ddmatrix-constructors: Distributed Matrix Creation

In pbdDMAT: 'pbdR' Distributed Matrix Methods

Description

Methods for simple construction of distributed matrices.

Usage

ddmatrix(data, ...)

## S4 method for signature 'ddmatrix'
ddmatrix(data, nrow = 1, ncol = 1,
  byrow = FALSE, ..., bldim = .pbd_env$BLDIM, ICTXT = .pbd_env$ICTXT)

## S4 method for signature 'missing'
ddmatrix(data, nrow = 1, ncol = 1, byrow = FALSE,
  ..., bldim = .pbd_env$BLDIM, ICTXT = .pbd_env$ICTXT)

## S4 method for signature 'vector'
ddmatrix(data, nrow = 1, ncol = 1, byrow = FALSE,
  ..., bldim = .pbd_env$BLDIM, ICTXT = .pbd_env$ICTXT)

## S4 method for signature 'matrix'
ddmatrix(data, nrow = 1, ncol = 1, byrow = FALSE,
  ..., bldim = .pbd_env$BLDIM, ICTXT = .pbd_env$ICTXT)

## S4 method for signature 'character'
ddmatrix(data, nrow = 1, ncol = 1,
  byrow = FALSE, ..., min = 0, max = 1, mean = 0, sd = 1,
  rate = 1, shape, scale = 1, bldim = .pbd_env$BLDIM,
  ICTXT = .pbd_env$ICTXT)

ddmatrix.local(data, ...)

## S4 method for signature 'missing'
ddmatrix.local(data, nrow = 1, ncol = 1,
  byrow = FALSE, ..., bldim = .pbd_env$BLDIM, ICTXT = .pbd_env$BLDIM)

## S4 method for signature 'vector'
ddmatrix.local(data, nrow = 1, ncol = 1,
  byrow = FALSE, ..., bldim = .pbd_env$BLDIM, ICTXT = .pbd_env$ICTXT)

## S4 method for signature 'matrix'
ddmatrix.local(data, nrow = 1, ncol = 1,
  byrow = FALSE, ..., bldim = .pbd_env$BLDIM, ICTXT = .pbd_env$ICTXT)

## S4 method for signature 'character'
ddmatrix.local(data, nrow = 1, ncol = 1,
  byrow = FALSE, ..., min = 0, max = 1, mean = 0, sd = 1,
  rate = 1, shape, scale = 1, bldim = .pbd_env$BLDIM,
  ICTXT = .pbd_env$ICTXT)

Arguments

data	A global value: a string (for random generation) or an optional data vector. In the data vector case, the data should be the same across all processes.
...	Extra arguments
nrow	number of rows. Global rows for ddmatrix(). Local rows for ddmatrix.local(). See details below.
ncol	number of columns. Global columns for ddmatrix(). Local columns for ddmatrix.local(). See details below.
byrow	logical. If FALSE then the distributed matrix will be filled by column major storage, otherwise row-major.
bldim	blocking dimension.
ICTXT	BLACS context number.
min, max	Min and max values for random uniform generation.
mean, sd	Mean and standard deviation for random normal generation.
rate	Rate for random exponential generation.
shape, scale	Shape and scale parameters for random weibull generation.

Details

These methods are simplified methods of creating distributed matrices, including random ones. These methods involve only local computations, i.e., no communication is performed in the construction of a ddmatrix using these methods (in contrast to using as.ddmatrix() et al).

For non-character inputs, the methods attempt to mimic R as closely as possible. So ddmatrix(1:3, 5, 7) produces the distributed analogue of matrix(1:3, 5, 7).

For character inputs, you may also specify additional parametric family information.

The functions predicated with .local generate data with a fixed local dimension, i.e., each processor gets an identical amount of data. Likewise, the remaining functions generate a fixed global amount of data, and each processor may or may not have an identical amount of local data.

To ensure good random number generation, you should only consider using the character methods with the comm.set.seed() function from pbdMPI which uses the method of L'Ecuyer via the rlecuyer package.

Value

Returns a distributed matrix.

pbdDMAT documentation built on May 1, 2019, 6:34 p.m.