Displaying 20 results from an estimated 31 matches for "clustercall".
2007 Aug 21
1
clusterCall with replicate function
...0
times and take the average of the output. A very simple way to do this is
to divide 5000 by the number of processors to get a number n and tell each
processor to run the algorithm n times. I realize there are more efficient
ways to manage the parallelization. To implement this I used the
clusterCall command with the replicate function along the lines of
clusterCall(cl, replicate, n, function(args)). Because my function is a
monte carlo process it relies on drawing from random distributions to
generate output. When I do this, all of my processors generate the same
random numbers. I copied...
2010 Aug 25
2
Problem with clusterCall, "Error in checkForRemoteErrors(lapply(cl, recvResult)) : "
...marker ];
x = X[ marker, ];
z = Z[ marker, ];
u[1,] = ui.1.Sample( u[1,] , y , x , beta , z , C[1] , sigma.sq , sd ,
burnin , iteration )$uFinal;
for( i in 2:n)
{
marker = levels == patients[i];
y = Y[ marker ];
x = X[ marker, ];
z = Z[ marker, ];
print( i );
u[i, ] = clusterCall( cluster , ui.1.Sample, u[i,] , y , x , beta , z ,
C[i] , sigma.sq , sd , burnin , iteration )$uFinal;
print( i );
}
stopCluster( cluster );
u;
}
If anyone could help that would be much appreciated! But big thanks for
taking your time to read the post!
TelM8
--
View this message in...
2005 Dec 01
1
Snow & rvpm
...U)
Origin = "GenuineIntel" Id = 0xf24 Stepping = 4
real memory = 260046848 (248 MB)
This machine NbBSD:
CPU: Mobile Intel(R) Pentium(R) 4 - M CPU 2.00GHz (1993.54-MHz 686-class CPU)
real memory = 536674304 (511 MB)
And starting library snow under R I have the following situation
clusterCall(cl, function() Sys.info()[c("sysname",
"release","nodename","machine")])
[[1]]
sysname release nodename machine
"FreeBSD" "5.4-RELEASE" "uffbsd.myd" "i386"
[[2]]
sysname...
2006 Apr 20
1
Parallel computing with the snow package: external file I/O possible?
...appen. I don't understand why the model
doesn't get run in the slave mode, but I noticed something described
below that I thought might be related.
It is hard for me to figure out a short and simple example of my
genoud() problem for posting here, so let me start with some code that
uses clusterCall(). As I understand the snow package, each execution of
the function "fun" from clusterCall() (or of the function fn() from
genoud()) should be independent. However, in the code below, the
directory created by each node has the same random number in its name.
I was expecting the contents...
2012 Aug 08
1
random number generator with SNOW/ Parallel/ foreach
...d to use
*rsprng* as a generator. Does this generator relay on seed as well? But it
seems to me that if I do not use rsprng, it is also fine.
## code without rsprng ##
testfun <- function(){
a <- rnorm(5,0,1)
return(a)
}
library(snow)
cl <- makeCluster(5,type="SOCK")
clusterCall(cl, testfun)
stopCluster(cl)
## code with rsprng ##
testfun <- function(){
a <- rnorm(5,0,1)
return(a)
}
library(snow)
library(rsprng)
cl <- makeCluster(5,type="SOCK")
clusterSetupRNG(cl)
clusterCall(cl, testfun)
stopCluster(cl)
3. Suppose I would like to use the p...
2011 Feb 03
1
problem with parLapply from snow
...The function is
CallSnow <- function (Nnodes = 2, Nsamples = Nnodes)
{
require(Rmpi)
require(snow)
cat("Using snow package, asking for ", Nnodes, "nodes \n")
cl <- makeCluster(Nnodes, type="MPI")
on.exit(stopCluster(cl))
#print(do.call("rbind", clusterCall(cl, function(cl) Sys.info()["nodename"])))
#
## uses RSPRNG if there
#
#clusterSetupSPRNG(cl)
clusterSetupRNGstream(cl, seed = rep(123456, 6))
yu <- clusterCall(cl, runif, Nsamples)
yusum <- parLapply(cl, yu, sum)
print(yusum)
yn <...
2006 Mar 13
1
Parallel computing with the snow package: external file I/O possible?
