# [R] Comparing execution times

Alaios alaios at yahoo.com
Mon Apr 11 12:29:56 CEST 2011

```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));

}
user   system  elapsed
1825.699  303.100 1063.352

--------------------------------------------------------------------------

B.Case. Same as above but with lapply instead of for
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

+ 			      #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.

```