| Title: | Benchmarking routine for R |
|---|---|
| Description: | rbenchmark is inspired by the Perl module Benchmark, and is intended to facilitate benchmarking of arbitrary R code. The library consists of just one function, benchmark, which is a simple wrapper around system.time. Given a specification of the benchmarking process (counts of replications, evaluation environment) and an arbitrary number of expressions, benchmark evaluates each of the expressions in the specified environment, replicating the evaluation as many times as specified, and returning the results conveniently wrapped into a data frame. |
| Authors: | Wacek Kusnierczyk <[email protected]> |
| Maintainer: | Wacek Kusnierczyk <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 1.0.0 |
| Built: | 2024-11-16 06:45:36 UTC |
| Source: | CRAN |
benchmark is a simple wrapper around system.time.
Given a specification of the benchmarking process (counts of replications, evaluation environment) and an arbitrary number of expressions, benchmark evaluates each of the expressions in the specified environment, replicating the evaluation as many times as specified, and returning the results conveniently wrapped into a data frame.
benchmark( ..., columns = c( "test", "replications", "elapsed", "relative", "user.self", "sys.self", "user.child", "sys.child"), order = "test", replications = 100, environment = parent.frame(), relative = "elapsed")benchmark( ..., columns = c( "test", "replications", "elapsed", "relative", "user.self", "sys.self", "user.child", "sys.child"), order = "test", replications = 100, environment = parent.frame(), relative = "elapsed")
... |
captures any number of unevaluated expressions passed to benchmark as named or unnamed arguments. |
columns |
a character or integer vector specifying which columns should be included in the returned data frame (see below). |
order |
a character or integer vector specifying which columns should be used to sort the output data frame. Any of the columns that can be specified for |
replications |
a numeric vector specifying how many times an expression should be evaluated when the runtime is measured. If |
environment |
the environment in which the expressions will be evaluated. |
relative |
the name or index of the column whose values will be used to compute relative timings (see below). If |
The parameters columns, order, replications, and environment are optional and have the following default values:
columns = c('test', 'replications', 'elapsed', 'relative', 'user.self', 'sys.self', 'user.child', 'sys.child')
By default, the returned data frame will contain all columns generated internally in benchmark. These named columns will contain the following data:
test: a character string naming each individual benchmark. If the corresponding expression was passed to benchmark in a named argument, the name will be used; otherwise, the expression itself converted to a character string will be used.
replications: a numeric vector specifying the number of replications used within each individual benchmark.
elapsed, user.self, sys.self, user.child, and sys.child are columns containing values reported by system.time; see Sec. 7.1 Operating system access in The R language definition, or see system.time.
relative: a column containing benchmark values relative to the shortest benchmark value. The benchmark values used in this computation are taken from the column specified with the relative argument.
order = 'test'
By default, the data frame is sorted by the column test (the labels of the expressions or the expressions themselves; see above).
replications = 100
By default, each expression will be benchmarked once, and will be evaluated 100 times within the benchmark.
environment = parent.frame()
By default, all expressions will be evaluated in the environment in which the call to benchmark is made.
relative = 'elapsed'
By default, relative timings are given based on values from the column 'elapsed'.
The value returned from a call to benchmark is a data frame with rows corresponding to individual benchmarks, and columns as specified above.
An individual benchmark corresponds to a unique combination (see below) of an expression from ... and a replication count from replications; if there are n expressions in ... and m replication counts in replication, the returned data frame will consist of n*m rows, each corresponding to an individual, independent (see below) benchmark.
If either ... or replications contain duplicates, the returned data frame will contain multiple benchmarks for the involved expression-replication combinations. Note that such multiple benchmarks for a particular expression-replication pair will, in general, have different timing results, since they will be evaluated independently (unless the expressions perform side effects that can influence each other's performance).
Not all expressions, if passed as unnamed arguments, will be cast to character strings as you might expect:
benchmark({x = 5; 1:x^x})
# the benchmark will be named '{'
benchmark performs no smart argument-parameter matching. Any named argument whose name is not exactly 'replications', 'environment', 'columns', or 'order' will be treated as an expression to be benchmarked:
benchmark(1:10^5, repl=1000) # there will be a benchmark named 'repl'
See <http://code.google.com/p/rbenchmark> for more details.
