Dear all,
In my 'simple' computer I was running some experiments to help me understand
how faster a multicore lapply will be. I thought it might be interesting for
some people to look at the results.
Even though are not accurate, still might be a good indicator how much
improvement there can be.
A.Case. The classic: for 1:100
for (i in c(1:dimz)){
print(sprintf('Creating the %d map',i));
Shadowlist[,,i]<- GaussRF(x=x, y=y, model=model,
grid=TRUE,param=c(mean,variance,nugget,scale,Whit.alpha))
}
user system elapsed
1825.699 303.100 1063.352
--------------------------------------------------------------------------
B.Case. Same as above but with lapply instead of for
Shadowlist<-lapply(1:dimz, function(i) {
print(sprintf('Creating the %d map',i));
GaussRF(x=x, y=y, model=model,
grid=TRUE,param=c(mean,variance,nugget,scale,Whit.alpha))
}
) )
user system elapsed
1816.784 296.745 1062.142
-------------------------------------------
C.Case. Foreach is considered to be easier to be applied to manycores.
foreach (i=1:dimz) %do% {
print(sprintf('Creating the %d map',i));
Shadowlist[,,i]<-f <- GaussRF(x=x, y=y, model=model,
grid=TRUE,param=c(mean,variance,nugget,scale,Whit.alpha))
}
user system elapsed
1027.058 13.243 1031.849
-----------------------------------
D. Case. The really multicore lapply. Great difference
system.time(Shadowlist<-mclapply(1:dimz, function(i) {
+ #print(sprintf('Creating the %d map',i));
+ GaussRF(x=x, y=y, model=model,
grid=TRUE,param=c(mean,variance,nugget,scale,Whit.alpha))
+ }
+ )
+ )
user system elapsed
263.134 99.639 549.366
-----------------------------------
My computer is a normal four core pc.
Great improvement with mlcapply.
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