multithread/concurrency/parallel methods and performance

[Dmitry Olshansky]

On Sunday, 18 February 2018 at 17:54:58 UTC, SrMordred wrote:
> I´m experimenting with threads and related recently.
> (i´m just started so may be some terrrible mistakes here)
>
> With this base work:
>
> foreach(i ; 0 .. SIZE)
> {
>     results[i] = values1[i] * values2[i];
> }
>
> and then with this 3 others methods: parallel, spawn and 
> Threads.
>
> this was my results:
>
> _base : 456 ms and 479 us
> _parallel : 331 ms, 324 us, and 4 hnsecs
> _concurrency : 367 ms, 348 us, and 2 hnsecs
> _thread : 369 ms, 565 us, and 3 hnsecs
>
> (code here : https://run.dlang.io/is/2pdmmk )
>
> All methods have minor speedup gains.  I was expecting a lot 
> more.
> Since I have 7 cores I expected like below 100ms.

The operation is trivial and dataset is rather small. In such 
cases SIMD with eg array ops is the way to go:
result[] = values[] * values2[];


Parallelism is gets more interesting with more expensive 
operations.  You may also try bigger sizes or both.
>
> I´m not seeing false sharing in this case. or i'm wrong?
>
> If someone can expand on this, i'll be grateful.
>
> Thanks!