How to use Reduce() function in R parallel computing?

Question

I want to run a Reduce code to out1 a list of 66000 list elements:

trialStep1_done <- Reduce(rbind, out1)

However, it takes too long to run. I wonder whether I can run this code with help of a parallel computing package.

I know there is mclapply, mcMap, but I don't see any function like mcReduce in parallel computing package.

Is there a function like mcReduce available for doing Reduce with parallel in R to complete the task I wanted to do?

Thanks a lot @BrodieG and @zheYuan Li, your answers are very helpful. I think the following code example can represent my question with more precision:

df1 <- data.frame(a=letters, b=LETTERS, c=1:26 %>% as.character())
set.seed(123)
df2 <- data.frame(a=letters %>% sample(), b=LETTERS %>% sample(), c=1:26 %>% sample() %>% as.character())
set.seed(1234)
df3 <- data.frame(a=letters %>% sample(), b=LETTERS %>% sample(), c=1:26 %>% sample() %>% as.character())
out1 <- list(df1, df2, df3)

# I don't know how to rbind() the list elements only using matrix()
# I have to use lapply() and Reduce() or do.call()
out2 <- lapply(out1, function(x) matrix(unlist(x), ncol = length(x), byrow = F))

Reduce(rbind, out2)
do.call(rbind, out2)
# One thing is sure is that `do.call()` is super faster than `Reduce()`, @BordieG's answer helps me understood why.

So, at this point, to my 200000 rows dataset, do.call() solves the problem very well.

Finally, I wonder whether this is an even faster way? or the way @ZheYuanLi demostrated with just matrix() could be possible here?

BrodieG BrodieG · Accepted Answer · 2016-06-05T02:13:02

The problem is not rbind, the problem is Reduce. Unfortunately, function calls in R are expensive, and particularly so when you keep creating new objects. In this case, you call rbind 65999 times, and each time you do you create a new R object with one row added. Instead, you can just call rbind once with 66000 arguments, which will be much faster since internally rbind will do the binding in C without having to call R functions 66000 times and allocating the memory just once. Here we compare your Reduce use with Zheyuan's matrix/unlist and finally with rbind called once with do.call (do.call allows you to call a function with all arguments specified as a list):

out1 <- replicate(1000, 1:20, simplify=FALSE)  # use 1000 elements for illustrative purposes

library(microbenchmark)    
microbenchmark(times=10,
  a <- do.call(rbind, out1),
  b <- matrix(unlist(out1), ncol=20, byrow=TRUE),
  c <- Reduce(rbind, out1)
)
# Unit: microseconds
#                                                expr        min         lq
#                           a <- do.call(rbind, out1)    469.873    479.815
#  b <- matrix(unlist(out1), ncol = 20, byrow = TRUE)    257.263    260.479
#                            c <- Reduce(rbind, out1) 110764.898 113976.376
all.equal(a, b, check.attributes=FALSE)
# [1] TRUE
all.equal(b, c, check.attributes=FALSE)
# [1] TRUE

Zheyuan is the fastest, but for all intents and purposes the do.call(rbind()) method is pretty similar.

How to use Reduce() function in R parallel computing?

2 Answers