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apr19.md

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@@ -58,9 +58,9 @@ iteration of the function.
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The tool is designed to have minimal external dependencies and can be run
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against a test compiler binary without additional packages. The experimental
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method of running for multiple embedded iterations is also used by Jane
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Street’s `core_bench`<sup>[3] and Haskell’s criterion[^4][3] and Haskell’s criterion[^4]</sup>.
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Street’s `core_bench`<sup>[3][3]</sup> and Haskell’s criterion<sup>[4][4]</sup>.
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#### operf-macro<sup>[5] and sandmark[^6][5] and sandmark[^6]</sup>
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#### operf-macro<sup>[5][5]</sup> and sandmark<sup>[6][6]</sup>
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Operf-macro provides a framework to define and run macro-benchmarks. The
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benchmarks themselves are OPAM packages and the compiler versions are OPAM
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The Python community has continuous benchmarking both for their CPython<sup>[7][7]</sup> and
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PyPy<sup>[8][8]</sup> runtimes. The benchmarking data is collected by running the Python
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Performance Benchmark Suite<sup>[9]. The open-source web application Codespeed[^10][9]. The open-source web application Codespeed[^10]</sup>
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Performance Benchmark Suite<sup>[9][9]</sup>. The open-source web application Codespeed<sup>[10][10]</sup>
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provides a front end to navigate and visualize the results. Codespeed is
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written in Python on Django and provides views into the results via a revision
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table, a timeline by benchmark and comparison over all benchmarks between

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