Wacek Kusnierczyk <mailto:[email protected]>
library(rbenchmark) # Example 1 # Benchmarking the allocation of one 10^6-element numeric vector, # by default replicated 100 times benchmark(1:10^6) # simple test functions used in subsequent examples random.array = function(rows, cols, dist=rnorm) array(dist(rows*cols), c(rows, cols)) random.replicate = function(rows, cols, dist=rnorm) replicate(cols, dist(rows)) # Example 2 # Benchmarking an expression multiple times with the same replication count, # output with selected columns only benchmark(replications=rep(100, 3), random.array(100, 100), random.array(100, 100), columns=c('test', 'elapsed', 'replications')) # Example 3 # Benchmarking two named expressions with three different replication # counts, output sorted by test name and replication count, # with additional column added after the benchmark within(benchmark(rep=random.replicate(100, 100), arr=random.array(100, 100), replications=10^(1:3), columns=c('test', 'replications', 'elapsed'), order=c('test', 'replications')), { average = elapsed/replications }) # Example 4 # Benchmarking a list of arbitrary predefined expressions tests = list(rep=expression(random.replicate(100, 100)), arr=expression(random.array(100, 100))) do.call(benchmark, c(tests, list(replications=100, columns=c('test', 'elapsed', 'replications'), order='elapsed')))library(rbenchmark) # Example 1 # Benchmarking the allocation of one 10^6-element numeric vector, # by default replicated 100 times benchmark(1:10^6) # simple test functions used in subsequent examples random.array = function(rows, cols, dist=rnorm) array(dist(rows*cols), c(rows, cols)) random.replicate = function(rows, cols, dist=rnorm) replicate(cols, dist(rows)) # Example 2 # Benchmarking an expression multiple times with the same replication count, # output with selected columns only benchmark(replications=rep(100, 3), random.array(100, 100), random.array(100, 100), columns=c('test', 'elapsed', 'replications')) # Example 3 # Benchmarking two named expressions with three different replication # counts, output sorted by test name and replication count, # with additional column added after the benchmark within(benchmark(rep=random.replicate(100, 100), arr=random.array(100, 100), replications=10^(1:3), columns=c('test', 'replications', 'elapsed'), order=c('test', 'replications')), { average = elapsed/replications }) # Example 4 # Benchmarking a list of arbitrary predefined expressions tests = list(rep=expression(random.replicate(100, 100)), arr=expression(random.array(100, 100))) do.call(benchmark, c(tests, list(replications=100, columns=c('test', 'elapsed', 'replications'), order='elapsed')))
rbenchmark is inspired by the Perl module Benchmark, and is intended to facilitate benchmarking of arbitrary R code.
The library consists of just one function, benchmark, which is a simple wrapper around system.time.
Given a specification of the benchmarking process (counts of replications, evaluation environment) and an arbitrary number of expressions, benchmark evaluates each of the expressions in the specified environment, replicating the evaluation as many times as specified, and returning the results conveniently wrapped into a data frame.
| Package: | benchmark |
| Type: | Package |
| Version: | 1.0.0 |
| Date: | 2012-08-30 |
| License: GPL-2 LazyLoad: | yes |
Wacek Kusnierczyk
Maintainer: Wacek Kusnierczyk <[email protected]>
Contributors: Dirk Eddelbuettel <[email protected]>, Berend Hasselman <[email protected]>
library(rbenchmark) # Example 1 # Benchmarking the allocation of one 10^6-element numeric vector, # by default replicated 100 times benchmark(1:10^6) # simple test functions used in subsequent examples random.array = function(rows, cols, dist=rnorm) array(dist(rows*cols), c(rows, cols)) random.replicate = function(rows, cols, dist=rnorm) replicate(cols, dist(rows)) # Example 2 # Benchmarking an expression multiple times with the same replication count, # output with selected columns only benchmark(replications=rep(100, 3), random.array(100, 100), random.array(100, 100), columns=c('test', 'elapsed', 'replications')) # Example 3 # Benchmarking two named expressions with three different replication # counts, output sorted by test name and replication count, # with additional column added after the benchmark within(benchmark(rep=random.replicate(100, 100), arr=random.array(100, 100), replications=10^(1:3), columns=c('test', 'replications', 'elapsed'), order=c('test', 'replications')), { average = elapsed/replications }) # Example 4 # Benchmarking a list of arbitrary predefined expressions tests = list(rep=expression(random.replicate(100, 100)), arr=expression(random.array(100, 100))) do.call(benchmark, c(tests, list(replications=100, columns=c('test', 'elapsed', 'replications'), order='elapsed')))library(rbenchmark) # Example 1 # Benchmarking the allocation of one 10^6-element numeric vector, # by default replicated 100 times benchmark(1:10^6) # simple test functions used in subsequent examples random.array = function(rows, cols, dist=rnorm) array(dist(rows*cols), c(rows, cols)) random.replicate = function(rows, cols, dist=rnorm) replicate(cols, dist(rows)) # Example 2 # Benchmarking an expression multiple times with the same replication count, # output with selected columns only benchmark(replications=rep(100, 3), random.array(100, 100), random.array(100, 100), columns=c('test', 'elapsed', 'replications')) # Example 3 # Benchmarking two named expressions with three different replication # counts, output sorted by test name and replication count, # with additional column added after the benchmark within(benchmark(rep=random.replicate(100, 100), arr=random.array(100, 100), replications=10^(1:3), columns=c('test', 'replications', 'elapsed'), order=c('test', 'replications')), { average = elapsed/replications }) # Example 4 # Benchmarking a list of arbitrary predefined expressions tests = list(rep=expression(random.replicate(100, 100)), arr=expression(random.array(100, 100))) do.call(benchmark, c(tests, list(replications=100, columns=c('test', 'elapsed', 'replications'), order='elapsed')))