benchr: High Precise Measurement of R Expressions Execution Time

Description Details Package options Author(s) See Also Examples


Package benchr provides an infrastructure (framework) for precise measurement of R expressions execution time.


To measure execution time, benchr provides function benchmark(), as well as a number of additional methods for analysis and representation of results.

For precise time measurement we use a cross-platform monotone clock, provided by C++11 standard in header file chrono. The timer accuracy depends on the implementation by the compiler in use, the OS and the hardware. Most commonly, the precision is one micro- or nanosecond. We provide the opportunity to get the timer accuracy (time interval between two consecutive timer ticks) via function timer_precision(). This accuracy is also listed in the output of implicit or explicit print call.

We estimate the timer overhead before the actual measurement by running multiple (2*10^5 by default) calls to an empty function. By doing that, we not only estimate the overhead, but also produce a warm-up effect on the processor, taking it out from idle state. After the actual measurement results are adjusted by the timer overhead.

Time intervals are measured in seconds and stored as long double, which lets us capture a wide range of possible values from .Machine$double.xmin to .Machine$double.xmax. This is quite enough to operate both within very small (nanoseconds) and very big time frames (e.g. the maximum time interval possible is .Machine$double.xmax / 3600 hours).

It should be noted that the R session is not an isolated container with strictly bounded resources, therefore the execution time can be influenced by various factors, which may lead to outliers. In order to increase measurement reliability, we repeat executions multiple times (100 repetitions for each expression by default). This approach allows to collect enough data for statistical analysis in time difference.

We have also implemented several execution regimes in order to minimize the probability of systematic errors in measurements. By default, a random order of execution is being used. There is also a block order of execution, when the first expression is repeated a fixed number of times, then the second and so on. In such regime one can decrease the influence of allocators and garbage collection, since the memory is allocated only at the beginning of each block. The third option is to execute expressions in the order, provided by the user.

Note that we do not make any checks regarding return objects, i.e. one can compare not only algorithms with the same result, but also the same algorithm with different input parameters (e.g. input data sets of different size).

We also do not check whether the expressions are language objects (see is.language()) and do not coerce to that type.

Package options

accessible through function arguments. We allow to modify these parameters via package options. We have tried to set optimal default values, which you may consider changing in some cases. Here's a complete list of package options:


Number of iterations for timer overhead estimation (2*10^5 by default).


Whether additional information on the measurement parameters should be displayed (FALSE by default).


Whether ggplot2 package should be used to produce plots, if the package is installed (TRUE by default).


Maintainer: Artem Klevtsov (ORCID)

Other contributors:

See Also

Useful links:


# Benchmark expressions
res <- benchmark(
  rep(1:10, each = 10),,, 10))
# Aggregated statistics
# Plot results

Example output

0%   10   20   30   40   50   60   70   80   90   100%
Time units : microseconds 
                           expr n.eval mean trimmed relative
           rep(1:10, each = 10)    100 4.79    4.66  3.70  9.96     1.03,, 10))    100 4.66    4.43  2.63 18.40     1.00
Time units : microseconds 
                           expr n.eval  min lw.qu median mean up.qu   max total
           rep(1:10, each = 10)    100 3.70  4.04   4.27 4.79  5.17  9.96   479,, 10))    100 2.63  3.28   3.86 4.66  5.39 18.40   466

benchr documentation built on March 13, 2020, 3:29 a.m.