I have a for loop that is something like this:
for (i=1:150000) { tempMatrix = {} tempMatrix = functionThatDoesSomething() #calling a function finalMatrix = cbind(finalMatrix, tempMatrix) } Could you tell me how to make this parallel ?
I tried this based on an example online, but am not sure if the syntax is correct. It also didn't increase the speed much.
finalMatrix = foreach(i=1:150000, .combine=cbind) %dopar% { tempMatrix = {} tempMatrix = functionThatDoesSomething() #calling a function cbind(finalMatrix, tempMatrix) } 1 Answer
Thanks for your feedback. I did look up parallel after I posted this question.
Finally after a few tries, I got it running. I have added the code below in case it is useful to others
library(foreach) library(doParallel) #setup parallel backend to use many processors cores=detectCores() cl <- makeCluster(cores[1]-1) #not to overload your computer registerDoParallel(cl) finalMatrix <- foreach(i=1:150000, .combine=cbind) %dopar% { tempMatrix = functionThatDoesSomething() #calling a function #do other things if you want tempMatrix #Equivalent to finalMatrix = cbind(finalMatrix, tempMatrix) } #stop cluster stopCluster(cl) Note - I must add a note that if the user allocates too many processes, then user may get this error: Error in serialize(data, node$con) : error writing to connection
Note - If .combine in the foreach statement is rbind , then the final object returned would have been created by appending output of each loop row-wise.
Hope this is useful for folks trying out parallel processing in R for the first time like me.
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