bpvec {BiocParallel} | R Documentation

## Parallel, vectorized evaluation

### Description

`bpvec` applies `FUN` to subsets of `X`. Any type of object `X` is allowed, provided `length`, and `[` are defined on `X`. `FUN` is a function such that `length(FUN(X)) == length(X)`. The objects returned by `FUN` are concatenated by `AGGREGATE` (`c()` by default). The return value is `FUN(X)`.

### Usage

    bpvec(X, FUN, ..., AGGREGATE=c, BPREDO=list(), BPPARAM=bpparam())

### Arguments

`X` | Any object for which methods `length` and `[` are implemented.
`FUN` | A function to be applied to subsets of `X`. The relationship between `X` and `FUN(X)` is 1:1, so that `length(FUN(X, ...)) == length(X)`. The return value of separate calls to `FUN` are concatenated with `AGGREGATE`.
`...` | Additional arguments for `FUN`.
`AGGREGATE` | A function taking any number of arguments `...` called to reduce results (elements of the `...` argument of `AGGREGATE` from parallel jobs. The default, `c`, concatenates objects and is appropriate for vectors; `rbind` might be appropriate for data frames.
`BPPARAM` | An optional `BiocParallelParam` instance determining the parallel back-end to be used during evaluation, or a `list` of `BiocParallelParam` instances, to be applied in sequence for nested calls to BiocParallel functions.
`BPREDO` | A `list` of output from `bpvec` with one or more failed elements. When a list is given in `BPREDO`, `bpok` is used to identify errors, tasks are rerun and inserted into the original results.

### Details

This method creates a vector of indices for `X` that divide the elements as evenly as possible given the number of `bpworkers()` and `bptasks()` of `BPPARAM`. Indices and data are passed to `bplapply` for parallel evaluation.

The distinction between `bpvec` and `bplapply` is that `bplapply` applies `FUN` to each element of `X` separately whereas `bpvec` assumes the function is vectorized, e.g., `c(FUN(x[1]), FUN(x[2]))` is equivalent to `FUN(x[1:2])`. This approach can be more efficient than `bplapply` but requires the assumption that `FUN` takes a vector input and creates a vector output of the same length as the input which does not depend on partitioning of the vector. This behavior is consistent with `parallel:::pvec` and the `?pvec` man page should be consulted for further details.

### Value

The result should be identical to `FUN(X, ...)` (assuming that `AGGREGATE` is set appropriately).

When evaluation of individual elements of `X` results in an error, the result is a `list` with the same geometry (i.e., `lengths()`) as the split applied to `X` to create chunks for parallel evaluation; one or more elements of the list contain a `bperror` element, indicting that the vectorized calculation failed for at least one of the index values in that chunk.

An error is also signaled when `FUN(X)` does not return an object of the same length as `X`; this condition is only detected when the number of elements in `X` is greater than the number of workers.

### Author(s)

Martin Morgan mailto:mtmorgan@fhcrc.org.

### See Also

`bplapply` for parallel lapply.

`BiocParallelParam` for possible values of `BPPARAM`.

`pvec` for background.

### Examples

    showMethods("bpvec")

    ## ten tasks (1:10), called with as many back-end elements are specified
    ## by BPPARAM.  Compare with bplapply
    fun <- function(v) {
        message("working")
        sqrt(v)
    }
    system.time(result <- bpvec(1:10, fun))
    result

    ## invalid FUN -- length(class(X)) is not equal to length(X)
    bptry(bpvec(1:2, class, BPPARAM=SerialParam()))

[Package BiocParallel version 1.14.2 Index]