A Split-Apply-Combine strategy to parallelize the evaluation of a function.

  p.combine = NULL,
  ind = cols_along(X),
  ncores = nb_cores(),



An object of class FBM.


The function to be applied to each subset matrix. It must take a Filebacked Big Matrix as first argument and ind, a vector of indices, which are used to split the data. For example, if you want to apply a function to X[ind.row, ind.col], you may use X[ind.row, ind.col[ind]] in a.FUN.


Function to combine the results with do.call. This function should accept multiple arguments (...). For example, you can use c, cbind, rbind. This package also provides function plus to add multiple arguments together. The default is NULL, in which case the results are not combined and are returned as a list, each element being the result of a block.


Initial vector of subsetting indices. Default is the vector of all column indices.


Number of cores used. Default doesn't use parallelism. You may use nb_cores.


Extra arguments to be passed to p.FUN.


Return a list of ncores elements, each element being the result of one of the cores, computed on a block. The elements of this list are then combined with do.call(p.combine, .) if p.combined is given.


This function splits indices in parts, then apply a given function to each part and finally combine the results.

See also


if (FALSE) # CRAN is super slow when parallelism. X <- big_attachExtdata() ### Computation on all the matrix true <- big_colstats(X)
#> Error in bigcolvars(X, ind.row, ind.col): objet 'X' introuvable
big_colstats_sub <- function(X, ind) { big_colstats(X, ind.col = ind) } # 1. the computation is split along all the columns # 2. for each part the computation is done, using `big_colstats` # 3. the results (data.frames) are combined via `rbind`. test <- big_parallelize(X, p.FUN = big_colstats_sub, p.combine = 'rbind', ncores = 2)
#> Error in ncol(x): objet 'X' introuvable
all.equal(test, true)
#> Error in all.equal(test, true): objet 'test' introuvable
### Computation on a part of the matrix n <- nrow(X)
#> Error in nrow(X): objet 'X' introuvable
m <- ncol(X)
#> Error in ncol(X): objet 'X' introuvable
rows <- sort(sample(n, n/2)) # sort to provide some locality in accesses
#> Error in sample(n, n/2): objet 'n' introuvable
cols <- sort(sample(m, m/2)) # idem
#> Error in sample(m, m/2): objet 'm' introuvable
true2 <- big_colstats(X, ind.row = rows, ind.col = cols)
#> Error in bigcolvars(X, ind.row, ind.col): objet 'X' introuvable
big_colstats_sub2 <- function(X, ind, rows, cols) { big_colstats(X, ind.row = rows, ind.col = cols[ind]) } # This doesn't work because, by default, the computation is spread # along all columns. We must explictly specify the `ind` parameter. tryCatch(big_parallelize(X, p.FUN = big_colstats_sub2, p.combine = 'rbind', ncores = 2, rows = rows, cols = cols), error = function(e) message(e))
#> Error in ncol(x): objet 'X' introuvable
# This now works, using `ind = seq_along(cols)`. test2 <- big_parallelize(X, p.FUN = big_colstats_sub2, p.combine = 'rbind', ncores = 2, ind = seq_along(cols), rows = rows, cols = cols)
#> Error in assert_one_int(total_len): objet 'cols' introuvable
all.equal(test2, true2)
#> Error in all.equal(test2, true2): objet 'test2' introuvable