...ternal file I/O is happening
only in the master node and not in the slaves. I have followed Jasjeet
Sekhon's suggestion to test the cluster setup, and that is fine:
> library(snow)
>
> #pick two machines
> cl <- makeCluster(c("moab","escalante"))
>
> clusterCall(cl, sin, 2)
> The output should be:
> > clusterCall(cl, sin, 2)
> [[1]]
> [1] 0.9092974
>
> [[2]]
> [1] 0.9092974
>
I do indeed get the above result, so I presume the network setup is ok.
Next I tested a function that creates a file. Here is the code that I
sourced...
2006 Oct 13
3
Rmpi performance
...det_inv = det(inversa)
+ tr_inv = sum(diag(inversa))
+ return(list(c(det=det_inv,tr=tr_inv)))
+ }
>nn = 3000
>XX = matrix(rnorm(nn*nn),nn,nn)
# with the master
> system.time(op_matrici(XX))
[1] 42.283 1.883 44.168 0.000 0.000
# with the cluster
> system.time(clusterCall(cl,op_matrici,XX))
[1] 11.523 12.612 71.562 0.000 0.000
You can see that using the master it takes 44.168 seconds for computing
the function on matrix XX while it takes 71.562 seconds (more time!!!)
with the cluster. Can you give us some advice in order to understand why
the cluster is slower...
2008 Sep 30
1
prblems changing directory in mpi snow clusters
...n, I cannot change the working directory in my nodes.
> noclusters<-2
> cl <- makeCluster(noclusters, type = "MPI")
2 slaves are spawned successfully. 0 failed.
> foo<-clusterApply(cl,1:noclusters,function(noderank) {2+2})
> foo
[[1]]
[1] 4
[[2]]
[1] 4
> clusterCall(cl,setwd("C:/"))
Error in checkForRemoteErrors(lapply(cl, recvResult)) :
2 nodes produced errors; first error: variable "C:/" of mode "function"
was not found
>
Any suggestions ?
Thanks in advance,
Tolga
Generally, this communication is for informational p...
2018 Mar 04
3
Random Seed Location
...ve
reproducible results.
In the example that Bill gave, I think the problem is that set.seed()
only resets the seed in the main thread, the nodes continue to operate
with unreset RNG. To demonstrate this to yourself you can do
library(parallel)
cl <- parallel::makeCluster(3)
parallel::clusterCall(cl, function()set.seed(100))
parallel::clusterCall(cl, function()RNGkind())
parallel::clusterCall(cl, function()runif(2)) # similar result from
all nodes
# [1] 0.3077661 0.2576725
However, do *NOT* do that in real work. You will be getting the s...
2010 Dec 02
1
parLapply - Error in do.call("fun", lapply(args, enquote)) : could not find function "fun"
...,n2=n2,MC_1st_obs2=MC_1st_obs2)
using this it gives me the follow error:
Error in do.call("fun", lapply(args, enquote)) :
could not find function "fun"
but, this works correctly when I just use lapply (it's just a bit slower
than I need it to be). Also, I know that the clusterCall function works
fine with my homemade function because all the nodes of the cluster return
the appropriate results when I try this:
clusterCall(cl,M.set.find,setin=setin[[1]],month=month,n1=n1,n2=n2,MC_1st_obs2=MC_1st_obs2)
but that will only let me do this calculation one at a time.
I perused th...
2006 Apr 04
1
Mpirun with R CMD scripts
...eaha ~]$ R
R : Copyright 2005, The R Foundation for Statistical Computing
Version 2.2.0 (2005-10-06 r35749)
ISBN 3-900051-07-0
> library(snow)
> c1<-makeCluster(3, type="MPI")
Loading required package: Rmpi
3 slaves are spawned successfully. 0 failed.
>
>
>
> clusterCall(c1,function() Sys.info()[c("nodename","machine")])
[[1]]
nodename machine
"cheaha.ac.uab.edu" "x86_64"
[[2]]
nodename machine
"compute-0-12.local" "x86_64"
[[3]]...
2018 Mar 04
0
Random Seed Location
...ts.
>
> In the example that Bill gave, I think the problem is that set.seed() only resets the seed in the main thread, the nodes continue to operate with unreset RNG. To demonstrate this to yourself you can do
>
> library(parallel)
> cl <- parallel::makeCluster(3)
> parallel::clusterCall(cl, function()set.seed(100))
> parallel::clusterCall(cl, function()RNGkind())
> parallel::clusterCall(cl, function()runif(2)) # similar result from all nodes
> # [1] 0.3077661 0.2576725
>
> However, do *NOT* do that in real work. You wil...
