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Update benchmark and README
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README.md

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@@ -35,7 +35,7 @@ See the table below for a runtime comparison of the variants that has been creat
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> [!TIP]
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> If the table is hard to read on the landing page, you might wanna read the README directly here: [click](README.md#performance)
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| variant | method | runtime (internal) | factor | runtime (total) | factor |
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| variant | method | runtime (calculate) | factor | runtime (total) | factor |
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|--------------|-------------|----------------------|---------------------|----------------------|---------------------|
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| reference | Gauß-Seidel | (0.5626 ± 0.0063) s | (1.0000 ± 0.0159) | (0.5640 ± 0.0061) s | (1.0000 ± 0.0154) |
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| reference | Jacobi | (0.4921 ± 0.0032) s | (1.0000 ± 0.0091) | (0.4939 ± 0.0031) s | (1.0000 ± 0.0089) |
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For all benchmarks, the arguments `1 {1,2} 100 2 2 100` were used. Therefore, this only serves to give you a rough overview.
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`runtime_internal` shows the runtime that partdiff measured itself (the `Calculation time` field in the output) and `runtime_total` shows the runtime measured via `time.perf_counter()`.
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`runtime (calculate)` shows the runtime that partdiff measured itself (the `Calculation time` field in the output) and `runtime (total)` shows the runtime measured via `time.perf_counter()`.
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All Python implementations below have a larger runtime in total than the reference implementation.
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Since all of the startup code (arg-parsing, matrix initialization) is written in a pythonic and straightforward way, this is not surprising.

benchmark/make_table.py

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@@ -104,7 +104,7 @@ def format_ufloat_from_list(x, unit: str = None) -> str:
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df.columns = (
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"variant",
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"method",
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"runtime (internal)",
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"runtime (calculate)",
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"factor1",
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"runtime (total)",
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"factor2",

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