# RAM超過6GB，才可以run!!
# 否則請更改矩陣大小(mat_size)或是列表的長度(length_list)
length_list = 30
mat_size = c(3000, 3000)

mat = matrix(rnorm(prod(mat_size)), mat_size[1], mat_size[2])
mat.list = lapply(1:length_list, function(i) mat)

res1 = function(){
    mat_sum = matrix(rep(0, prod(mat_size)), mat_size[1], mat_size[2])
    for (i in 1:length_list){
        mat_sum = mat_sum + mat.list[[i]]
    }
    mat_sum
}
res2 = function() Reduce('+', mat.list)
res3 = function() matrix(rowSums(matrix(unlist(mat.list), nrow=prod(mat_size))), nrow=mat_size[1])
res4 = function() matrix(colSums(do.call(rbind, lapply(mat.list, as.vector))), nrow=mat_size[1])
res5 = function() matrix(rowSums(do.call(cbind, lapply(mat.list, as.vector))), nrow=mat_size[1])
library(Rcpp)
library(RcppArmadillo)
library(inline)
sourceCpp(code = '
// [[Rcpp::depends(RcppArmadillo)]]
#include <RcppArmadillo.h>
#include <stdio.h>
using namespace Rcpp;
using namespace arma;
// [[Rcpp::export]]
mat list_mat_sum_f(List data_list){
  int n = data_list.size();
  SEXP mat1 = data_list[0];
  NumericMatrix mat1_rmat(mat1);
  mat result_mat(mat1_rmat.begin(), mat1_rmat.nrow(), mat1_rmat.ncol());
  for(int i = 1; i < n; i++)
  {
    SEXP tmp_m = data_list[i];
    NumericMatrix data_m(tmp_m);
    mat working_m(data_m.begin(), data_m.nrow(), data_m.ncol(), false);
    result_mat += working_m;
  }
  return result_mat;
}')
res6 = function() list_mat_sum_f(mat.list)

# cmpfun
library(compiler)
res1_cmp = cmpfun(res1)
res2_cmp = cmpfun(res2)
res3_cmp = cmpfun(res3)
res4_cmp = cmpfun(res4)
res5_cmp = cmpfun(res5)

all.equal(tmp <- res1(), res2())
# TRUE
all.equal(tmp, res3())
# TRUE
all.equal(tmp, res4())
# TRUE
all.equal(tmp, res5())
# TRUE
all.equal(tmp, res6())
# TRUE

library(rbenchmark)
benchmark(res1(),res2(),res3(),res4(),res5(),res6(),res1_cmp(),res2_cmp(),res3_cmp(),res4_cmp(),res5_cmp(),replications = 20, columns=c('test', 'replications', 'elapsed','relative', 'user.self'),  order='relative')

#          test replications elapsed relative user.self
# 6      res6()           20    6.49    1.000      6.13
# 2      res2()           20   15.58    2.401      8.45
# 8  res2_cmp()           20   16.19    2.495      8.61
# 1      res1()           20   18.44    2.841     10.61
# 7  res1_cmp()           20   19.33    2.978     11.15
# 5      res5()           20   56.60    8.721     40.24
# 11 res5_cmp()           20   56.79    8.750     40.36
# 4      res4()           20   70.48   10.860     54.63
# 10 res4_cmp()           20   70.53   10.867     55.17
# 3      res3()           20   92.87   14.310     76.21
# 9  res3_cmp()           20   93.74   14.444     77.19