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docs/src/index.md

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@@ -17,19 +17,22 @@ julia> using BenchmarkTools
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# timing results. Note that each sample can require multiple evaluations
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# benchmark kernel evaluations. See the BenchmarkTools manual for details.
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julia> @benchmark sort(data) setup=(data=rand(10))
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BenchmarkTools.Trial:
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10000 samples with 968 evaulations took a median time of 90.902 ns (0.00% GC)
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Time (mean ± σ): 94.936 ns ± 47.797 ns (GC: 2.78% ± 5.03%)
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Range (min max): 77.655 ns 954.823 ns (GC: 0.00% 87.94%)
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▁▃▅▆▇█▇▆▅▂▁
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▂▂▃▃▄▅▆▇███████████▇▆▄▄▃▃▂▂▂▂▂▂▂▂▂▂▂▁▂▁▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂
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77.7 ns Histogram: frequency by time 137 ns
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Memory estimate: 160 bytes, allocs estimate: 1.
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┌ Trial:
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│ min 46.954 ns, median 59.475 ns, mean 61.344 ns, 99ᵗʰ 80.203 ns
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1 allocation, 144 bytes
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│ GC time: mean 1.092 ns (1.78%), max 537.224 ns (88.05%)
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│ ◔ ◑ *
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│ ▂▄▅▇▇█▆▆▄▂
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│ ▁▂▁▁▂▂▂▂▁▂▂▁▁▂▂▂▂▃▃▅▆████████████▇▅▅▃▃▃▃▃▃▃▃▃▃▃▃▂▃▂▂▂▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▁▂▁ ▄
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46 ns 10_000 samples, each 985 evaluations 81 ns +
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```
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For quick sanity checks, one can use the [`@btime` macro](https://github.com/JuliaCI/BenchmarkTools.jl/blob/master/doc/manual.md#benchmarking-basics), which is a convenience wrapper around `@benchmark` whose output is analogous to Julia's built-in [`@time` macro](https://docs.julialang.org/en/v1/base/base/#Base.@time):
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In the histogram of sample times, the median is marked `` and the mean `*`; on most displays
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these will be indicaded by color too (but not in the documentation).
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For quick sanity checks, one can use the [`@btime` macro](https://github.com/JuliaCI/BenchmarkTools.jl/blob/master/doc/manual.md#benchmarking-basics), which is a convenience wrapper around `@benchmark` whose output is analogous to Julia's built-in [`@time` macro](https://docs.julialang.org/en/v1/base/base/#Base.@time).
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This prints only the **minimum** time, which is often the most informative for fast-running
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calculations:
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```julia
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julia> @btime sin(x) setup=(x=rand())

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