Efficiently type alias Float128, Float64, and Float32

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I think you are measuring compile time:

D:\Temp>julia test2.jl 10000
BenchmarkTools.Trial: 23 samples with 1 evaluation.
 Range (min … max):  129.072 ms … 331.770 ms  ┊ GC (min … max):  0.00% … 36.07%
 Time  (median):     223.699 ms               ┊ GC (median):    39.80%
 Time  (mean ± σ):   220.838 ms ±  62.594 ms  ┊ GC (mean ± σ):  28.47% ± 23.30%

   ▃          █                      █                        ▃
  ▇█▁▁▁▁▇▁▁▁▁▁█▁▇▁▁▁▁▇▇▇▁▁▁▁▁▁▇▁▁▁▁▇▇█▇▁▁▁▁▁▁▁▁▁▇▇▁▁▁▁▁▁▁▁▁▁▇▁█ ▁
  129 ms           Histogram: frequency by time          332 ms <

 Memory estimate: 762.94 MiB, allocs estimate: 2.

D:\Temp>julia test1.jl Float64 10000
BenchmarkTools.Trial: 23 samples with 1 evaluation.
 Range (min … max):  123.081 ms … 319.950 ms  ┊ GC (min … max):  0.00% … 43.44%
 Time  (median):     203.366 ms               ┊ GC (median):    32.62%
 Time  (mean ± σ):   217.781 ms ±  63.084 ms  ┊ GC (mean ± σ):  28.12% ± 23.13%

                                     █                       ▃
  ▇▁▁▇▇▇▁▁▁▁▇▁▁▇▁▇▁▁▁▇▇▇▇▁▇▁▁▁▁▁▁▇▁▁▁█▇▁▁▁▁▁▁▁▁▁▁▁▁▁▁▇▇▇▁▁▁▁▁█▇ ▁
  123 ms           Histogram: frequency by time          320 ms <

 Memory estimate: 762.94 MiB, allocs estimate: 4.

with nearly same timings.

Julia 1.7.1 , THIS MAY BE IMPORTANT HERE!

with
test1.jl:

using BenchmarkTools

FloatA = eval(Meta.parse(ARGS[1]));
n=parse(Int,ARGS[2])

function init_a(n::Integer)::Matrix{FloatA}
    a = zeros(FloatA, n, n)
    #Initialize a
    return a
end

b = @benchmark init_a(n)
io = IOBuffer()
show(io, "text/plain", b)
s = String(take!(io))
println(s)

and test2.jl:

using BenchmarkTools

#FloatA = eval(Meta.parse(ARGS[1]));
n=parse(Int,ARGS[1])

function init_a(n::Integer)::Matrix{Float64}
    a = zeros(Float64, n, n)
    #Initialize a
    return a
end

b = @benchmark init_a(n)
io = IOBuffer()
show(io, "text/plain", b)
s = String(take!(io))
println(s)
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