The Julia 1.0.0 documentation provides general tips.
It also suggests that instead of using the @time macro:
For more serious benchmarking, consider the BenchmarkTools.jl package which among other things evaluates the function multiple times in order to reduce noise.
How do they compare in use and is it worth the trouble to use something not in "base" Julia?
From a statistical point of view, @benchmark is much better than @time
TL;DR The BenchmarkTools @benchmark macro is a great micro-benchmark tool.
Use the @time macro with caution and don't take the first run seriously.
This simple example illustrates use and differences:
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)
This simple example illustrates how easy it is to use the @benchmark macro and the caution with which the @time macro results should be taken.
Yes, it is worth the trouble to use the @benchmark macro.