remoteCrossProdMatVec {bigGP}

Distributed Crossproduct of a Rectangular Matrix and a Vector

Description

remoteCrossProdMatVec multiplies the transpose of a distributed rectangular matrix by a distributed vector or matrix. The objects can both be contained within environments or ReferenceClass objects as well as the global environment on the slave processes.

Usage

remoteCrossProdMatVec(matName, inputName, outputName, matPos = '.GlobalEnv',
inputPos = '.GlobalEnv', outputPos = '.GlobalEnv', n1, n2, h1 = 1, h2 = 1)

Arguments

matName

name of the rectangular matrix, given as a character string, giving the name of the object on the slave processes.

inputName

name of the vector being multiplied by, given as a character string, giving the name of the object on the slave processes.

outputName

the name to use for resulting vector, on the slave processes.

matPos

where to look for the rectangular matrix, given as a character string (unlike get). This can indicate an environment, a list, or a ReferenceClass object.

inputPos

where to look for the input vector, 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 vector, 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 using a blocked algorithm, resulting in a distributed vector.

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. 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.

See Also

bigGP

Examples

## Not run: 
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$h_n)
remoteCalc("data", "mean", `-`, "tmp1", input1Pos = "prob", input2Pos = "prob")
remoteForwardsolve(cholName = "L", inputName = "tmp1", outputName = "tmp2", 
cholPos = "prob", n1 = n, h1 = prob$h_n)
remoteBacksolve(cholName = "L", inputName = "tmp2", outputName = "tmp3",
 cholPos = "prob", n1 = n, h1 = prob$h_n)
prob$remoteConstructCov(obs = FALSE, pred = FALSE, cross = TRUE, verbose = TRUE)
# we now have a rectangular cross-covariance matrix named 'crossC'
remoteCrossProdMatVec(matName = "crossC", inputName = "tmp3", outputName = "result", 
matPos = "prob", n1 = n, n2 = m, h1 = prob$h_n, h2 = prob$h_m)
 
result <- collectVector("result", n = n, h = prob$h_n)
}

## End(Not run)