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Wu wei (non-effort) is a benchmarking toolkit developed in the Sable Lab at McGill University with the objective of simplifying the study of languages and tools used for numerical computing.
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README.md

Wu-Wei (無爲) Benchmarking Toolkit

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Wu-Wei (non-effort) is a benchmarking toolkit developed in the Sable Lab at McGill University with the objective of simplifying the study of the performance of programming languages implementations and tools.

We aim to make the toolkit and the benchmark suites built with it:

  1. Consistent and Correct by supporting correctness checks for every language implementation of benchmarks that automatically ensure that the computation result of the benchmarks are consistent across all language implementations and correct with regard to the algorithm for known inputs;
  2. Extensible across numerical languages, benchmarks, compilers, run-time environments;
  3. Friendly to language implementation research by automating all tasks for compiler and virtual-machine research and encouraging a writing style for benchmarks that factors the core computation from the runners to minimize the non-core functions necessary to validate the output of compilers;
  4. Easy to use by automating the deployment of benchmarks, their test on virtual (web browser and others) and native platforms, as well as the gathering and reporting of relative performance data;
  5. Fast by making the setup (data generation and loading) and teardown as quick as possible so that most of the time is spent in the core computation in every language;
  6. Small by minimizing the amount of data needed to use the suite;
  7. Simple by minimizing the amount of external dependencies and tools required to run the suite;

Dependencies

Although we tried our best to minimize external dependencies, the toolkit still depends on the following external tools:

  1. Node.js
  2. Python

Individual artifacts may have more dependencies. Refer to their documentation for more details.

Getting Started

Please read the handbook for more details on how to use it.

Copyright and License

Copyright (c) 2016, Erick Lavoie, Hanfeng Chen

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