Simple performance question from a newcomer
Daniel Kozak via Digitalmars-d-learn
digitalmars-d-learn at puremagic.com
Sun Feb 21 07:56:40 PST 2016
You can use -profile to see what is causing it.
Num Tree Func Per
Calls Time Time Call
23000000 550799875 550243765 23 pure nothrow @nogc
@safe double std.algorithm.iteration.sumPairwise!(double,
std.experimental.ndslice.slice.Slice!(1uL, std.range.iota!(double,
double, double).iota(double, double,
double).Result).Slice).sumPairwise(std.experimental.ndslice.slice.Slice!(1uL,
std.range.iota!(double, double, double).iota(double, double,
double).Result).Slice)
Dne 21.2.2016 v 15:32 dextorious via Digitalmars-d-learn napsal(a):
> I've been vaguely aware of D for many years, but the recent addition
> of std.experimental.ndslice finally inspired me to give it a try,
> since my main expertise lies in the domain of scientific computing and
> I primarily use Python/Julia/C++, where multidimensional arrays can be
> handled with a great deal of expressiveness and flexibility. Before
> writing anything serious, I wanted to get a sense for the kind of code
> I would have to write to get the best performance for numerical
> calculations, so I wrote a trivial summation benchmark. The following
> code gave me slightly surprising results:
>
> import std.stdio;
> import std.array : array;
> import std.algorithm;
> import std.datetime;
> import std.range;
> import std.experimental.ndslice;
>
> void main() {
> int N = 1000;
> int Q = 20;
> int times = 1_000;
> double[] res1 = uninitializedArray!(double[])(N);
> double[] res2 = uninitializedArray!(double[])(N);
> double[] res3 = uninitializedArray!(double[])(N);
> auto f = iota(0.0, 1.0, 1.0 / Q / N).sliced(N, Q);
> StopWatch sw;
> double t0, t1, t2;
> sw.start();
> foreach (unused; 0..times) {
> for (int i=0; i<N; ++i) {
> res1[i] = sumtest1(f[i]);
> }
> }
> sw.stop();
> t0 = sw.peek().msecs;
> sw.reset();
> sw.start();
> foreach (unused; 0..times) {
> for (int i=0; i<N; ++i) {
> res2[i] = sumtest2(f[i]);
> }
> }
> sw.stop();
> t1 = sw.peek().msecs;
> sw.reset();
> sw.start();
> foreach (unused; 0..times) {
> sumtest3(f, res3, N, Q);
> }
> t2 = sw.peek().msecs;
> writeln(t0, " ms");
> writeln(t1, " ms");
> writeln(t2, " ms");
> assert( res1 == res2 );
> assert( res2 == res3 );
> }
>
> auto sumtest1(Range)(Range range) @safe pure nothrow @nogc {
> return range.sum;
> }
>
> auto sumtest2(Range)(Range f) @safe pure nothrow @nogc {
> double retval = 0.0;
> foreach (double f_ ; f) {
> retval += f_;
> }
> return retval;
> }
>
> auto sumtest3(Range)(Range f, double[] retval, double N, double Q)
> @safe pure nothrow @nogc {
> for (int i=0; i<N; ++i) {
> for (int j=1; j<Q; ++j) {
> retval[i] += f[i,j];
> }
> }
> }
>
> When I compiled it using dmd -release -inline -O -noboundscheck
> ../src/main.d, I got the following timings:
> 1268 ms
> 312 ms
> 271 ms
>
> I had heard while reading up on the language that in D explicit loops
> are generally frowned upon and not necessary for the usual performance
> reasons. Nevertheless, the two explicit loop functions gave me an
> improvement by a factor of 4+. Furthermore, the difference between
> sumtest2 and sumtest3 seems to indicate that function calls have a
> significant overhead. I also tried using f.reduce!((a, b) => a + b)
> instead of f.sum in sumtest1, but that yielded even worse performance.
> I did not try the GDC/LDC compilers yet, since they don't seem to be
> up to date on the standard library and don't include the ndslice
> package last I checked.
>
> Now, seeing as how my experience writing D is literally a few hours,
> is there anything I did blatantly wrong? Did I miss any optimizations?
> Most importantly, can the elegant operator chaining style be generally
> made as fast as the explicit loops we've all been writing for decades?
More information about the Digitalmars-d-learn
mailing list