Sub-millisecond accurate timing of expression evaluation.
microbenchmark serves as a more accurate replacement of the
often seen system.time(replicate(1000, expr))
expression. It tries hard to accurately measure only the time it
takes to evaluate expr. To achieved this, the
sub-millisecond (supposedly nanosecond) accurate timing functions
most modern operating systems provide are used. Additionally all
evaluations of the expressions are done in C code to minimize any
overhead.
microbenchmark(..., list = NULL, times = 100L, unit, check = NULL, control = list(), setup = NULL)
... |
Expressions to benchmark. |
list |
List of unevaluated expression to benchmark. |
times |
Number of times to evaluate the expression. |
unit |
Default unit used in |
check |
A function to check if the expressions are equal. By default |
control |
List of control arguments. See Details. |
setup |
An unevaluated expression to be run (untimed) before each benchmark expression. |
This function is only meant for micro-benchmarking small pieces of source code and to compare their relative performance characteristics. You should generally avoid benchmarking larger chunks of your code using this function. Instead, try using the R profiler to detect hot spots and consider rewriting them in C/C++ or FORTRAN.
The control list can contain the following entries:
the order in which the expressions are evaluated. “random” (the default) randomizes the execution order, “inorder” executes each expression in order and “block” executes all repetitions of each expression as one block.
the number of warm-up iterations performed before the actual benchmark. These are used to estimate the timing overhead as well as spinning up the processor from any sleep or idle states it might be in. The default value is 2.
Object of class ‘microbenchmark’, a data frame with
columns expr and time. expr contains the
deparsed expression as passed to microbenchmark or the name
of the argument if the expression was passed as a named
argument. time is the measured execution time of the
expression in nanoseconds. The order of the observations in the
data frame is the order in which they were executed.
Depending on the underlying operating system, different
methods are used for timing. On Windows the
QueryPerformanceCounter interface is used to measure the
time passed. For Linux the clock_gettime API is used and on
Solaris the gethrtime function. Finally on MacOS X the,
undocumented, mach_absolute_time function is used to avoid
a dependency on the CoreServices Framework.
Before evaluating each expression times times, the overhead
of calling the timing functions and the C function call overhead
are estimated. This estimated overhead is subtracted from each
measured evaluation time. Should the resulting timing be negative,
a warning is thrown and the respective value is replaced by
0. If the timing is zero, a warning is raised.
Should all evaluations result in one of the two error conditions described above, an error is raised.
One platform on which the clock resolution is known to be too low to measure short runtimes with the required precision is Oracle® Solaris on some SPARC® hardware. Reports of other platforms with similar problems are welcome. Please contact the package maintainer.
Olaf Mersmann
print.microbenchmark to display and
boxplot.microbenchmark or
autoplot.microbenchmark to plot the results.
## Measure the time it takes to dispatch a simple function call
## compared to simply evaluating the constant \code{NULL}
f <- function() NULL
res <- microbenchmark(NULL, f(), times=1000L)
## Print results:
print(res)
## Plot results:
boxplot(res)
## Pretty plot:
if (requireNamespace("ggplot2")) {
ggplot2::autoplot(res)
}
## Example check usage
my_check <- function(values) {
all(sapply(values[-1], function(x) identical(values[[1]], x)))
}
f <- function(a, b)
2 + 2
a <- 2
## Check passes
microbenchmark(2 + 2, 2 + a, f(2, a), f(2, 2), check=my_check)
## Not run:
a <- 3
## Check fails
microbenchmark(2 + 2, 2 + a, f(2, a), f(2, 2), check=my_check)
## End(Not run)
## Example setup usage
set.seed(21)
x <- rnorm(10)
microbenchmark(x, rnorm(10), check=my_check, setup=set.seed(21))
## Will fail without setup
## Not run:
microbenchmark(x, rnorm(10), check=my_check)
## End(Not run)
## using check
a <- 2
microbenchmark(2 + 2, 2 + a, sum(2, a), sum(2, 2), check='identical')
microbenchmark(2 + 2, 2 + a, sum(2, a), sum(2, 2), check='equal')
attr(a, 'abc') <- 123
microbenchmark(2 + 2, 2 + a, sum(2, a), sum(2, 2), check='equivalent')
## check='equal' will fail due to difference in attribute
## Not run:
microbenchmark(2 + 2, 2 + a, sum(2, a), sum(2, 2), check='equal')
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.