similar to: Memory Experimentation: Rule of Thumb = 10-15 Times the Memory

Hi, I've been having similar experiences and haven't been able to substantially improve the efficiency using the guidance in the I/O Manual. Could anyone advise on how to improve the following scan()? It is not based on my real file, please assume that I do need to read in characters, and can't do any pre-processing of the file, etc. ## Create Sample File

This might be because I didn't get it right, but; I thought R would release memory back to the system as (big) objects get removed? Here is my platform (with 1Gb of RAM): platform sparc-sun-solaris2.8 arch sparc os solaris2.8 system sparc, solaris2.8 status major 1 minor 3.1 year 2001 month 08 day 31 language R A little example: Start a new section of R, with

I am getting "Error: cannot allocate vector of size 197 MB". I know that similar problems were discussed a lot already, but I didn't find any satisfactory answers so far! Details: *** I have XP (32bit) with 4GB ram. At the time when the problem appeared I had 1.5GB of available physical memory. *** I increased R memory limit to 3GB via memory.limit(3000) *** I did gs() and got

Please, I have a 2GB computer and a huge time-series to embedd, and i tried increasing memory.limit() and memory.size(max=TRUE), but nothing. Just before the command: > memory.size(max=TRUE) [1] 13.4375 > memory.limit() [1] 1535.875 > gc() used (Mb) gc trigger (Mb) max used (Mb) Ncells 209552 5.6 407500 10.9 350000 9.4 Vcells 125966 1.0 786432 6.0 496686 3.8

I came across this in a simulation I ran under 1.6.0: If I do something like R> x <- rnorm(10) R> rval <- NULL R> for(i in 1:100000) rval <- t.test(x)$p.value then the process size remains at about 14M under 1.5.1, but it seems to be almost linearly growing up to more than 100M under 1.6.0. I know that the above simulation is nonsense, but it was the simplest I could come up

Full_Name: Don Maszle Version: 2.3.0 OS: x86_64-unknown-linux-gnu Submission from: (NULL) (206.86.87.3) # On our new 32 GB x86_64 machine R : Copyright 2006, The R Foundation for Statistical Computing Version 2.3.0 (2006-04-24) ISBN 3-900051-07-0 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or

How do people deal with R and memory issues? I have tried using gc() to see how much memory is used at each step. Scanned Crawley R-Book and all other R books I have available and the FAQ on-line but no help really found. Running WinXP Pro (32 bit) with 4 GB RAM. One SATA drive pair is in RAID 0 configuration with 10000 MB allocated as virtual memory. I do have another machine

Hi developers, After some investigation I have found there can be large discrepancies in the same object being saved as an external "xx.RData" file. The immediate repercussion of this is the possible increased size of your .RData workspace for no apparent reason. The function and its three scenarios below highlight these discrepancies. Note that the object being returned is exactly

Hello all again, I want to do bitwise addition in R. I am trying to generate a matrix 0000 0001 0010 .... .... 1111 I know the other ways of generating this matrix but I need to look at bitwise addition. Any suggestions??? thanks a lot Nameeta ------------------------------------------------- This email is intended only for the use of the individual or...{{dropped}}

I can't run a function which generates random numbrers using linear congruential generator. My multiplier is a=5+8^6, increment is b=1 and modulo is m=2^30. the code I have written works for modulo upto m=2^28. For m= 2^29 , it says, can not allocate memory for the vector or something like that. For m= 2^31 or more, its says the argument "for i in 1:m " is too large in

Dear All, I am running R in a system with the following configuration *Processor: Intel(R) Xeon(R) CPU X5650 @ 2.67GHz OS: Ubuntu X86_64 10.10 RAM: 24 GB* The R session info is * R version 2.14.1 (2011-12-22) Platform: x86_64-pc-linux-gnu (64-bit) locale: [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 [5] LC_MONETARY=en_US.UTF-8

Is it expected behavior for gc() to return shrinking values as it gets called multiple times? Here's what I've got: > gc() used (Mb) gc trigger (Mb) Ncells 221754 6.0 467875 12.5 Vcells 3760209 28.7 14880310 113.6 > gc() used (Mb) gc trigger (Mb) Ncells 221760 6.0 467875 12.5 Vcells 3016206 23.1 11904247 90.9 > gc() used (Mb) gc

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

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 just starting out with R and came across R_inferno.pdf by Patrick Burns just yesterday - I recommend it! His description of how 'growing' objects (e.g. obj <- c(obj, additionalValue) eats up memory prompted me to rewrite a function (which made such calls ~210 times) so that it used indexing into a dimensioned object instead (i.e. obj[i, ] <- additionalValue). This

Hello everyone - I have a couple of questions about memory management of large objects. Thanks in advance for your response. I'm running R version 1.9.1 on solaris 8, compiled as a 32 bit app. My system has 12.0 GB of memory, with usually ~ 11GB free. I checked system limits using ulimit, and there is nothing set that would limit the maximum amount of memory for a process (with the

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, the question concerning to the memory used and g.c. after having removed objects. What is wrong? bevor ------- > gc() used (Mb) gc trigger (Mb) max used (Mb) Ncells 313142 8.4 1801024 48.1 1835812 49.1 Vcells 809238 6.2 142909728 1090.4 178426948 1361.3 hier all attached objects

Hi, I am trying to minimize a rather complex function of 5 parameters with gafit and nlm. Besides some problems with both optimization algorithms (with respect to consistantly generating similar results), I tried to run this optimization about a hundred times for yet two other parameters. Unfortunately, as the log below shows, during that batch process R starts to eat up all my RAM,