I have just found that adding the following type annotation solves the
problem:
s3 = string("x = Any['0'", join([string(", ", i) for i in 1:256]), "]")
but I do not understand exactly why (it seems that the core reason is how
Julia handles map on tuples but I am not sure why adding Any annotation
changes the situation).
Thank you,
Bogumił
On Friday, April 15, 2016 at 9:44:58 PM UTC+2, bogumil....@gmail.com wrote:
>
> Could someone help me to understand why the following code works slowly
> and how to make it run faster?
>
> function run()
> # this is fast
> s1 = string("x = [0", join([string(", ", i) for i in 1:256]), "]")
> p1 = parse(s1)
> @time eval(p1)
> @time eval(p1)
>
> # here starts the slow part
> s2 = string("x = ['0'", join([string(", ", i) for i in 1:128]), "]")
> p2 = parse(s2)
> @time eval(p2)
> @time eval(p2)
>
> s3 = string("x = ['0'", join([string(", ", i) for i in 1:256]), "]")
> p3 = parse(s3)
> @time eval(p3)
> @time eval(p3)
>
> s4 = string("x = ['0'", join([string(", ", i) for i in 1:192]), "]")
> p4 = parse(s4)
> @time eval(p4)
> @time eval(p4)
> println("done")
> end
>
> run()
>
> I understand that arrays of Any would evaluate slower than arrays of
> specified type but there seems to be a qualitative difference in how they
> are handled. It seems that the problem is with compilation of the code as
> after we run eval(p3) then consecutive run is fast and eval(p4) is also
> fast.
>
> The comparable code in Python:
>
> exec("x = [0"+"".join([", " + str(i) for i in range(1, 513)]) + "]")
> exec("x = ['0'"+"".join([", " + str(i) for i in range(1, 513)]) + "]")
>
> runs fast in both cases.
>
> The problem here is an artificial example. The actual situation occurred
> when I was trying to include code that constructed a long vector of
> strings consisting of ASCIIString and UTF8String entries (some of the
> string literals included code contained only ASCII characters and some did
> not eg. x = ["a", "ą"], the actual vector was much longer).
>
> Thank you,
> Bogumił
>
