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Programare paralela in .NET 4.0

Plecam de la premiza ca avem o metoda care executa acelasi cod pentru fiecare element dintr-o colectie:
public bool CheckItem(Item item)
In mod natural am scrie:
foreach (Item item in items)
item.IsValid = CheckItem(item);

Am putea sa rulam paralel acest cod foarte usor, daca ne folosim de clasa ajutatoare Parallel:
Parallel.ForEach(items,new Action(item=>CheckItem(item)));
O varianta mai simpla in acest caz este sa scriem direct:
Daca vrem sa parcurgem doar o parte din colectie putem sa scriem in felul urmator:
Parallel.For(0, 3, new Action(index => CheckItem(items[index])));
Atentie, daca lucrati cu stream-uri sau baze de date trebuie avut grija la close si dispose.
Problema este ceea ce se petrece in spate. N threaduri sunt folosite pentru linia de cod scrisa mai sus. Daca suntem pe client side, acest lucru nu este atat de important, dar daca suntem pe partea de server, putem avea mari probleme. De exemplu putem sa ajunge sa avem toate threadurile din pool folosite. Aceasta problema apare din cauza ca pentru fiecare request care vine prin WCF sau HTTP poate sa porneasca N threaduri. Din punct de vedere a scalabilității aici pot sa apara mari problem( vezi P.S. de la sfîrșitul postului).
public async Task CheckItem(ITempDataProvider item)
from item in items select CheckItem(item),
endTask => NotifyWaiter());
Pentru a putea face acest lucru aveti nevoie de Async CTP instalat. Acesta in spate apeleaza asyncron pentru fiecare item in parte metoda CheckItem. Keywordul async ii spune compilatorului ca acesta metoda o sa fie apelata asyncron. Iar ContinueWhenAll creaza un task care o sa fie rulat la sfarsitul executiei listei de taskuri specificate.
Async CTP
O alta varianta este sa folosim
WithDegreeOfParallelism. Aceasta ne permite sa specificam numarul de threaduri care pot sa ruleze paralel prin intermediul limbajului PLINQ
P.S.: Am ajuns sa scriu acest post in momentul in care am scris un cod server side care folosea Parallel.ForEach pentru a prelucra un request de la client si am ajuns doar prin cateva requesturi sa ocup peste 200MB din memorie.


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