I'm trying std.parallelism, and I made this code (based over foreach parallel example) :
import std.stdio;
import std.parallelism;
import std.math;
import std.c.time;

void main () {
  auto logs = new double[20_000_000];
		const num = 10;

		clock_t clk;
		double norm;
		double par;

		writeln("CPUs : ",totalCPUs );

		clk = clock();
		foreach (t; 0..num) {

	    foreach(i, ref elem; logs) {
	        elem = log(i + 1.0);
	    }
		}
		norm = clock() -clk;

		clk = clock();
		foreach (t; 0..num) {

	    foreach(i, ref elem; taskPool.parallel(logs, 100)) {
	        elem = log(i + 1.0);
	    }

    }
		par = clock() -clk;

		norm = norm / num;
		par = par / num;

    writeln("Normal : ", norm / CLOCKS_PER_SEC, " Parallel : ", par / CLOCKS_PER_SEC);
}

I get this result :

CPUs : 2
Normal : 1.325 Parallel : 1.646

And the result changes, every time that I run it, around +-100ms (I think that depends of how are CPUs busy in these moment)

I played changin workUnitSize from 1 to 10000000 without any apreciable change....
My computer it's a AMD Athlon 64 X2 Dual Core Processor 6000+ running over a kUbuntu 11.04 64bits with 2 GiB of ram. I compiled it with dmd 2.053
htop shows that when test program are running parallel foreach, both cores are at ~98% of load and with normal foreach, only one core gets at ~99% of load.

On 23/06/2011 11:05, Zardoz wrote:
> I'm trying std.parallelism, and I made this code (based over foreach parallel example) :
> import std.stdio;
> import std.parallelism;
> import std.math;
> import std.c.time;
>
> void main () {
>    auto logs = new double[20_000_000];
> 		const num = 10;
>
> 		clock_t clk;
> 		double norm;
> 		double par;
>
> 		writeln("CPUs : ",totalCPUs );
>
> 		clk = clock();
> 		foreach (t; 0..num) {
>
> 	    foreach(i, ref elem; logs) {
> 	        elem = log(i + 1.0);
> 	    }
> 		}
> 		norm = clock() -clk;
>
> 		clk = clock();
> 		foreach (t; 0..num) {
>
> 	    foreach(i, ref elem; taskPool.parallel(logs, 100)) {
> 	        elem = log(i + 1.0);
> 	    }
>
>      }
> 		par = clock() -clk;
>
> 		norm = norm / num;
> 		par = par / num;
>
>      writeln("Normal : ", norm / CLOCKS_PER_SEC, " Parallel : ", par / CLOCKS_PER_SEC);
> }
>
> I get this result :
>
> CPUs : 2
> Normal : 1.325 Parallel : 1.646
>
> And the result changes, every time that I run it, around +-100ms (I think that depends of how are CPUs busy in these moment)
>
> I played changin workUnitSize from 1 to 10000000 without any apreciable change....
> My computer it's a AMD Athlon 64 X2 Dual Core Processor 6000+ running over a kUbuntu 11.04 64bits with 2 GiB of ram. I compiled it with dmd 2.053
> htop shows that when test program are running parallel foreach, both cores are at ~98% of load and with normal foreach, only one core gets at ~99% of load.

The reason for this is your workload is very small - it's likely that the overhead from context switching and spawning threads is greater than the gain in performance from running in parallel. Using parallel() in foreach will only be faster if you're doing something more expensive.

Also note that parallel() is an alias for taskPool.parallel(), saving you a few characters :)

-- 
Robert
http://octarineparrot.com/

Ok, so why in std.parallelism examples are this :

// Same thing, but use the default work unit size.
    //
    // Timings on an Athlon 64 X2 dual core machine:
    //
    // Parallel foreach:  388 milliseconds
    // Regular foreach:   619 milliseconds
    foreach(i, ref elem; taskPool.parallel(logs)) {
        elem = log(i + 1.0);
    }

Plus, a change my code to make that for same elem, calc log 100000 times in each loop, and now I get  parallel foreach get a bit shorter time that normal foreach....
Change code :

  auto logs = new double[200];
		const num = 2;

		clock_t clk;
		double norm;
		double par;

		writeln("CPUs : ",totalCPUs );
foreach (t; 0..num) {

	    foreach(i, ref elem; logs) {
	    	foreach(p; 0..100_000)
	        elem = log(i + 1.0);
	    }
		}
		norm = clock() -clk;

		clk = clock();
		foreach (t; 0..num) {

	    foreach(i, ref elem; taskPool.parallel(logs, 100)) {
	    	foreach(p; 0..100_000)
	        elem = log(i + 1.0);
	    }

    }

New times : Normal : 12.725 Parallel : 12.499

Zardoz wrote:
> 		const num = 10;
>
> 		foreach (t; 0..num) {
>
> 	    foreach(i, ref elem; taskPool.parallel(logs, 100)) {
> 	        elem = log(i + 1.0);
> 	    }
>
>      }

I think you just spawned 10 tasks. Look at foreach (t; 0..num).

