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michaelweylandtmichaelweylandt
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Condition benchmarking on microbenchmark package
- Move benchmark script into a `if(require("microbenchmark"))` block so script works even if microbenchmark is not installed - Add microbenchmark to `Suggests` - Add r-cran-microbenchmark to .travis.yml
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‎.travis.yml‎

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@@ -13,9 +13,9 @@ before_install:
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- ./run.sh bootstrap
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install:
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- ./run.sh install_aptget r-cran-rcpp r-cran-matrix r-cran-inline r-cran-runit r-cran-pkgkitten
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- ./run.sh install_aptget r-cran-rcpp r-cran-matrix r-cran-inline r-cran-runit r-cran-pkgkitten r-cran-microbenchmark
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script:
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script:
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- ./run.sh run_tests
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#after_success:
@@ -28,4 +28,3 @@ notifications:
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email:
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on_success: change
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on_failure: change
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‎DESCRIPTION‎

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@@ -25,6 +25,6 @@ Depends: R (>= 2.15.1)
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LazyLoad: yes
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LinkingTo: Rcpp
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Imports: Matrix (>= 1.1-0), Rcpp (>= 0.11.0), stats, utils
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Suggests: inline, RUnit, pkgKitten
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Suggests: inline, RUnit, pkgKitten, microbenchmark
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URL: http://dirk.eddelbuettel.com/code/rcpp.eigen.html
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BugReports: https://github.com/RcppCore/RcppEigen/issues

