more time on data management then on innovation. At the end, we decided to provide a complete solution in CK where our plugin-based autotuning tools are combined with our repository and python or R-based machine learning plugins.

We are gradually moving, simplifying and extending autotuning from Collective Mind into new CK format! Since design and optimization spaces are very large, we are trying to make their exploration practical and scalable by combining autotuning, crowdsourcing, predictive analytics and run-time adaptation.

Modules from this repository will be used to unify:

  • program compilation and execution (with multiple data sets)
  • benchmarking
  • statistical analysis
  • plugin-based autotuning
  • automatic performance modeling
  • static and dynamic features extraction
  • machine learning to predict optimizations and run-time adaptation
  • reproducibility of experimental results

Publications

Authors

  • Grigori Fursin, cTuning foundation (France) / dividiti (UK)
  • Anton Lokhmotov, dividiti (UK)

License

  • BSD, 3-clause

Installation

ck pull repo:ck-autotuning

Modules with actions

choice - exploring choices in multi-dimensional spaces (customizing autotuning)

  • make - make next choice in multi-dimensional space
  • select_list - select from a list of choices in console mode

compiler - describing compilers and their optimization choices

  • extract_to_pipeline - prepare compiler flag choices for universal tuning via program pipeline (experimental workflow)

dataset - datasets

dataset.features - dataset features

  • convert_raw_rgb_image - convert raw RGB image to png (originally used for SLAM application tuning)
  • extract - extract data set features

pipeline - defining universal pipelines (experiment workflows)

  • autotune - perform autotuning of any available CK pipeline (workflow)
  • run - run a given pipeline (workflow) once
  • run_stat - run a given pipeline (workflow) N times and perform statistical analysis
  • setup - setup a given pipeline (workflow) for execution/autotuning

pipeline.cmd - demonstrating command line pipeline (for CMD-based autotuning)

  • pipeline - run universal command line pipeline (demo)

program - program compilation and execution workflow (pipeline)

  • autotune - universal, multi-objective, multi-dimensional software/hardware autotuning
  • clean - clean temporal files and directories of a given program
  • compile - compile a given program
  • pipeline - universal program compilation and execution pipeline (workflow)
  • run - run a given program
  • select_uoa - select program UOA in console mode

program.experiment.speedup - checking program speedups vs compiler flags vs data sets

  • describe - describe experiment
  • reproduce - reproduce experiment

program.species - classification of similar programs using machine learning or manually by the community

program.static.features - program semantic features