朱莉娅 1.0.0文档提供一般提示。
它还建议不要使用 @time 宏：
对于更严格的基准测试，请考虑 BenchmarkTools.jl 包，其中多次评估函数以减少噪音。
它们在使用中的比较如何？是否值得麻烦地使用“基础”Julia 中没有的东西？
从统计角度来看，@benchmark 比@time 好很多
TL;DR 基准工具@benchmark宏是一个很棒的微基准工具。
使用@time谨慎使用宏，不要认真对待第一次运行。
这个简单的示例说明了用途和差异：
julia> # Fresh Julia 1.0.0 REPL
julia> # Add BenchmarkTools package using ] key package manager
(v1.0) pkg> add BenchmarkTools
julia> # Press backspace key to get back to Julia REPL
# Load BenchmarkTools package into current REPL
julia> using BenchmarkTools
julia> # Definine a function with a known elapsed time
julia> f(n) = sleep(n) # n is in seconds
f (generic function with 1 method)
# Expect just over 500 ms for elapsed time
julia> @benchmark f(0.5)
BenchmarkTools.Trial:
memory estimate: 192 bytes
allocs estimate: 5
--------------
minimum time: 501.825 ms (0.00% GC)
median time: 507.386 ms (0.00% GC)
mean time: 508.069 ms (0.00% GC)
maximum time: 514.496 ms (0.00% GC)
--------------
samples: 10
evals/sample: 1
julia> # Try second run to compare consistency
julia> # Note the very close consistency in ms for both median and mean times
julia> @benchmark f(0.5)
BenchmarkTools.Trial:
memory estimate: 192 bytes
allocs estimate: 5
--------------
minimum time: 502.603 ms (0.00% GC)
median time: 508.716 ms (0.00% GC)
mean time: 508.619 ms (0.00% GC)
maximum time: 515.602 ms (0.00% GC)
--------------
samples: 10
evals/sample: 1
julia> # Define the same function with new name for @time macro tests
julia> g(n) = sleep(n)
g (generic function with 1 method)
# First run suffers from compilation time, so 518 ms
julia> @time sleep(0.5)
0.517897 seconds (83 allocations: 5.813 KiB)
# Second run drops to 502 ms, 16 ms drop
julia> @time sleep(0.5)
0.502038 seconds (9 allocations: 352 bytes)
# Third run similar to second
julia> @time sleep(0.5)
0.503606 seconds (9 allocations: 352 bytes)
# Fourth run increases over second by about 13 ms
julia> @time sleep(0.5)
0.514629 seconds (9 allocations: 352 bytes)
这个简单的例子说明了使用@benchmark宏观和谨慎@time应采取宏观结果。
是的，不厌其烦地使用是值得的@benchmark macro.
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