Code :
 auto logs = new double[200];
 const num = 2;
 clock_t clk;
 double norm;
 double par;
 writeln("CPUs : ",totalCPUs );
 clk = clock();
 foreach(i, ref elem; logs) {
  elem = log(i + 1.0);
 }
 norm = clock() -clk;
 clk = clock();
 foreach(i, ref elem; taskPool.parallel(logs, 100)) {
  elem = log(i + 1.0);
 }

I get same problem. Parallel foreach, is more slower that normal foreach. And it's same code that hace lib example that claims that parallel foreach do it in aprox. half time in Athlon X2

I don't know why David set a work unit of 100 for a 1 million element
array. I get slow results for this example:
    foreach(i, ref elem; taskPool.parallel(logs, 100)) {
        elem = log(i + 1.0);
    }

CPUs : 4
Serial usecs:   70418.
Parallel usecs: 91519.

But if I up the work unit size to 100_000 I get much better results:
    foreach(i, ref elem; taskPool.parallel(logs, 100_000)) {
        elem = log(i + 1.0);
    }

CPUs : 4
Serial usecs:   69979.
Parallel usecs: 25355.

Sometimes the best thing to do is let parallel use the default work unit size:
    foreach(i, ref elem; taskPool.parallel(logs)) {
        elem = log(i + 1.0);
    }
CPUs : 4
Serial usecs:   70219.
Parallel usecs: 21942.

Here's your original example on my PC:
CPUs : 4
Normal : 1.4609 Parallel : 2.4797

And here it is by letting parallel use the default work unit size:
CPUs : 4
Normal : 1.461 Parallel : 0.425

It's all about fine-tuning your parameters. Essentially when you up the work unit size it means each thread will process more elements from the array or range. If the loop body executes really fast, then you should increase the work unit size.

On Thu, 23 Jun 2011 23:18:36 +0000, Zardoz wrote:

> Code :
>  auto logs = new double[200];
>  const num = 2;
>  clock_t clk;
>  double norm;
>  double par;
>  writeln("CPUs : ",totalCPUs );
>  clk = clock();
>  foreach(i, ref elem; logs) {
>   elem = log(i + 1.0);
>  }
>  norm = clock() -clk;
>  clk = clock();
>  foreach(i, ref elem; taskPool.parallel(logs, 100)) {
>   elem = log(i + 1.0);
>  }
> 
> I get same problem. Parallel foreach, is more slower that normal foreach. And it's same code that hace lib example that claims that parallel foreach do it in aprox. half time in Athlon X2

I was able to reproduce your results. I think there is a problem with clock(). Try StopWatch:

import std.parallelism;
import std.stdio;
import std.math;
import std.datetime;

void main()
{
    auto logs = new double[200_000_000];

    writeln("CPUs : ",totalCPUs );

    {
        StopWatch stopWatch;
        stopWatch.start();

        foreach(i, ref elem; logs) {
            elem = log(i + 1.0);
        }

        writeln(stopWatch.peek().msecs);
    }

    {
        StopWatch stopWatch;
        stopWatch.start();

        foreach(i, ref elem; parallel(logs)) {
            elem = log(i + 1.0);
        }

        writeln(stopWatch.peek().msecs);
    }
}

Here is my output:

CPUs : 4
8061
2686

I get similar results whether I pass 100_000 to parallel() or not.

Ali

Thanks !!! Now I'm getting a more logical result. I get  1407ms normal vs 759ms in parallel foreach with logs[20_000_000]
It's strange that clock() do weird things with threading, not ?

PD: It's me, or http version of the forum do strange things, like not getting correct javascript action for the buttons if you hover from one to other ??

On 24/06/11 09:19, Zardoz wrote:
> Thanks !!! Now I'm getting a more logical result. I get  1407ms normal vs 759ms in parallel foreach with logs[20_000_000]
> It's strange that clock() do weird things with threading, not ?
>
> PD: It's me, or http version of the forum do strange things, like not getting correct javascript action for the buttons if you hover from one to other ??

I'm surprised that web ng thing hasn't been nuked already .. everyone around here (except you and some others) use Thunderbird!

http://www.mozillamessaging.com/en-US/thunderbird/

It's cross platform and comes in several languages, there's also support for POP3 and newsgroups. There may be more features, but I don't use them ..

Khint Enco Wrote:

> I'm surprised that web ng thing hasn't been nuked already .. everyone around here (except you and some others) use Thunderbird!
> 
> http://www.mozillamessaging.com/en-US/thunderbird/

There's no point in internet if every site would require its own brand name client to access it.