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, numfiles = 100L, bufsize = 10000L)
The file to be used for recording the profiling results.
""to disable profiling.
- logical: should the file be over-written or appended to?
- real: time interval between samples.
- logical: write memory use information to the file?
- logical: record whether GC is running?
- logical: write line locations to the file?
- numfiles, bufsize
- integers: line profiling memory allocation
Enabling profiling automatically disables any existing profiling to
another or the same file. Profiling works by writing out the call stack every
seconds, to the file specified. Either the
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.
How time is measured varies by platform: on a Unix-alike it is the 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. Note that the timing interval cannot usefully be too small: once the
timer goes off, the information is not recorded until the next timing
click (probably in the range 1--10msecs). Functions will only be recorded in the profile log if they put a
context on the call stack (see
primitive functions do not do so: specifically those which are
"special" (see the ‘R Internals’ manual
for more details). Individual statements will be recorded in the profile log if
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.
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
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.
The chapter on “Tidying and profiling R code” in
“Writing R Extensions” (see the
of the R source tree).
summaryRprof to analyse the output file.
Rprofmem for other ways to track
## 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 ## ---------------------------------------------