[R-sig-hpc] Parallel linear model

[Martin Morgan]

On 08/24/2012 07:28 AM, Patrik Waldmann wrote:
> I have some additional questions: I want to add an extra matrix that I keep fixed, but I cannot get the parallel version working, any suggestions?
>
> y<-rnorm(1000)
> x<-matrix(rnorm(1000*10000),ncol=10000)
> fix<-matrix(rnorm(1000*10),ncol=10)
> #Non-parallel version works fine
> pval<-apply(x,2, function(x)summary(lm(y~x+fix))$coeff[2,4])
>
> library(parallel)
> cores<-detectCores()
> cl <- makeCluster(cores, methods=FALSE)
> clusterExport(cl,"y","fix")
> pvalstruct<-parApply(cl, x,2, function(x)summary(lm(y~x+fix))$coeff[2,4])

the easiest is to write function in *apply to accept all necessary 
arguments, e.g.,

   fun = function(x, fix) summary(lm(y~x+fix))$coeff[2,4]
   parApply(cl, x, 2, fun, fix)

Martin

>
>
> Patrik
>
>
>>>> "Patrik Waldmann" <patrik.waldmann at boku.ac.at> 23/08/2012 12:12 >>>
> Here's a comparison on Windows based on 8 cores (excluding foreach):
>> y<-rnorm(1000)
>> x<-matrix(rnorm(1000*10000),ncol=10000)
>> dimx<-dim(x)
>> library(rbenchmark)
>> benchmark(pval<-apply(x,2, function(x)summary(lm(y~x))$coeff[2,4]), replications=1)
>
> 1 pval <- apply(x, 2, function(x) summary(lm(y ~ x))$coeff[2, 4])
> test replications elapsed relative user.self sys.self user.child sys.child
> 1    1   25.16        1     20.46     2.17         NA        NA
>
>> library(parallel)
>> cores<-detectCores()
>> cl <- makeCluster(cores, methods=FALSE)
>> clusterExport(cl,"y")
>> benchmark(pval<-parApply(cl, x,2, function(x)summary(lm(y~x))$coeff[2,4]), replications=1)
> 1 pval <- parApply(cl, x, 2, function(x) summary(lm(y ~ x))$coeff[2, 4])
>    test replications elapsed relative user.self sys.self user.child sys.child
> 1      1    5.52        1      0.74     0.28         NA        NA
>
>> stopCluster(cl)
>
> # More fair
>
>> benchmark({cores<-detectCores()
> + cl <- makeCluster(cores, methods=FALSE)
> + clusterExport(cl,"y")
> + pval<-parApply(cl, x,2, function(x)summary(lm(y~x))$coeff[2,4])}, replications=1)
>    test replications elapsed relative user.self sys.self user.child sys.child
> 1    {            1    7.11        1      0.65     0.37         NA        NA
> Warning messages:
> 1: closing unused connection 10 (<-patwa-PC:10187)
> 2: closing unused connection 9 (<-patwa-PC:10187)
> 3: closing unused connection 8 (<-patwa-PC:10187)
> 4: closing unused connection 7 (<-patwa-PC:10187)
> 5: closing unused connection 6 (<-patwa-PC:10187)
> 6: closing unused connection 5 (<-patwa-PC:10187)
> 7: closing unused connection 4 (<-patwa-PC:10187)
> 8: closing unused connection 3 (<-patwa-PC:10187)
>> stopCluster(cl)
>
> What does the warnings refer to?
>
> Patrik
>
>>>> Dirk Eddelbuettel <edd at debian.org> 23/08/2012 02:53 >>>
>
> The difference between user and elapsed is an old hat. Here is a great
> example (and IIRC first shown here by Simon) with no compute time:
>
>     R> system.time(mclapply(1:8, function(x) Sys.sleep(1)))   ## 2 cores by default
>        user  system elapsed
>       0.000   0.012   4.014
>     R> system.time(mclapply(1:8, function(x) Sys.sleep(1), mc.cores=8))
>        user  system elapsed
>       0.012   0.020   1.039
>     R>
>
> so elapsed time is effectively the one second a Sys.sleep(1) takes, plus
> overhead, if we allow for all eight (hyperthreaded) cores here.  By Brian
> Ripley's choice a default of two is baked-in, so clueless users only get a
> small gain.  "user time" is roughly the actual system load _summed over all
> processes / threads_.
>
> With that, could I ask any of the participants in the thread to re-try with a
> proper benchmarking package such as rbenchmark or microbenchmark?  Either one
> beats to the socks of system.time:
>
>     R> library(rbenchmark)
>     R> benchmark( mclapply(1:8, function(x) Sys.sleep(1)), mclapply(1:8, function(x) Sys.sleep(1), mc.cores=8), replications=1)
>                                                        test replications elapsed relative user.self sys.self user.child sys.child
>     1               mclapply(1:8, function(x) Sys.sleep(1))            1   4.013  3.89612     0.000    0.008      0.000     0.004
>     2 mclapply(1:8, function(x) Sys.sleep(1), mc.cores = 8)            1   1.030  1.00000     0.004    0.008      0.004     0.000
>     R>
>
> and
>
>     R> library(microbenchmark)
>     R> microbenchmark( mclapply(1:8, function(x) Sys.sleep(1)), mclapply(1:8, function(x) Sys.sleep(1), mc.cores=8), times=1)
>     Unit: seconds
>                                                        expr     min      lq  median      uq     max
>     1               mclapply(1:8, function(x) Sys.sleep(1)) 4.01377 4.01377 4.01377 4.01377 4.01377
>     2 mclapply(1:8, function(x) Sys.sleep(1), mc.cores = 8) 1.03457 1.03457 1.03457 1.03457 1.03457
>     R>
>
> (and you normally want to run either with 10 or 100 or ... replications /
> times).
>
> Dirk
>


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