Re: sliced().array compatibility with parallel?
On Sunday, 10 January 2016 at 01:16:43 UTC, Ilya Yaroshenko wrote: On Saturday, 9 January 2016 at 23:20:00 UTC, Jay Norwood wrote: I'm playing around with win32, v2.069.2 dmd and "dip80-ndslice": "~>0.8.8". If I convert the 2D slice with .array(), should that first dimension then be compatible with parallel foreach? [...] Oh... there is no bug. means must be shared =) : shared double[1000] means; I'd say, if `shared` is required, but it compiles without, then it's still a bug.
Re: sliced().array compatibility with parallel?
On Sun, 2016-01-10 at 01:46 +, Jay Norwood via Digitalmars-d-learn
wrote:
>
[…]
> // processed non-parallel works ok
> foreach( dv; dv2){
> if(dv != dv){ // test for NaN
> return 1;
> }
> }
>
> // calculated parallel leaves out processing of many values
> foreach( dv; dvp){
> if(dv != dv){ // test for NaN
> return 1;
> }
> }
> return(0);
> }
I am not convinced these "Tests for NaN" actually test for NaN. I
believe you have to use isNan(dv).
--
Russel.
=
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41 Buckmaster Roadm: +44 7770 465 077 xmpp: rus...@winder.org.uk
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Re: sliced().array compatibility with parallel?
On Sunday, 10 January 2016 at 11:21:53 UTC, Marc Schütz wrote: I'd say, if `shared` is required, but it compiles without, then it's still a bug. Yeah, probably so. Interestingly, without 'shared' and using a simple assignment from a constant (means[i]= 1.0;), instead of assignment from the sum() evaluation, results in all the values being initialized, so not marking it shared doesn't protect it from being written from the other thread. Anyway, the shared declaration doesn't seem to slow the execution, and it does make sense to me that it should be marked shared.
Re: sliced().array compatibility with parallel?
On Sunday, 10 January 2016 at 03:23:14 UTC, Ilya wrote: I will add significantly faster pairwise summation based on SIMD instructions into the future std.las. --Ilya Wow! A lot of overhead in the debug build. I checked the computed values are the same. This is on my laptop corei5. dub -b release-nobounds --force parallel time msec:448 non_parallel msec:767 dub -b debug --force parallel time msec:2465 non_parallel msec:4962 on my corei7 desktop, the release-no bounds parallel time msec:161 non_parallel msec:571
Re: sliced().array compatibility with parallel?
On Sunday, 10 January 2016 at 12:11:39 UTC, Russel Winder wrote:
foreach( dv; dvp){
if(dv != dv){ // test for NaN
return 1;
}
}
return(0);
}
I am not convinced these "Tests for NaN" actually test for NaN.
I
believe you have to use isNan(dv).
I saw it mentioned in another post, and tried it. Works.
Re: sliced().array compatibility with parallel?
On Sunday, 10 January 2016 at 00:41:35 UTC, Ilya Yaroshenko wrote: It is a bug (Slice or Parallel ?). Please fill this issue. Slice should work with parallel, and array of slices should work with parallel. Ok, thanks, I'll submit it.
Re: sliced().array compatibility with parallel?
On Saturday, 9 January 2016 at 23:20:00 UTC, Jay Norwood wrote:
I'm playing around with win32, v2.069.2 dmd and
"dip80-ndslice": "~>0.8.8". If I convert the 2D slice with
.array(), should that first dimension then be compatible with
parallel foreach?
I find that without using parallel, all the means get computed,
but with parallel, only about half of them are computed in
this example. The others remain NaN, examined in the debugger
in Visual D.
import std.range : iota;
import std.array : array;
import std.algorithm;
import std.datetime;
import std.conv : to;
import std.stdio;
import std.experimental.ndslice;
enum testCount = 1;
double[1000] means;
double[] data;
void f1() {
import std.parallelism;
auto sl = data.sliced(1000,100_000);
auto sla = sl.array();
foreach(i,vec; parallel(sla)){
double v=vec.sum(0.0);
means[i] = v / 100_000;
}
}
void main() {
data = new double[100_000_000];
for(int i=0;i<100_000_000;i++){ data[i] = i/100_000_000.0;}
auto r = benchmark!(f1)(testCount);
auto f0Result = to!Duration(r[0] / testCount);
f0Result.writeln;
writeln(means[0]);
}
This is a bug in std.parallelism :-)
Proof:
import std.range : iota;
import std.array : array;
import std.algorithm;
import std.datetime;
import std.conv : to;
import std.stdio;
import mir.ndslice;
import std.parallelism;
enum testCount = 1;
double[1000] means;
double[] data;
void f1() {
//auto sl = data.sliced(1000, 100_000);
//auto sla = sl.array();
auto sla = new double[][1000];
foreach(i, ref e; sla)
{
e = data[i * 100_000 .. (i+1) * 100_000];
}
foreach(i,vec; parallel(sla))
{
double v = vec.sum;
means[i] = v / vec.length;
}
}
void main() {
data = new double[100_000_000];
foreach(i, ref e; data){
e = i / 100_000_000.0;
}
auto r = benchmark!(f1)(testCount);
auto f0Result = to!Duration(r[0] / testCount);
f0Result.writeln;
writeln(means);
}
Prints:
[0.00045, 0.0015, 0.0025, 0.0035, 0.0044, 0.0054,
0.0064, 0.0074, 0.0084, 0.0094, 0.0105, 0.0115,
0.0125, 0.0135, 0.0145, 0.0155, 0.0165, 0.0175, 0.0185, 0.0195,
0.0205, 0.0215, 0.0225, 0.0235, 0.0245, 0.0255, 0.0265, 0.0275,
0.0285, 0.0295, 0.0305, 0.0315, 0.0325, 0.0335, 0.0345, 0.0355,
0.0365, 0.0375, 0.0385, 0.0395, 0.0405, 0.0415, 0.0425, 0.0435,
0.0445, 0.0455, 0.0465, 0.0475, 0.0485, 0.0495, 0.0505, 0.0515,
0.0525, 0.0535, 0.0545, 0.0555, 0.0565, 0.0575, 0.0585, 0.0595,
0.0605, 0.0615, 0.0625, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan
sliced().array compatibility with parallel?
