aa_reduce-method: A Rank Receive a Reduction of Objects from Every Rank

In RBigData/pbdMPI: R Interface to MPI for HPC Clusters (Programming with Big Data Project)

A Rank Receive a Reduction of Objects from Every Rank

Description

This method lets a rank receive a reduction of objects from every rank in the same communicator based on a given operation. The default return is an object as the input.

Usage

reduce(x, x.buffer = NULL, op = .pbd_env$SPMD.CT$op,
       rank.dest = .pbd_env$SPMD.CT$rank.source,
       comm = .pbd_env$SPMD.CT$comm)

Arguments

x	an object to be gathered from all ranks.
x.buffer	a buffer to hold the return object which probably has x with the same type of x.
op	a reduction operation applied on combine all x.
rank.dest	a rank of destination where all x reduce to.
comm	a communicator number.

Details

By default, the object is reduced to .pbd_env$SPMD.CT$rank.source, i.e. rank 0L.

All x on all ranks are likely presumed to have the same size and type.

x.buffer can be NULL or unspecified. If specified, the type should be either integer or double specified correctly according to the type of x.

See methods{"reduce"} for S4 dispatch cases and the source code for further details.

Value

The reduced object of the same type as x is returned by default.

Author(s)

Wei-Chen Chen [e-mail], George Ostrouchov, Drew Schmidt, Pragneshkumar Patel, and Hao Yu.

References

Programming with Big Data in R Website: https://pbdr.org/

See Also

allgather(), gather(), reduce().

Examples

### Save code in a file "demo.r" and run with 2 processors by
### SHELL> mpiexec -np 2 Rscript demo.r

spmd.code <- "
### Initial.
suppressMessages(library(pbdMPI, quietly = TRUE))
init()
.comm.size <- comm.size()
.comm.rank <- comm.rank()

### Examples.
N <- 5
x <- (1:N) + N * .comm.rank
y <- reduce(matrix(x, nrow = 1), op = \"sum\")
comm.print(y)

y <- reduce(x, double(N), op = \"prod\")
comm.print(y)

x <- as.logical(round(runif(N)))
y <- reduce(x, logical(N), op = \"land\")
comm.print(y)

### Finish.
finalize()
"
pbdMPI::execmpi(spmd.code = spmd.code, nranks = 2L)

RBigData/pbdMPI documentation built on Jan. 31, 2024, 10:34 p.m.