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yaTuner

yaTuner: yet another auto tuner for compilers.

Getting Started

A virtual environment can be created using make init and examples are placed at directory examples.

To tune a program, use yatuner -g <filename> to generate a basic template for the program. Then modify the the script for further use. More information about the tuning script is contained in the template.

Also, yatuner.utils includes tools that might be necessary for use, here is a brief summary:

Tool	Functionality
yatuner.utils.execute	Execute command
yatuner.utils.fetch_perf_stat	Get the result of perf stat of certain command
yatuner.utils.fetch_arch	fetch the architecture of the machine
yatuner.utils.fetch_gcc_version	fetch the gcc version
yatuner.utils.fetch_gcc_optimizers	fetch the gcc optimizers
yatuner.utils.fetch_gcc_parameters	fetch the gcc parameters
yatuner.utils.fetch_gcc_enabled_optimizers	fetch the gcc enabled optimizers of given options
yatuner.utils.fetch_size	fetch a file size

These tools can be used in the tuning script, see examples for details.

The tuning process and relative methods and their functionalities are listed as below.

Method	Functionality
yatuner.Tuner.initialize	Initialize workspace
yatuner.Tuner.test_run	Doing an initial test run
yatuner.Tuner.hypotest_optimizers	Hypothesis test for optimizers
yatuner.Tuner.hypotest_parameters	Hypothesis test for parameters
yatuner.Tuner.optimize	Tune parameters with Bayesian Optimization
yatuner.Tuner.optimize_linUCB	Tune parameters with LinUCB
yatuner.Tuner.run	Run final test and generate result
yatuner.Tuner.plot_data	Plot result in violin graph

Detailed documentation of yatuner.Tuner and yatuner.utils can be found in docs.

Module	Documentation
yatuner.Tuner	docs/yatuner.tuner.md
yatuner.utils	docs/yatuner.utils.md

Usage

1. Install yaTuner.
2. Auto-generate tuning script with yatuner -g <filename>.
3. Manually modify tuning script and add details.
4. Run tuning script with python <filename>.

Architecture

graph TB
subgraph User Interface
    O([yaTuner]) --> A
    A[(Auto-generated Tuning Script)] -- manually add details --> B[(Final Tuning Script)]
end
B == run ==> C
subgraph Tuning Process
    C(Auto-fetching compiler options) --> D(Hypothesis Test for Optimizers) 
    D --> E(Tuning Optimizers)
    E --> F(Hypothesis Test for Parameters)
    F --> G(Tuning Parameters)
    G -- run test and compare --> I[(Tuning Report)]
end
G -- store --> H[(Tuned Options)]
E -- store --> H
subgraph Tuner
    J([LinUCB Optimizer]) -.-> G
    K([Bayesian Optimizer]) -.-> G
    K -.-> E
end
subgraph Pre-defined Functions
    B == defines ==> L([comp])
    B == defines ==> M([run])
    B == defines ==> N([perf])
end
N -.-> J
L & M -.-> J & K

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License

yaTuner is licensed under Mulan PSL v2. See LICENSE for more details.

About

This is a project for the OS competition 2022, proj105 problem, see this for further information.