On Friday, 24 April 2015 at 09:18:48 UTC, Chris wrote:
On Thursday, 23 April 2015 at 14:40:58 UTC, Shammah Chancellor
wrote:
So, I was tooling around with one of the benchmarks I saw
listed on twitter:
https://github.com/kostya/benchmarks/tree/master/brainfuck
This D benchmark spends most of it's time on AA lookups.
Discussions about the implementation aside, I am curious as to
why the D AA is so slow even after calling AA.rehash.
I took a look at the Nim tables implementation, and it looks
that they use power-of-two vectors and use the property of
primitive roots modulo n to get the next value in the case of
a collision:
```
proc nextTry(h, maxHash: THash): THash {.inline.} =
result = ((5 * h) + 1) and maxHash
```
So, my questions:
1) What are the general purpose performance implications of
something like this?
2) I saw a thread awhile ago where someone had posted an
updated implementation of the AA with benchmarks and showed
that it was substantially faster in all cases. I can't find
the thread again -- what happened with this work?
-Shammah
Interesting. I noticed while doing some benchmarking that
looking up data that is stored in an AA is slower than
generating the data on the fly. I was really surprised.
AA lookup is (more often than not) slightly slower than (or at
least as fast as) generating the data on demand:
import std.datetime : benchmark, Duration;
import std.string : format;
import std.conv : to;
import std.stdio : writeln;
enum {
string[string] words = [
"Data1":"Blah",
"Data2":"Blub",
],
string[] letters = ["B", "l", "a", "h"]
}
void main() {
words.rehash();
auto result = benchmark!(lookup, make)(100_000);
writeln(to!Duration(result[0]));
writeln(to!Duration(result[1]));
}
string lookup() {
auto blah = words["Data1"];
return blah;
}
string make() {
string res;
foreach (c; letters)
res ~= c;
return res;
}
dmd v2.067.0 -release -O -boundscheck=off -inline
