I have to repeatedly serialize (big) R objects. To avoid repeated garbage collecting of the resulting raw vectors (after profiling, it turns out that half of my script running time is spent in gc!) I’d like to ask R to directly write in a memory buffer -- always the same, as after each serialization I’d call a C function with .C that would work directly on this memory buffer; it is the result of this C function that interests me.
Is that possible? How unreasonable is it?
Thanks in advance.
I might not have understood your problem, but why don't you directly use your R object in c++ code using Rcpp. There will be no copy and you don't not need any serialization.
Related
Original motivation behind this is that I have a dynamically sized array of floats that I want to pass to R through Rcpp without either incurring the cost of a zeroing out nor the cost of a deep copy.
Originally I had thought that there might be some way to take heap allocated array, make it aware to R's gc system and then wrap it with other data to create a "Rcpp::NumericVector" but it seems like that that's not possible - or doable with my current knowledge.
However and correct me if I'm wrong it looks like simply constructing a NumericVector with a size N and then using it as an N sized allocation will call R.h's Rf_allocVector and that itself does not either zero out the allocated array - I tested it on a small C program that gets dyn.loaded into R and it looks like garbage values. I also took a peek at the assembly and there doesn't seem to be any zeroing out.
Can anyone confirm this or offer any alternate solution?
Welcome to StackOverflow.
You marked this rcpp but that is a function from the C API of R -- whereas the Rcpp API offers you its constructors which do in fact set the memory tp zero:
> Rcpp::cppFunction("NumericVector goodVec(int n) { return NumericVector(n); }")
> sum(goodVec(1e7))
[1] 0
>
This creates a dynamically allocated vector using R's memory functions. The vector is indistinguishable from R's own. And it has the memory set to zero
as we use R_Calloc, which is documented in Writing R Extension to setting the memory to zero. (We may also use memcpy() explicitly, you can check the sources.)
So in short, you just have yourself confused over what the C API of R, as well as Rcpp offer, and what is easiest to use when. Keep reading documentation, running and writing examples, and studying existing code. It's all out there!
In short: How can I call, from within Rccp C++ code, the agrep C internal function that gets called when users use the regular agrep function from base R?
In long: I have found multiple questions here about how to invoke, from within Rcpp, a C or C++ function created for another package (e.g. using C function from other package in Rcpp
and Rcpp: Call C function from a package within Rcpp).
The thing that I am trying to achieve, however, is at the same time simpler but also way less documented: it is to directly call, from within Rcpp, a .Internal C function that comes with base R rather than another package, without interfacing with R (that is, without doing what is said in Call R functions in Rcpp). How could I do that for the .Internal C function that lays underneath base R's agrep wrapper?
The specific function I am trying to call here is the agrep internal C function. And for context, what I am ultimately trying to achieve is to speed-up a call to agrep for when millions of patterns must be each checked against each of millions of x targets.
Great question. The long and short of it is "You cant" (in many cases) unless the function is visible in one of the header files in "src/include/". At least not that easily.
Not long ago I had a similar fun challenge, where I tried to get access to the do_docall function (called by do.call), and it is not a simple task. First of all, it is not directly possible to just #include <agrep.c> (or something similar). That file simply isn't available for inclusion, as it is not a part of the "src/include". It is compiled and the uncompiled file is removed (not to mention that one should never "include" a .c file).
If one is willing to go the mile, then the next step one could look at is "copying" and "altering" the source code. Basically find the function in "src/main/agrep.c", copy it into your package and then fix any errors you find.
Problems with this approach:
As documented in R-exts the internal structures of sexprec_info is not made public (this is the base structure for all objects in R). Many internal function use the fields within this structure, so one has to "copy" the structure into your source code, to make it public to your code specifically.
If you ever #include <Rcpp.h> prior to this file, you will need to go through each and every call to internal functions and likely add either R_ or Rf_.
The function may contain calls to other "internal" functions, that further needs to be copied and altered for it to work.
You will also need to get a clear understanding of what CDR, CAR and similar does. The internal functions have a documented structure, where the first argument contains the full call passed to the function, and function like those 2 are used to access parts of the call.
I did myself a solid and rewrote do_docall changing the input format, to avoid having to consider this. But this takes time. The alternative is to create a pairlist according to the documentation, set its type as a call-sexp (the exact name is lost to me at the moment) and pass the appropriate arguments for op, args and env.
And lastly, if you go through the steps, and find that it is necessary to copy the internal structures of sexprec_info (as described later), then you will need to be very careful about when you include Rinternals and Rcpp, as any one of these causes your code to crash and burn in the most beautiful and silent way if you include your header and these in the wrong order! Note that this even goes for [[Rcpp::export]], which may indeed turn out to include them in the wrong arbitrary order!
