R: Enable Profiling of R's Execution

Rprof {utils} | R Documentation

## Enable Profiling of R's Execution

### Description

Enable or disable profiling of the execution of R expressions.

### Usage

    Rprof(filename = "Rprof.out", append = FALSE, interval = 0.02,
           memory.profiling = FALSE, gc.profiling = FALSE,
           line.profiling = FALSE, numfiles = 100L, bufsize = 10000L)

### Arguments

`filename` | The file to be used for recording the profiling results. Set to `NULL` or `""` to disable profiling.
`append` | logical: should the file be over-written or appended to?
`interval` | real: time interval between samples.
`memory.profiling` | logical: write memory use information to the file?
`gc.profiling` | logical: record whether GC is running?
`line.profiling` | logical: write line locations to the file?
`numfiles, bufsize` | integers: line profiling memory allocation

### 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, 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 the time interval measures is subtle: 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 (on Windows, but not on a Unix-alike).

Note that the timing interval cannot be too small, for the time spent in each profiling step is added to the interval. What is feasible is machine-dependent, but 10ms seemed as small as advisable on a 1GHz machine.

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.

### Note

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

### Note

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” (see the ‘doc/manual’ subdirectory of the R source tree).

`summaryRprof` to analyse the output file.

`tracemem`, `Rprofmem` for other ways to track memory use.

### Examples

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

    ## End(Not run)

[Package utils version 3.5.1 Index]