On Sunday, 17 May 2020 at 09:41:55 UTC, Johan wrote:
On Sunday, 17 May 2020 at 03:30:57 UTC, Adnan wrote:
Hello, I am trying to examine what causes my similar D
solution to lag behind performance.
In the link, they don't have ldc or gdc but according to my
machine, the dmd generated code isn'
On Sunday, 17 May 2020 at 11:39:30 UTC, kinke wrote:
As a side note, using jagged arrays for multiple dimensions
should probably be avoided whenever you can.
By jagged array, do you mean vector of vectors? What would be an
alternative?
On Sunday, 17 May 2020 at 11:39:30 UTC, kinke wrote:
DMD v2.091:
* dmd -m64 -O -release -boundscheck=off -run ..\speed.d aa bbc:
~11 μs
I forgot `-inline` for DMD; that reduces the speed, yielding ~16
μs.
On Sunday, 17 May 2020 at 03:30:57 UTC, Adnan wrote:
In my machine, if you feed "aa" and "bbc" to the function, ldc
generated code takes around 400 microseconds. I don't have an
access to gdc in my machine.
https://imgshare.io/image/NN8Xmp
Full code:
D : https://run.dlang.io/is/vLj7BC
On Sunday, 17 May 2020 at 03:30:57 UTC, Adnan wrote:
Hello, I am trying to examine what causes my similar D solution
to lag behind performance.
In the link, they don't have ldc or gdc but according to my
machine, the dmd generated code isn't really far behind ldc
generated code.
https://img
Hello, I am trying to examine what causes my similar D solution
to lag behind performance.
In the link, they don't have ldc or gdc but according to my
machine, the dmd generated code isn't really far behind ldc
generated code.
So here is the actual code:
ulong levenshteinEditDistance(T)(in
