R/benchmark_matrix_functions.R

In csgillespie/benchmarkme: Crowd Sourced System Benchmarks

#' Matrix function benchmarks
#'
#' @description A collection of matrix benchmark functions
#' \itemize{
#' \item FFT over 2,500,000 random values.
#' \item Eigenvalues of a 640x640 random matrix.
#' \item Determinant of a 2500x2500 random matrix.
#' \item Cholesky decomposition of a 3000x3000 matrix.
#' \item Inverse of a 1600x1600 random matrix.
#' }
#' These benchmarks have been developed by many authors.
#' See http://r.research.att.com/benchmarks/R-benchmark-25.R
#' for a complete history. The function \code{benchmark_matrix_fun()}
#' runs the five \code{bm} functions.
#' @inheritParams benchmark_std
#' @references http://r.research.att.com/benchmarks/R-benchmark-25.R
#' @importFrom stats fft
#' @export
bm_matrix_fun_fft = function(runs = 3, verbose = TRUE) {
  b = 0
  timings = data.frame(user = numeric(runs), system = 0, elapsed = 0,
                       test = "fft", test_group = "matrix_fun",
                       stringsAsFactors = FALSE)
  for (i in 1:runs) {
    a = Rnorm(2500000)
    invisible(gc())
    timings[i, 1:3] = system.time({b <- fft(a)})[1:3] #nolint
  }
  if (verbose)
    message(c("\tFFT over 2,500,000 random values", timings_mean(timings)))
  timings
}


#' @rdname bm_matrix_fun_fft
#' @export
bm_matrix_fun_eigen = function(runs = 3, verbose = TRUE) {
  b = 0
  timings = data.frame(user = numeric(runs), system = 0, elapsed = 0,
                       test = "eigen", test_group = "matrix_fun",
                       stringsAsFactors = FALSE)
  for (i in 1:runs) {
    a = array(Rnorm(600 * 600), dim = c(600, 600))
    invisible(gc())
    timings[i, 1:3] = system.time({
      b <- eigen(a, symmetric = FALSE, only.values = TRUE)$Value})[1:3] #nolint
  }
  if (verbose)
    message(c("\tEigenvalues of a 640 x 640 random matrix", timings_mean(timings)))
  timings
}

#' @rdname bm_matrix_fun_fft
#' @export
bm_matrix_fun_determinant = function(runs = 3, verbose = TRUE) {
  b = 0
  timings = data.frame(user = numeric(runs), system = 0, elapsed = 0,
                       test = "determinant", test_group = "matrix_fun",
                       stringsAsFactors = FALSE)
  for (i in 1:runs) {
    a = Rnorm(2500 * 2500)
    dim(a) = c(2500, 2500)
    invisible(gc())
    timings[i, 1:3] = system.time({b <- det(a)})[1:3] #nolint
  }
  if (verbose)
    message(c("\tDeterminant of a 2,500 x 2,500 random matrix", timings_mean(timings)))
  timings
}

#' @importFrom methods new
#' @rdname bm_matrix_fun_fft
#' @import Matrix
#' @export
bm_matrix_fun_cholesky = function(runs = 3, verbose = TRUE) {
  timings = data.frame(user = numeric(runs), system = 0, elapsed = 0,
                       test = "cholesky", test_group = "matrix_fun",
                       stringsAsFactors = FALSE)
  for (i in 1:runs) {
    a = crossprod(new("dgeMatrix", x = Rnorm(3000 * 3000),
                       Dim = as.integer(c(3000, 3000))))
    invisible(gc())
    timings[i, 1:3] = system.time({b <- chol(a)})[1:3] #nolint
  }
  if (verbose)
    message(c("\tCholesky decomposition of a 3,000 x 3,000 matrix", timings_mean(timings)))
  timings
}

#' @rdname bm_matrix_fun_fft
#' @export
bm_matrix_fun_inverse = function(runs = 3, verbose = TRUE) {
  b = 0
  timings = data.frame(user = numeric(runs), system = 0, elapsed = 0,
                       test = "inverse", test_group = "matrix_fun",
                       stringsAsFactors = FALSE)
  for (i in 1:runs) {
    a = new("dgeMatrix", x = Rnorm(1600 * 1600), Dim = as.integer(c(1600, 1600)))
    invisible(gc())
    timings[i, 1:3] = system.time({b <- solve(a)})[1:3] #nolint
  }
  if (verbose)
    message(c("\tInverse of a 1,600 x 1,600 random matrix", timings_mean(timings)))
  timings
}