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Simpletuner

Tool for running Combined Elimination against custom projects. The tool was originally created by Maxim Blinov of Embecosm.

The project is made freely available under the GPL 3.0 license (for code), and the Creative Commons Attribution-ShareAlike 4.0 International license (for documentation).

Quickstart

Simpletuner comes pre-prepared with two example projects to optimize:

  • A simple example project (invoked with --context ExampleWorkerContext)
  • The ChipsAlliance SweRV EH1 verilated core (--context SweRVWorkerContext)

Running Simpletuner on native machine

Running Simpletuner is a two-step process:

1) Generate the flags file the Combined Elimination will use:

$ ./gen-flags.py --base-opt O2 --cc $(which gcc) > config/my-flag-set.json
  • --base-opt O2: Use the -O2 optimisation flag as the "base" for the configuration.
  • --cc: The C compiler to use.

2) Run Simpletuner, optimising the example project for size using the above flags:

$ ./simpletuner.py --context ExampleWorkerContext --benchmark size --config config/riscv.O2.json --cc $(which gcc) -j $(nproc)
  • --context ExampleWorkerContext: use the ExampleWorkerContext class implemented in context/ExampleWorkerContext.py.
  • --benchmark size: Optimise the flags for the size benchmark, the implementation of which is provided by the ExampleWorkerClass class. Note that this parameter will be specific to each context class, and context classes provide simpletuner with information of what benchmarks they support.
  • --config flags/riscv.O2.json: Use the provided configuration as the starting point for optimisation. config/riscv.O2.json is provided as part of this repository, however you can generate your own configuration if you wish.
  • --cc: The C compiler to use. Note that its the reponsibility of the context class to actually play nice and use this parameter. Nothing is stopping you from calling your own arbitrary C compiler from within the context class compile() method hook, but you should honour this parameter if you can.
  • -j: Number of threads to start. More = Better.

This will start a lengthy Combined Elimination run for the ExampleWorkerContext (with a very noisy log output) across nproc threads for the native system compiler.

Live output viewing

To view the live output, look into the workspace/ directory (which should be created when running simpletuner for the first time), and look into the latest directory. Every time you run ./simpletuner, the script will generate a timestamped directory under the workspace/ directory which will contain all the data and output for the current invocation. Typically it might look something like this:

simpletuner/workspace/20220204-150006-QfaH$ ls
0  7                        iteration.13  iteration.2   iteration.7
1  global_leaderboard.live  iteration.14  iteration.20  iteration.8
2  iteration.0              iteration.15  iteration.21  iteration.9
3  iteration.1              iteration.16  iteration.3   log.txt
4  iteration.10             iteration.17  iteration.4
5  iteration.11             iteration.18  iteration.5
6  iteration.12             iteration.19  iteration.6
  • iteration.N: These files are iterations of the combined elimination process.
  • global_leaderboard.live: This file is updated live during the process, letting you inspect what the current best set of flags are.
  • 0/, 1/, ..., n/: These are worker context directories, where the worker context actually runs the benchmark.
  • log.txt: Huge log with all of the combined elimination process output

SweRV

Running the ChipsAlliance SweRV EH1 core is a bit more involved. the eh1/ directory contains a Makefile which does the following:

  1. Fetch verilator source from GitHub
  2. Builds verilator and installs into the same directory
  3. Fetch the SweRV source from GitHub
  4. Build the SweRV core from source
  5. Package the whole directory into a eh1.tar.gz

By running $ make all in the eh1/ directory, the Makefile should do the above and finally generate the file eh1.tar.gz.

You won't have to generate the flags file for this project - you can just use the config/riscv.O2.json flag set.

Make sure that you have a riscv32-unknown-elf-gcc compiler in your path, (you can download an upstream riscv32 compiler from here), and invoke the simpletuner script as follows:

SWERV_SOURCE_TAR=$(realpath eh1/eh1.tar.gz) ./simpletuner.py -j 8 --context SweRVWorkerContext --benchmark execute --config config/riscv.O2.json --cc [path to riscv32-unknown-elf-gcc]

Creating custom worker contexts

A "Worker context" is simply a class within the context/ that implements certain methods required by the Simpletuner driver.

Simpletuner comes pre-included with two worker contexts:

context
├── ExampleWorkerContext.py
└── SweRVWorkerContext.py

The ExampleWorkerContext.py file provides a very simple worker context that shows how to implement the callbacks neccesary for Simpletuner to function. Below is a brief summary of the class methods that you'll need to implement, and when Simpletuner will call them.

class MyWorkerContext:
    # Return the "type" of benchmark your Worker supports.
    # This information will be used by the Simpletuner driver
    # to check the user-supplied --benchmark flag.
    @staticmethod
    def get_available_benchmark_types() -> list:
        return ["execution", "size"];

    # Simpletuner will call this function when it first instantiates the class.
    # `idx` is the numeric index of the current thread,
    # `workspace` is the absolute path to a workspace created for the class,
    # `cc` contains the absolute path to the C compiler,
    # `benchmark_type` contains the user-provided `--benchmark` argument. This
    #      is useful when you want to run either for size, execution speed, or
    #      anything else.
    def __init__(self, idx, workspace, cc, benchmark_type):

    # Initialise workspace, whatever that may be.
    # Simpletuner will call this function after it has created your directory and called
    # your `__init__` function. The `workspace` parameter provided earlier in
    # `__init__` is intended to be used here.
    def init_workspace(self):

    # Simpletuner will call this function to compare scores.
    # Return `True` if score `x` is "better" than score `y`.
    def better(self, x, y) -> float:

    # Simpletuner will call this function when your `benchmark` or `compile` step fails
    # for any reason. You want to return numerically the worst possible score, usually "ininity".
    def worst_possible_result(self) -> float:
        return float('inf');

    # Simpletuner will call this function right before it calls your `benchmark` function.
    def compile(self, flags) -> CompileResult:

    # Simpletuner will call this to run your benchmark.
    def benchmark(self):