pmm: Parallelized Matrix Multiplication
In georob: Robust Geostatistical Analysis of Spatial Data

Description Usage Arguments Details Value Author(s) See Also Examples

View source: R/georob_exported_functions.R

This page documents the function pmm for parallelized matrix multiplication and the function
control.pcmp, which controls the behaviour of pmm and other functions that execute tasks in parallel.

pmm(A, B, control = control.pcmp())

control.pcmp(pmm.ncores = 1, gcr.ncores = 1, max.ncores = detectCores(),
    f = 1, sfstop = FALSE, allow.recursive = TRUE,
    fork = !identical(.Platform[["OS.type"]], "windows"), ...)

`A, B`	matrices to be multiplied.
`control`	a list of with the arguments `pmm.ncores`, `gcr.ncores`, `max.ncores`, `f`, `sfstop` and `allow.recursive` or a function such as `control.pcmp` that generates such a list.
`pmm.ncores`	number (integer, default 1) of cores used for parallelized matrix multiplication.
`gcr.ncores`	number (integer, default 1) of cores used for parallelized computation of semi-variance matrix.
`max.ncores`	maximum number of cores (integer, default all cores of a machine) used for parallelized computations.
`f`	number (integer, default 2) of tasks assigned to each core in parallelized operations.
`sfstop`	logical controlling whether the SNOW socket cluster is stopped after each parallelized matrix multiplication on windows OS (default `FALSE`).
`allow.recursive`	logical controlling whether nested parallelized computation should be allowed (default `TRUE`).
`fork`	logical controlling whether forking should be used for parallel computations (default `TRUE` on unix and `FALSE` on windows operating systems). Note that settting `fork == TRUE` on windows suppresses parallel computations.
`...`	further arguments, currently not used.

Parallelized matrix multiplication shortens computing time for large data sets (n>1000). However, spawning child processes requires itself resources and increasing the number of cores for parallel matrix multiplication does not always result in reduced computing time. A sensible default for the number of cores is likely pmm.ncores=2.

Note, however, that very substantial reductions in computing time results when one uses the OpenBLAS library instead of the reference BLAS library that ships with R, see https://www.openblas.net/ and R FAQ for your OS. With OpenBLAS no gains are obtained by using more than one core for matrix multiplication, and one should therefore use the default argument pmm.ncores = 1 for control.pcmp().

max.ncores controls how many child processes are spawned in total. This can be used to prevent that child processes spawn themselves children which may result in a considerable number of child processes.

pmm:: the matrix product A %*% B,
control.pcmp:: a list with components pmm.ncores, gcr.ncores, max.ncores, f, sfstop,
allow.recursive.

Andreas Papritz andreas.papritz@env.ethz.ch.

georobIntro for a description of the model and a brief summary of the algorithms;

georob for (robust) fitting of spatial linear models.

## Not run: 
A <- as.matrix(dist(rnorm(2000)))
B <- as.matrix(dist(rnorm(2000)))
system.time(C <- pmm(A, B, control = control.pcmp(pmm.ncores = 1)))
## using 4 cores
## system.time(C <- pmm(A, B, control = control.pcmp(pmm.ncores = 4)))

## End(Not run)