Re: [R] on parallel processing...

2022-11-04 Thread akshay kulkarni
: Saturday, November 5, 2022 1:24 AM To: r-help@r-project.org ; akshay kulkarni ; R help Mailing list Subject: Re: [R] on parallel processing... You don't specify processors. Just invoke the worker functions with the relevant packages and they will be allocated according to how you defined the cluster

Re: [R] on parallel processing...

2022-11-04 Thread Jeff Newmiller
You don't specify processors. Just invoke the worker functions with the relevant packages and they will be allocated according to how you defined the cluster object... typically automatically. Processors are usually specified (to the cluster object) according to IP address. Cores within the

[R] on parallel processing...

2022-11-04 Thread akshay kulkarni
Dear members, I want to send the same function with different arguments to different processors. This solution was provided in Stack Overflow https://stackoverflow.com/questions/25045998/send-function-calls-with-different-arguments-to-different-processors-in-r-using

[R] %dopar% parallel processing experiment

2011-07-02 Thread ivo welch
dear R experts--- I am experimenting with multicore processing, so far with pretty disappointing results. Here is my simple example: A - 10 randvalues - abs(rnorm(A)) minfn - function( x, i ) { log(abs(x))+x^3+i/A+randvalues[i] }  ## an arbitrary function ARGV -

Re: [R] %dopar% parallel processing experiment

2011-07-02 Thread Uwe Ligges
On 02.07.2011 19:32, ivo welch wrote: dear R experts--- I am experimenting with multicore processing, so far with pretty disappointing results. Here is my simple example: A- 10 randvalues- abs(rnorm(A)) minfn- function( x, i ) { log(abs(x))+x^3+i/A+randvalues[i] } ## an arbitrary

Re: [R] %dopar% parallel processing experiment

2011-07-02 Thread ivo welch
thank you, uwe. this is a little disappointing. parallel processing for embarrassingly simple parallel operations--those needing no communication---should be feasible if the thread is not always created and released, but held. is there light-weight parallel processing that could facilitate

Re: [R] %dopar% parallel processing experiment

2011-07-02 Thread Uwe Ligges
On 02.07.2011 20:04, ivo welch wrote: thank you, uwe. this is a little disappointing. parallel processing for embarrassingly simple parallel operations--those needing no communication---should be feasible if the thread is not always created and released, but held. is there light-weight

Re: [R] %dopar% parallel processing experiment

2011-07-02 Thread ivo welch
hi uwe--I did not know what snow was. from my 1 minute reading, it seems like a much more involved setup that is much more flexible after the setup cost has been incurred (specifically, allowing use of many machines). the attractiveness of the doMC/foreach framework is its simplicity of

Re: [R] %dopar% parallel processing experiment

2011-07-02 Thread Uwe Ligges
On 02.07.2011 20:42, ivo welch wrote: hi uwe--I did not know what snow was. from my 1 minute reading, it seems like a much more involved setup that is much more flexible after the setup cost has been incurred (specifically, allowing use of many machines). the attractiveness of the

Re: [R] %dopar% parallel processing experiment

2011-07-02 Thread Steve Lianoglou
Here's another datapoint using the multicore package -- which is what the foreach/doMC combo uses internally: I halved your A value to 50,000 because I was getting impatient :-) A=5 randvalues - abs(rnorm(A)) minfn - function( x, i ) { log(abs(x))+x^3+i/A+randvalues[i] } system.time(a1 -

[R] manual parallel processing

2007-11-22 Thread jgarcia
Hi; I have a R script that includes a call to genoud(); genoud process lasts about 4 seconds, what would be OK if I hadn't have to call it about 2000 times. This yields about 2 hours of processing. And I would like to use this script operationally; so that it should be run twice a day. It seems to

Re: [R] manual parallel processing

2007-11-22 Thread Martin Morgan
Hi Javier The Rmpi or snow packages might help, e.g., mpi.parLapply; you need to pay attention to what gets (explicitly or implicitly) shared with other nodes. Martin [EMAIL PROTECTED] writes: Hi; I have a R script that includes a call to genoud(); genoud process lasts about 4 seconds, what