remoteCrossProdMatSelf: Distributed Crossproduct of a Rectangular Matrix with Itself

Description

remoteCrossProdMatSelf multiplies the transpose of a distributed rectangular matrix by itself. remoteCrossProdMatSelf 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

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. 2013. Parallelizing Gaussian Process Calculations in R. arXiv:1305.4886. http://arxiv.org/abs/1305.4886.

Examples

Run this code

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)
}