similar to: Interpreting R memory profiling statistics from Rprof() and gc()

Hello, Appending information to the profiler's output seems to generate problems. Here is a small example of code : <code r> require(boot) Rprof( memory.profiling = TRUE) Rprof(NULL) for(i in 1:2){ Rprof( memory.profiling = TRUE, append = TRUE) example(boot) Rprof(NULL) } </code> The problem is that the file Rprof.out contains more than once the header information: $ grep

Dear all, I am working on a 64 bits Linux system. I issue the following R commands: > rm(list=ls()) # To remove all objects in the workspace. > gc() # To free memory. used (Mb) gc trigger (Mb) max used (Mb) Ncells 124250 6.7 350000 18.7 350000 18.7 Vcells 124547 1.0 786432 6.0 476934 3.7 > gc() # I had to do it again, don't know why! used (Mb) gc trigger (Mb) max used (Mb) Ncells

Rprof seems to ignore all time spent inside system() calls. E.g., this simple example actually takes about 10 seconds, but Rprof thinks the total time is only 0.12 seconds: > Rprof("sleep-system.out") ; system.time(system(command="sleep 10")) ; Rprof(NULL) user system elapsed 0.000 0.004 10.015 > summaryRprof("sleep-system.out")$by.total

I'm picking up this 5-year old thread. 1. About the four memory allocations without a stacktrace I think the four memory allocations without a stacktrace reported by Rprofmem(): > Rprofmem(); x <- raw(2000); Rprofmem("") > cat(readLines("Rprofmem.out", n=5, warn=FALSE), sep="\n") 192 :360 :360 :1064 :2040 :"raw" are due to some

Hello there, I am not quite sure how to interpret the output of Rprof (in the following the output I was staring at). I was poking around the web a little bit for documentation but without much success. I guess if I want to figure out what takes so long in my code the 2nd table $by.total and the total.pct column (pct = percent) is the most helpful. What does it mean that [ or [.data.frame is

Hi all, When I run the example in RProfmem, I get: Rprofmem("Rprofmem.out", threshold=1000) example(glm) Rprofmem(NULL) noquote(readLines("Rprofmem.out", n=5)) ... [1] 1384 :5416 :5416 :1064 :1064 :"readRDS" "index.search" "example" [2] 1064 :"readRDS" "index.search" "example" [3] 4712

No. But it has not been posted either. You got that message because you sent your message to the wrong address. You should have sent it to r-help at r-project.org You had probably sent it to r-help-request at r-project.org which would have had the effect that the server would have tried to interpret the contents of you message as commands (e.g. to unsubscribe, change your subscription

Hello! I have run Rprof on a function of mine and the results look very strange, to say the least. At the end I of this email is an output of summaryRprof. Can someone help me interpret this output? I have read the appropriate section in the manual "Writing R Extensions" and help pages. If I understand this output correctly, it is saying that "unlist" has been active in

I have some trouble interpreting the output from profiling. I have read the help pages Rprof, summaryRprof and consult the R extensions manual, but I still have problems understanding the output. Basically the output consist of self.time and total.time. I have the understanding that total.time is the time spent in a given function including any subcalls or child functions or whatever the

Hello, I submit a patch for review that implements code coverage tracing in the R interpreter. It records the lines that are actually executed and their associated frequency for which srcref information is available. I perfectly understands that this patch will not make its way inside R as it is, that they are many concerns of stability, compatibility, maintenance and so on. I would like to have

The Rprofmem facility is currently enabled only if the configuration option --enable-memory-profiling is used. However, the overhead of having it enabled is negligible when profiling is not actually being done, and can easily be made even smaller. So I think it ought to be enabled all the time. I've attached a patch doing this, which also makes a number of other improvements to Rprofmem,

Yes. It should have something to do with read/write permissions, but it is not clear how it happens. I can write file to C drive using R. I usually write my results matrix to a txt file in C drive. For Rprof(), the boot.out file can be created, but only with one line sample.interval=20000 The situation is the same even if I specify the directory to the D drive,where I have the full

Hi, >From reading ?Rprof, I checked R CMD Rprof --help and learned that there are options to specify the min % to print. This is currently (R-devel r52975) displayed with the --help option as --min%total minimum % to print for 'by total' --min%self minimum % to print for 'by self' So I tried R CMD Rprof --min%total 5 and got an error. After looking at

Dear all, I am doing 200 times simulation. For each time, I generate a matrix and define some function on this matrix to get a 6 dimension vector as my results. As the loop should be slow, I generate 200 matrice first, and save them into a list named ma, then I define zz<-sapply(ma, myfunction) To my surprise, It almost costs me the same time to get my results if I directly use a loop

Dear all, I am doing 200 times simulation. For each time, I generate a matrix and define some function on this matrix to get a 6 dimension vector as my results. As the loop should be slow, I generate 200 matrice first, and save them into a list named ma, then I define zz<-sapply(ma, myfunction) To my surprise, It almost costs me the same time to get my results if I directly use a loop

Hi, there was a memory improvement done in R going from R 3.3.3 to R 3.4.0 when it comes to comparing an integer 'x' an double 'y' (either may be scalar or vector). For example, in R 3.3.3, I get: > getRversion() [1] '3.3.3' > x <- integer(1000) > y <- double(1000) > profmem::profmem(z <- (x < y)) Rprofmem memory profiling of: z <- (x < y)

Hi folks, I'm attempting to use the EMD package to analyze some neuroimaging data (timeseries with 64 channels sampled across 1 million time points within each of 20 people). I found that processing a single channel of data using EMD::emd() took about 8 hours. Exploration using Rprof() suggested that most of the compute time was spent in EMD::extrema(). Looking at the code for EMD:extrema(),

I have a question concerning memory. I understood that R takes a fixed amount of memory at startup (which I can influence with --vsize --nsize) and that gc() shows the memory still free of the total memory reserved for R. However, if I create a long vector of character data, gc() only seem to reflect the space needed for a vector of pointers to char, the space used for the character data itself

Hi, when doing as.double() on an object that is already a double, the object seems to be copied internally, doubling the memory requirement. See example below. Same for as.character() etc. Is this intended? Example: % R --vanilla > x <- double(1e7) > gc() used (Mb) gc trigger (Mb) max used (Mb) Ncells 234019 6.3 467875 12.5 350000 9.4 Vcells 10103774 77.1

Hi, I'm running some routines with standard matrix operations like solve() and diag(). When I do a profile, the lead item under total time is standardGeneric(). Furthermore, solve() and diag() have much greater total time than self time. ??? I assume there is some time-consuming decision going on in the usual functions; is there any way to avoid that and go straight to the calculaions? Thanks