Rprof {utils}R Documentation

Enable Profiling of R's Execution


Enable or disable profiling of the execution of R expressions.


Rprof(filename = "Rprof.out", append = FALSE, interval = 0.02,
       memory.profiling = FALSE, gc.profiling = FALSE,
       line.profiling = FALSE, filter.callframes = FALSE,
       numfiles = 100L, bufsize = 10000L,
       event = c("default", "cpu", "elapsed"))



The file to be used for recording the profiling results. Set to NULL or "" to disable profiling.


logical: should the file be over-written or appended to?


real: distance (time interval) between samples in seconds.


logical: write memory use information to the file?


logical: record whether GC is running?


logical: write line locations to the file?


logical: filter out intervening call frames of the call tree. See the filtering out call frames section.

numfiles, bufsize

integers: line profiling memory allocation


character: profiling event, character vector of length one, "elapsed" for elapsed (real, wall-clock) time and "cpu" for CPU time, both measured in seconds. "default" is the default event on the platform, one of the two. See the ‘Details’.


Enabling profiling automatically disables any existing profiling to another or the same file.

Profiling works by writing out the call stack every interval seconds (units of the profiling event), to the file specified. Either the summaryRprof function or the wrapper script R CMD Rprof can be used to process the output file to produce a summary of the usage; use R CMD Rprof --help for usage information.

Exactly what is measured is subtle and depends on the profiling event.

With "elapsed" (the default and only supported event on Windows): it is time that the R process is running and executing an R command. It is not however just CPU time, for if readline() is waiting for input, that counts as well. It is also known as ‘elapsed’ time.

With "cpu" (the default on Unix and typically the preferred event for identifying performance bottlenecks), it is CPU time of the R process, so for example excludes time when R is waiting for input or for processes run by system to return. It may go slower than "elapsed" when the process is often waiting for I/O to finish, but it may go faster with actively computing concurrent threads (say via OpenMP) on a multi-core system.

Note that the (timing) interval cannot be too small. With "cpu", the time spent in each profiling step is currently added to the interval. With all profiling events, the computation in each profiling step causes perturbation to the observed system and biases the results. What is feasible is machine-dependent. On Linux, R requires the interval to be at least 10ms, on all other platforms at least 1ms. Shorter intervals will be rounded up with a warning.

The "default" profiling event is "elapsed" on Windows and "cpu" on Unix.

Support for "elapsed" event on Unix is new and considered experimental. To reduce the risk of missing a sample, R tries to use the (real-time) FIFO scheduling policy with the maximum scheduling priority for an internal thread which initiates collection of each sample. If setting that priority fails, it tries to use the maximum scheduling priority of the current scheduling policy, falling back to the current scheduling parameters. On Linux, regular users are typically not allowed to use the real-time scheduling priorities. This can be usually allowed via PAM (e.g. ‘/etc/security/limits.conf’), see the OS documentation for details. The priorities only matter when profiling a system under high load.

Functions will only be recorded in the profile log if they put a context on the call stack (see sys.calls). Some primitive functions do not do so: specifically those which are of type "special" (see the ‘R Internals’ manual for more details).

Individual statements will be recorded in the profile log if line.profiling is TRUE, and if the code being executed was parsed with source references. See parse for a discussion of source references. By default the statement locations are not shown in summaryRprof, but see that help page for options to enable the display.

Filtering Out Call Frames

Lazy evaluation makes the call stack more complex because intervening call frames are created between the time arguments are applied to a function, and the time they are effectively evaluated. When the call stack is represented as a tree, these intervening frames appear as sibling nodes. For instance, evaluating try(EXPR) produces the following call tree, at the time EXPR gets evaluated:

1. +-base::try(EXPR)
2. | \-base::tryCatch(...)
3. |   \-base:::tryCatchList(expr, classes, parentenv, handlers)
4. |     \-base:::tryCatchOne(expr, names, parentenv, handlers[[1L]])
5. |       \-base:::doTryCatch(return(expr), name, parentenv, handler)
6. \-EXPR

Lines 2 to 5 are intervening call frames, the last of which finally triggered evaluation of EXPR. Setting filter.callframes to TRUE simplifies the profiler output by removing all sibling nodes of intervening frames.

The same kind of call frame filtering is applied with eval() frames. When you call eval(), two frames are pushed on the stack to ensure a continuity between frames. Say we have these definitions:

calling <- function() evaluator(quote(called()), environment())
evaluator <- function(expr, env) eval(expr, env)
called <- function() EXPR()

calling() calls called() in its own environment, via eval(). The latter is called indirectly through evaluator(). The net effect of this code is identical to just calling called() directly, without the intermediaries. However, the full call stack looks like this:

1. calling()
2. \-evaluator(quote(called()), environment())
3.   \-base::eval(expr, env)
4.     \-base::eval(expr, env)
5.       \-called()
6.         \-EXPR()

When call frame filtering is turned on, the true calling environment of called() is looked up, and the filtered call stack looks like this:

1. calling()
5. \-called()
6.   \-EXPR()

If the calling environment is not on the stack, the function called by eval() becomes a root node. Say we have:

calling <- function() evaluator(quote(called()), new.env())

With call frame filtering we then get the following filtered call stack:

5. called()
6. \-EXPR()


On Unix-alikes:

Profiling is not available on all platforms. By default, support for profiling is compiled in if possible – configure R with --disable-R-profiling to change this.

As R CPU profiling uses the same mechanisms as C profiling, the two cannot be used together, so do not use Rprof(event = "cpu") (the default) in an executable built for C-level profiling (such as using the GCC option -p or -pg).

On Windows:

filename can be a UTF-8-encoded filepath that cannot be translated to the current locale.

The profiler interrupts R asynchronously, and it cannot allocate memory to store results as it runs. This affects line profiling, which needs to store an unknown number of file pathnames. The numfiles and bufsize arguments control the size of pre-allocated buffers to hold these results: the former counts the maximum number of paths, the latter counts the numbers of bytes in them. If the profiler runs out of space it will skip recording the line information for new files, and issue a warning when Rprof(NULL) is called to finish profiling.

See Also

The chapter on “Tidying and profiling R code” in ‘Writing R Extensions’: RShowDoc("R-exts").

summaryRprof to analyse the output file.

tracemem, Rprofmem for other ways to track memory use.


## Not run: Rprof()
## some code to be profiled
## some code NOT to be profiled
Rprof(append = TRUE)
## some code to be profiled
## ...
## Now post-process the output as described in Details

## End(Not run)

[Package utils version 4.4.1 Index]