remoteCrossProdMatSelf: Distributed Crossproduct of a Rectangular Matrix with Itself

Description

remoteCrossProdMatSelf multiplies the transpose of a distributed rectangular matrix by itself. remoteCrossProdMatSelfDiag calculates only the diagonal of the crossproduct. The objects can both be contained within environments or ReferenceClass objects as well as the global environment on the slave processes.

Usage

remoteCrossProdMatSelf(inputName, outputName, inputPos = '.GlobalEnv',
outputPos = '.GlobalEnv', n1, n2, h1 = 1, h2 = 1)
remoteCrossProdMatSelfDiag(inputName, outputName, inputPos =
'.GlobalEnv', outputPos = '.GlobalEnv', n1, n2, h1 = 1, h2 = 1)

Arguments

inputName: name of the matrix, given as a character string, giving the name of the object on the slave processes.
outputName: the name to use for resulting matrix, on the slave processes.
inputPos: where to look for the matrix, given as a character string (unlike get). This can indicate an environment, a list, or a ReferenceClass object.
outputPos: where to do the assignment of the output matrix, given as a character string (unlike assign). This can indicate an environment or a ReferenceClass object.
n1: a positive integer, the number of rows of the matrix.
n2: a positive integer, the number of columns of the matrix.
h1: a positive integer, the block replication factor, \(h\), relevant for the rows of the matrix.
h2: a positive integer, the block replication factor, \(h\), relevant for the columns of the matrix.

Details

Computes the distributed product, \(X^T X\) using a blocked algorithm, resulting in a distributed matrix.

References

Paciorek, C.J., B. Lipshitz, W. Zhuo, Prabhat, C.G. Kaufman, and R.C. Thomas. 2015. Parallelizing Gaussian Process Calculations in R. Journal of Statistical Software, 63(10), 1-23. tools:::Rd_expr_doi("10.18637/jss.v063.i10").

Paciorek, C.J., B. Lipshitz, W. Zhuo, Prabhat, C.G. Kaufman, and R.C. Thomas. 2013. Parallelizing Gaussian Process Calculations in R. arXiv:1305.4886. https://arxiv.org/abs/1305.4886.

Examples

Run this code

if (FALSE) {
if(require(fields)) {
  SN2011fe <- SN2011fe_subset
  SN2011fe_newdata <- SN2011fe_newdata_subset
  SN2011fe_mle <- SN2011fe_mle_subset
  nProc <- 3
n <- nrow(SN2011fe)
m <- nrow(SN2011fe_newdata)
nu <- 2
inputs <- c(as.list(SN2011fe), as.list(SN2011fe_newdata), nu = nu)
prob <- krigeProblem$new("prob", numProcesses = nProc, n = n, m = m,
predMeanFunction = SN2011fe_predmeanfunc, crossCovFunction =
SN2011fe_crosscovfunc,  predCovFunction = SN2011fe_predcovfunc,
meanFunction = SN2011fe_meanfunc, covFunction = SN2011fe_covfunc,
inputs = inputs, params = SN2011fe_mle$par, data = SN2011fe$flux,
packages = c("fields"))

remoteCalcChol(matName = "C", cholName = "L", matPos = "prob",
  cholPos = "prob", n = n, h = prob$h_n)
prob$remoteConstructCov(obs = FALSE, pred = FALSE, cross = TRUE, verbose = TRUE)
# we now have a rectangular cross-covariance matrix named 'crossC'
remoteForwardsolve(cholName = "L", inputName = "crossC", outputName = "tmp1", 
cholPos = "prob", inputPos = "prob", n1 = n, n2 = m, h1 = prob$h_n, h2 = prob$h_m)

remoteCrossProdMatSelf(inputName = "tmp1", outputName = "result", n1 = n,
n2 = m, h1 = prob$h_n, h2 = prob$h_m)
result <- collectTriangularMatrix("result", n = m, h = prob$h_m)

remoteCrossProdMatSelfDiag(inputName = "tmp1", outputName = "resultDiag",
n1 = n, n2 = m, h1 = prob$h_n, h2 = prob$h_m)
resultDiag <- collectVector("resultDiag", n = m, h = prob$h_m)
}
}