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R's default configuration options disable memory profiling, shared-library libR, static-library libR.a, GNU gettext, valgrind headers, and link-time optimisation.
Some of these (like valgrind) must just be because not everyone will use it. Others might be answered elsewhere on SO (like Is there any reason why not to use link time optimization? might answer --enable-lto[=no]).
But what is the possible disadvantage or cost of enabling memory profiling in R?
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Specifically, the Memory Profiler can show you the following about object allocations: What types of objects were allocated and how much space they use. The stack trace of each allocation, including in which thread. When the objects were deallocated (only when using a device with Android 8.0 or higher).
The easiest way to profile a single method or function is the open source memory-profiler package. It's similar to line_profiler , which I've written about before . You can use it by putting the @profile decorator around any function or method and running python -m memory_profiler myscript.
Oops, apparently the answer can be found from https://stat.ethz.ch/R-manual/R-devel/library/utils/html/Rprofmem.html:
The memory profiler slows down
Reven when not in use, and so is a compile-time option.
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