similar to: Suggestion for memory optimization and as.double() with friends

Per the request to test the latest tarball referenced below, I have built R on AIX 5.3. There is a memory issue, please see 3) below. 1) Build with --enable-BLAS-shlib option. Builds and passes "make check". 2) GNU libiconv was installed; R configured *without* the --without- iconv option. Builds and passes "make check." 3) Memory issue: a)

Hello, I have the following data.frame structure(list(Study = structure(c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,

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 Uwe, (When replying to your message, I sent the reply to r-devel and not r-package-devel, as Martin Meachler suggested that this thread would be a better fit for r-devel.) Thanks. In the example below I used rm() explicitly, but in general users wouldn't do that. One of the reasons for the large number of file handles is that sometimes unnamed temporary objects are created. For

Hallo all Suppose I have a summary amount of various colours used in different areas colour plastic paint black 15.5 173.8 brown 6.0 523.2 green 2.5 6.4 red 77.1 237.4 yellow 144.6 77.3 It seems that there are some preferable colours in these two areas (yellow in plastic, brown an red in paint). The problem seems to me similar to chi square test but it is intended for testing counts or

Hello! I use tincd to interconnect 3 LANs: A, B and C. So long, it works fine: everybody reaches everybody. But I want a different behavior: A and B should be allowed to talk, as should B and C. I tried to simply delete the host-files on the nodes that should not be allowed to talk to eachother: A has a hostfile from B B has a hostfile from A and C C has a hostfile from B But this is no

Hi There, We currently using iproute2 for load balancing. However we need more upload speed as we load balance over 3 dsl lines. I''ve been looking for a way to combine the upload speed to more faster. Found a site called www.upstreaminter.net where these guys bond the adsl channels to improve uploads, Since downloading is problem as its need to know the ip address of the downloader they

Dear Luke, Thanks. See below On 07-08-18 17:07, luke-tierney at uiowa.edu wrote: > In R 3.5 and later you should not need to gc() -- that should happen > automatically within the connections code. Could you elaborate on what has changed in R 3.5? As far as I can tell my problem also occurs in R 3.5 (my computer is still on 3.4.4; but I assume the solaris CRAN machine isn't). And

Hi, I am not sure if this is a bug and I apologize if it is something I didn't read carefully in the R extension manual. My initial search on the R help and R devel list archive didn't find useful information. I am using .Call (as written in the R extension manual) for the C code and have found that the .Call didn't release the memory claimed by allocVector. Even after applying

Hi, I am not sure if this is a bug and I apologize if it is something I didn't read carefully in the R extension manual. My initial search on the R help and R devel list archive didn't find useful information. I am using .Call (as written in the R extension manual) for the C code and have found that the .Call didn't release the memory claimed by allocVector. Even after applying

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

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

I have always been using ebitbin to set the 3GB switch in the windows binary, but version 2.2.1 has this set as default (which I verified using dumpbin). However, when I generate junk data to fill up my memory and read the memory usage using gc(), it seems that I am not getting as good results with 2.2.1 patched as I was with 2.2.0 after I edited the header. Under R 2.2.0 I was able to use over

Full_Name: Ashley Ford Version: 1.4.0 OS: Windows NT4 Submission from: (NULL) (146.80.9.20) I am suffering from a memory leak in read.table in the new precompiled windows 1.4. it works fine in 1.3 Create a 90000 line file of 7 variables eg perl -e '$e=exp(1);for($i=0;$i<90000;$i++){printf "%d".(" %f"x6)."\n", $i,$i*$e,3,4,5,6,7,8,9}' > n90000 R :

On 7 Aug 2015, at 23:17, Tom Stellard via llvm-dev <llvm-dev at lists.llvm.org> wrote: > > I don't have any strong opinions. Did this actually break anyone's use > case or is it just a hypothetical problem? It doesn't seem like there > is much value in fixing this until llvm-config is also fixed. The current 3.7 code breaks for the codebase that I use for

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

Have searched through the archives but have been unable to find any related issues - hopefully I'm not bringing up an old topic. Am using RW1041 on a Windows NT on a machine with 1Gb of memory. Have a function doit() that reads in a chunk of data using readBin, performs a regression, saves out coeffs and then returns. When using Rgui with the default memory limit of 256Mb I'm able to

I was surprised to observe the following difference between 2.4.1 and 2.6.0 after a long overdue upgrade a few months ago of our departmental server. It wasn't a bug fix, but a subtle improvement. Here's the simplest example I could create. The size is excessive, on the order of the Netflix Competition data. The integer matrix is about 1.12 GB, and if coerced to numeric it is 2.24 GB.

[some de-capitalization of *SXP done manually by mailing list maintainer ; the originally was caught as potential spam. MM] I have a little R program that crashes with the message xmalloc: out of virtual memory The code has a repeat{} loop that watches the sizes of some files. When there's an increase it updates things by reading the last 65 lines of each file, doing some

In R 3.5 and later you should not need to gc() -- that should happen automatically within the connections code. Nevertheless, I would recommend redesigning your approach to avoid hanging onto open file connections as these are a scarce resource. You can keep around your temporary files without having them open and only open/close them on access, with the close run in an on.exit or a