‎inst/examples/lmBenchmark.R‎

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@@ -5,77 +5,78 @@
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## This file is part of RcppEigen.
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require("stats", character=TRUE, quietly=TRUE)
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require("microbenchmark", character=TRUE, quietly=TRUE)
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require("RcppEigen", character=TRUE, quietly=TRUE)
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## define different versions of lm
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exprs <- list()
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## These versions use rank-revealing decompositions and thus can
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## handle rank-deficient cases.
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# default version used in lm()
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exprs["lm.fit"] <- alist(stats::lm.fit(mm, y))
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# versions from RcppEigen
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## column-pivoted QR decomposition - similar to lm.fit
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exprs["PivQR"] <- alist(RcppEigen::fastLmPure(mm, y, 0L))
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## LDLt Cholesky decomposition with rank detection
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exprs["LDLt"] <- alist(RcppEigen::fastLmPure(mm, y, 2L))
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## SVD using the Lapack subroutine dgesdd and Eigen support
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exprs["GESDD"] <- alist(RcppEigen::fastLmPure(mm, y, 6L))
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## SVD (the JacobiSVD class from Eigen)
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exprs["SVD"] <- alist(RcppEigen::fastLmPure(mm, y, 4L))
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## eigenvalues and eigenvectors of X'X
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exprs["SymmEig"] <- alist(RcppEigen::fastLmPure(mm, y, 5L))
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## Non-rank-revealing decompositions. These work fine except when
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## they don't.
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## Unpivoted QR decomposition
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exprs["QR"] <- alist(RcppEigen::fastLmPure(mm, y, 1L))
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## LLt Cholesky decomposition
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exprs["LLt"] <- alist(RcppEigen::fastLmPure(mm, y, 3L))
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if (suppressMessages(require("RcppArmadillo", character=TRUE, quietly=TRUE))) {
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exprs["arma"] <- alist(RcppArmadillo::fastLmPure(mm, y))
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}
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if(require("microbenchmark", character=TRUE, quietly=TRUE)){
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if (suppressMessages(require("RcppGSL", character=TRUE, quietly=TRUE))) {
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exprs["GSL"] <- alist(RcppGSL::fastLmPure(mm, y))
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}
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## define different versions of lm
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exprs <- list()
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do_bench <- function(n=100000L, p=40L, nrep=20L, suppressSVD=(n > 100000L)) {
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mm <- cbind(1, matrix(rnorm(n * (p - 1L)), nc=p-1L))
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y <- rnorm(n)
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if (suppressSVD) exprs <- exprs[!names(exprs) %in% c("SVD", "GSL")]
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cat("lm benchmark for n = ", n, " and p = ", p, ": nrep = ", nrep, "\n", sep='')
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mb <- microbenchmark(list=exprs, times = nrep)
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op <- options(microbenchmark.unit="relative")
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on.exit(options(op))
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mb_relative <- summary(mb)
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levels(mb_relative$expr) <- names(exprs)
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options(microbenchmark.unit=NULL)
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mb_absolute <- summary(mb)
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levels(mb_absolute$expr) <- names(exprs)
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mb_combined <- merge(mb_relative[, c("expr", "median")],
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mb_absolute[, c("expr", "median")],
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by="expr")
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colnames(mb_combined) <- c("Method",
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"Relative",
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paste0("Elapsed (", attr(mb_absolute, "unit"), ")"))
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mb_combined[order(mb_combined$Relative),]
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}
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## These versions use rank-revealing decompositions and thus can
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## handle rank-deficient cases.
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# default version used in lm()
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exprs["lm.fit"] <- alist(stats::lm.fit(mm, y))
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# versions from RcppEigen
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## column-pivoted QR decomposition - similar to lm.fit
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exprs["PivQR"] <- alist(RcppEigen::fastLmPure(mm, y, 0L))
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## LDLt Cholesky decomposition with rank detection
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exprs["LDLt"] <- alist(RcppEigen::fastLmPure(mm, y, 2L))
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## SVD using the Lapack subroutine dgesdd and Eigen support
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exprs["GESDD"] <- alist(RcppEigen::fastLmPure(mm, y, 6L))
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## SVD (the JacobiSVD class from Eigen)
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exprs["SVD"] <- alist(RcppEigen::fastLmPure(mm, y, 4L))
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## eigenvalues and eigenvectors of X'X
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exprs["SymmEig"] <- alist(RcppEigen::fastLmPure(mm, y, 5L))
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## Non-rank-revealing decompositions. These work fine except when
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## they don't.
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## Unpivoted QR decomposition
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exprs["QR"] <- alist(RcppEigen::fastLmPure(mm, y, 1L))
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## LLt Cholesky decomposition
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exprs["LLt"] <- alist(RcppEigen::fastLmPure(mm, y, 3L))
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if (suppressMessages(require("RcppArmadillo", character=TRUE, quietly=TRUE))) {
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exprs["arma"] <- alist(RcppArmadillo::fastLmPure(mm, y))
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}
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print(do_bench())
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if (suppressMessages(require("RcppGSL", character=TRUE, quietly=TRUE))) {
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exprs["GSL"] <- alist(RcppGSL::fastLmPure(mm, y))
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}
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sessionInfo()
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do_bench <- function(n=100000L, p=40L, nrep=20L, suppressSVD=(n > 100000L)) {
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mm <- cbind(1, matrix(rnorm(n * (p - 1L)), nc=p-1L))
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y <- rnorm(n)
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if (suppressSVD) exprs <- exprs[!names(exprs) %in% c("SVD", "GSL")]
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.Call("RcppEigen_eigen_version", FALSE, PACKAGE="RcppEigen")
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cat("lm benchmark for n = ", n, " and p = ", p, ": nrep = ", nrep, "\n", sep='')
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cat("RcppEigen: Included Eigen version", paste(RcppEigen:::eigen_version(FALSE), collapse="."), "\n")
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cat("RcppEigen: Eigen SSE support", RcppEigen:::Eigen_SSE(), "\n")
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.Call("RcppEigen_Eigen_SSE", PACKAGE="RcppEigen")
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mb <- microbenchmark(list=exprs, times = nrep)
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60+
op <- options(microbenchmark.unit="relative")
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on.exit(options(op))
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mb_relative <- summary(mb)
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levels(mb_relative$expr) <- names(exprs)
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options(microbenchmark.unit=NULL)
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mb_absolute <- summary(mb)
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levels(mb_absolute$expr) <- names(exprs)
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mb_combined <- merge(mb_relative[, c("expr", "median")],
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mb_absolute[, c("expr", "median")],
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by="expr")
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colnames(mb_combined) <- c("Method",
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"Relative",
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paste0("Elapsed (", attr(mb_absolute, "unit"), ")"))
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mb_combined[order(mb_combined$Relative),]
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}
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print(do_bench())
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}

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