I'm playing around with win32, v2.069.2 dmd and "dip80-ndslice":
"~>0.8.8". If I convert the 2D slice with .array(), should that
first dimension then be compatible with parallel foreach?
I find that without using parallel, all the means get computed,
but with parallel, only about half of them are computed in this
example. The others remain NaN, examined in the debugger in
Visual D.
import std.range : iota;
import std.array : array;
import std.algorithm;
import std.datetime;
import std.conv : to;
import std.stdio;
import std.experimental.ndslice;
enum testCount = 1;
double[1000] means;
double[] data;
void f1() {
import std.parallelism;
auto sl = data.sliced(1000,100_000);
auto sla = sl.array();
foreach(i,vec; parallel(sla)){
double v=vec.sum(0.0);
means[i] = v / 100_000;
}
}
void main() {
data = new double[100_000_000];
for(int i=0;i<100_000_000;i++){ data[i] = i/100_000_000.0;}
auto r = benchmark!(f1)(testCount);
auto f0Result = to!Duration(r[0] / testCount);
f0Result.writeln;
writeln(means[0]);
}
Re: sliced().array compatibility with parallel?
for example, means[63] through means[251] are consistently all NaN when using parallel in this test, but are all computed double values when parallel is not used.
Re: sliced().array compatibility with parallel?
On Saturday, 9 January 2016 at 23:20:00 UTC, Jay Norwood wrote: I'm playing around with win32, v2.069.2 dmd and "dip80-ndslice": "~>0.8.8". If I convert the 2D slice with .array(), should that first dimension then be compatible with parallel foreach? [...] It is a bug (Slice or Parallel ?). Please fill this issue. Slice should work with parallel, and array of slices should work with parallel.
Re: sliced().array compatibility with parallel?
On Saturday, 9 January 2016 at 23:20:00 UTC, Jay Norwood wrote: I'm playing around with win32, v2.069.2 dmd and "dip80-ndslice": "~>0.8.8". If I convert the 2D slice with .array(), should that first dimension then be compatible with parallel foreach? [...] Oh... there is no bug. means must be shared =) : shared double[1000] means;
Re: sliced().array compatibility with parallel?
On Sunday, 10 January 2016 at 01:54:18 UTC, Jay Norwood wrote:
ok, thanks. That works. I'll go back to trying ndslice now.
The parallel time for this case is about a 2x speed-up on my
corei5 laptop, debug build in windows32, dmd.
D:\ec_mars_ddt\workspace\nd8>nd8.exe
parallel time msec:2495
non_parallel msec:5093
===
import std.array : array;
import std.algorithm;
import std.datetime;
import std.conv : to;
import std.stdio;
import std.experimental.ndslice;
shared double[1000] means;
double[] data;
void f1() {
import std.parallelism;
auto sl = data.sliced(1000,100_000);
foreach(i,vec; parallel(sl)){
means[i] = vec.sum / 100_000;
}
}
void f2() {
auto sl = data.sliced(1000,100_000);
foreach(i,vec; sl.array){
means[i] = vec.sum / 100_000;
}
}
void main() {
data = new double[100_000_000];
for(int i=0;i<100_000_000;i++){ data[i] = i/100_000_000.0;}
StopWatch sw1, sw2;
sw1.start();
f1() ;
auto r1 = sw1.peek().msecs;
sw2.start();
f2();
auto r2 = sw2.peek().msecs;
writeln("parallel time msec:",r1);
writeln("non_parallel msec:", r2);
}
Re: sliced().array compatibility with parallel?
On Sunday, 10 January 2016 at 02:43:05 UTC, Jay Norwood wrote: On Sunday, 10 January 2016 at 01:54:18 UTC, Jay Norwood wrote: [...] The parallel time for this case is about a 2x speed-up on my corei5 laptop, debug build in windows32, dmd. [...] I will add significantly faster pairwise summation based on SIMD instructions into the future std.las. --Ilya
Re: sliced().array compatibility with parallel?
On Sunday, 10 January 2016 at 01:16:43 UTC, Ilya Yaroshenko wrote: On Saturday, 9 January 2016 at 23:20:00 UTC, Jay Norwood wrote: I'm playing around with win32, v2.069.2 dmd and "dip80-ndslice": "~>0.8.8". If I convert the 2D slice with .array(), should that first dimension then be compatible with parallel foreach? [...] Oh... there is no bug. means must be shared =) : shared double[1000] means; ok, thanks. That works. I'll go back to trying ndslice now.