2002 Aug 19
4
question about Rpvm, SNOW, etc.
...aged to get the following, using the same example that Prof. Tierney
used:
> system.time(nuke.boot <-
+ boot(nuke.data, nuke.fun, R=999, m=1,
+ fit.pred=new.fit, x.pred=new.data))
[1] 29.38 0.52 30.68 0.00 0.00
> system.time(cl.nuke.boot <-
+ clusterCall(cl,boot,nuke.data, nuke.fun, R=500, m=1,
+ fit.pred=new.fit, x.pred=new.data))
[1] 0.03 0.00 15.44 0.00 0.00
So I'm getting almost twice the performance, which is great.
Now the questions:
1. Since each of these boxes has two CPUs, how do I spawn more than one
sl...
2018 Mar 04
2
Random Seed Location
...In the example that Bill gave, I think the problem is that set.seed() only resets the seed in the main thread, the nodes continue to operate with unreset RNG. To demonstrate this to yourself you can do
>>
>> library(parallel)
>> cl <- parallel::makeCluster(3)
>> parallel::clusterCall(cl, function()set.seed(100))
>> parallel::clusterCall(cl, function()RNGkind())
>> parallel::clusterCall(cl, function()runif(2)) # similar result from all nodes
>> # [1] 0.3077661 0.2576725
>>
>> However, do *NOT* do that in...
2005 Jan 21
2
Parallel computations using snow: how to combine boot objects?
...t (Linux
cluster) in order to estimate confidence intervals for a certain
parameter. Following the example in the documentation of the "snow"
package (http://www.stat.uiowa.edu/~luke/R/cluster/cluster.html), I
launch my computations by something like
> cl.nuke.boot <-
+ clusterCall(cl,boot,nuke.data, nuke.fun, R=500, m=1,
+ fit.pred=new.fit, x.pred=new.data)
which gives me a list of n boot objects (where n is the number of nodes
in my cluster). So far, so good.
However, if I now want to go further, I need to combine all these boot
objects to a single...
2018 Mar 04
0
Random Seed Location
...hat Bill gave, I think the problem is that set.seed() only resets the seed in the main thread, the nodes continue to operate with unreset RNG. To demonstrate this to yourself you can do
>>>
>>> library(parallel)
>>> cl <- parallel::makeCluster(3)
>>> parallel::clusterCall(cl, function()set.seed(100))
>>> parallel::clusterCall(cl, function()RNGkind())
>>> parallel::clusterCall(cl, function()runif(2)) # similar result from all nodes
>>> # [1] 0.3077661 0.2576725
>>>
>>> However...
2011 Feb 24
1
parallel bootstrap linear model on multicore mac (re-post)
...snowfall and snow. While I can create a cluster and run
simple processes (e.g., provided example from literature), I can't get
the bootstrap to run. For example, using snow:
cl <- makeCluster(8)
clusterSetupRNG(cl)
clusterEvalQ(cl,library(boot))
clusterEvalQ(cl,library(lme4))
boot.out<-clusterCall(cl,boot(dat.res,boot.fun, R = nboot))
stopCluster()
returns the following error:
Error in checkForRemoteErrors(lapply(cl, recvResult)) :
8 nodes produced errors; first error: could not find function "fun"
I am stuck and at the limit of my programming knowledge and am punting
to the...
2018 Mar 05
1
Random Seed Location
...d()
>>>> only resets the seed in the main thread, the nodes continue to operate with
>>>> unreset RNG. To demonstrate this to yourself you can do
>>>>
>>>> library(parallel)
>>>> cl <- parallel::makeCluster(3)
>>>> parallel::clusterCall(cl, function()set.seed(100))
>>>> parallel::clusterCall(cl, function()RNGkind())
>>>> parallel::clusterCall(cl, function()runif(2)) # similar result from all
>>>> nodes
>>>> # [1] 0.3077661 0.2576725
>...
2005 Dec 02
0
problems with R and snow on a debian box only
...akeCluster(3,type="PVM")
R complains that tid<0 and I cannot go further even thoug R goes on working.
Case 2) One of the 2 fbsd boxes as master and the others as slaves: R and
snow seem to work; I can makeCluster but R freezes as soon as I try to
execute a cluster order like parApply, clusterCall, etc.
What should I check?
Vittorio