If you are willing to go this far down the drainage, I would suggest carefully reading adv-R "R's C interface" and Chapter 2, 5 and 6 of R-ext and maybe even the R internal manual, and finally once that is done take a look at do_docall from src/main/coerce.c and compare it to the implementation in my repository cmdline.arguments/src/utils/{cmd_coerce.h, cmd_coerce.c}. In this version I have
Added all the internal structures that are not public, so that I can access their unmodified form (unmodified by the current session).
This includes the table used to store the currently used SEXP's, that was used as a lookup. This caused a problem as I can't access the modified version, so my code is slightly altered with the old code blocked by the macro #if --- defined(CMDLINE_ARGUMENTS_MAYBE_IN_THE_FUTURE). Luckily the code causing a problem had a static answer, so I could work around this (but this might not always be the case).
I added quite a few Rf_s as their macro version is not available (since I #include <Rcpp.h> at some point)
The code has been split into smaller functions to make it more readable (for my own sake).
The function has one additional argument (name), that is not used in the internal function, with some added errors (for my specific need).
This implementation will be frozen "for all time to come" as I've moved on to another branch (and this one is frozen for my own future benefit, if I ever want to walk down this path again).
I spent a few days scouring the internet for information on this and found 2 different posts, talking about how this could be achieved, and my approach basically copies this. Whether this is actually allowed in a cran package, is an whole other question (and not one that I will be testing out).
This approach goes again if you want to use not-public code from other packages. While often here it is as simple as "copy-paste" their files into your repository.
As a final side note, you mention the intend is to "speed up" your code for when you have to perform millions upon millions of calls to agrep. It seems that this is a time where one should consider performing the task in parallel. Even after going through the steps outlined above, creating N parallel sessions to take care of K evaluations each (say 100.000), would be the first step to reduce computing time. Of course each session should be given a batch and not a single call to agrep.
It seems that I can duplicate a kernel by get the program object and kernel name from the kernel. And then I can create a new one.
Is this the right way? It doesn't looks so good, though.
EDIT: To answer properly the question: Yes it is the correct way, there is no other way in CL 2.0 or earlier versions.
The compilation (and therefore, slow step) of the CL code creation is in the "program" creation (clProgramBuild + clProgramLink).
When you create a kernel. You are just creating a object that packs:
An entry point to a function in the program code
Parameters for input + output to that function
Some memory to remember all the above data between calls
It is an simple task that should be almost for free.
That is why it is preferred to have multiple kernel with different input parameters. Rather than one single kernel, and changing the parameters every loop.
I am tuning a data import script, and occasionally I find an approach puts too much into memory in one call (usually this is because I am writing inefficient code). The "failed to allocate" message is only sort of useful in that it tells you how much memory was needed (without an informative traceback) and only if the allocation fails. Regular profiling requires that enough memory be available for allocation (and contiguously placed), which changes depending on the circumstances under which the code is run, and is very slow.
Is there a function that simulates a call to see how much memory will be used, or otherwise efficiently profiles how much memory a line of R will need whether it succeeds or fails? Something that could wrap an existing line of code in a script like System.time would be ideal.
Edit: lsos() does not work for this because it only describes what is stored after a command is run. (see: Reserved memory of R is twice the size of an allocated array)
I have started using the doMC package for R as the parallel backend for parallelised plyr routines.
The parallelisation itself seems to be working fine (though I have yet to properly benchmark the speedup), my problem is that the logging is now asynchronous and messages from different cores are getting mixed in together. I could created different logfiles for each core, but I think I neater solution is to simply add a different label for each core. I am currently using the log4r package for my logging needs.
I remember when using MPI that each processor got a rank, which was a way of distinguishing each process from one another, so is there a way to do this with doMC? I did have the idea of extracting the PID, but this does seem messy and will change for every iteration.
I am open to ideas though, so any suggestions are welcome.
EDIT (2011-04-08): Going with the suggestion of one answer, I still have the issue of correctly identifying which subprocess I am currently inside, as I would either need separate closures for each log() call so that it writes to the correct file, or I would have a single log() function, but have some logic inside it determining which logfile to append to. In either case, I would still need some way of labelling the current subprocess, but I am not sure how to do this.
Is there an equivalent of the mpi_rank() function in the MPI library?
I think having multiple process write to the same file is a recipe for a disaster (it's just a log though, so maybe "disaster" is a bit strong).
Often times I parallelize work over chromosomes. Here is an example of what I'd do (I've mostly been using foreach/doMC):
foreach(chr=chromosomes, ...) %dopar% {
cat("+++", chr, "+++\n")
## ... some undoubtedly amazing code would then follow ...
}
And it wouldn't be unusual to get output that tramples over each other ... something like (not exactly) this:
+++chr1+++
+++chr2+++
++++chr3++chr4+++
... you get the idea ...
If I were in your shoes, I think I'd split the logs for each process and set their respective filenames to be unique with respect to something happening in that process's loop (like chr in my case above). Collate them later if you must ... ie. map/reduce your log files :-)