similar to: ready to toss the towel | lapack and 2.9.0

Well, CentOS 5.3, which amounts to the same thing. I recently decided to upgrade my main research machine from Fedora Core 8 -> CentOS 5.3. Basically, I was looking to move to a distro with longer 'term-of-life' than the release schedule for Fedora currently allows. The machine is a multi-Opteron box, so both 32- and 64-bit apps natively supported. Since I do a lot of 'linear

So, I tried the following configure: ./configure --with-lapack --with-blas --with-tcltk No use of ACML at all, but both lapack and blas. Configure proceeds without any errors. Make, same thing. Make check - same problem with stats. But, this time a .fail file got created in tests/Example. Bottom of the file has something which might resonate with someone out there: Error in La.svd(x, nu,

Dear All, Under Debian GNU/Linux La.svd (with method = "dgesdd") sometimes gives the error "Error in La.svd(data, nu = 0, nv = min(nrow, ncol), method = "dgesdd") : LAPACK routine DGESDD gave error code -12" It seems not to depend on the data per se, but on the relationship between numbers of rows and columns. For example, if the number of columns is 100,

While I recently received some very helpful files and email from Kevin Hendricks for compiling with ATLAS, thought I'd first have a stab at ACML. Having problems, which I suspect are trivial to solve: 1. machine is running RHEL 4, meaning, it uses gcc 3.4.5 out of the box, and g77. In my experience, over-riding RHEL's choice, and manually upgrading to gcc 4.x.x (and, as a consequence

Greetings - For a host of reasons I chose (was forced) to upgrade my multi-Opteron box from Fedora 7 -> Fedora 8. In the process, I also updated the ACML I had installed from 4.0.0 to 4.1.0. While I get no errors (that I can find) in the config -> make -> make install sequence, I'm pretty sure (based on some benchmarks) that I'm not getting BLAS and/or Lapack to compile in. So,

Hi, I'm just wondering if it is planned to include the Lapack routine DGESDD (and friends) in R-1.4.0? This is faster (supposedly by a factor of ~6 for large matrices) than DGESVD which is currently (R-1.3.1) called by La.svd. And if it is not in the plans yet, is there a chance it could be? I've added it to my local version of R-1.3.1 and so far see a factor of 4 improvement over

Hello. I am a Linux neophyte and know almost nothing ?about compiling, so I would appreciate any help and advice y'all would care to offer. I am trying to compile the 64 bit version of R using a tuned ATLAS and LAPACK (ATLAS 3.9.24). I am running Ubunto 10.04 LTS (through wubi, FWIW). The ATLAS and LAPACK files (atlas.so, f77blas.so, lapack.so, and cblas.so) are sitting in the folder

Hi, I wonder where I can find an example of using a function in LAPACK library in a user's own C code. I wrote a C program which will be compiled and linked to produce a DLL file and then loaded into R. I hope to use a function from LAPACK library, for example, dgesdd, in the program. Following R manual, I call the function by F77_CALL(dgesdd) in the program. The program can be compiled

Dear Philipp, this is just a tentative answer because debugging is really not possible without a reproducible example (or, at a very bare minimum, the output from traceback()). Anyway, thank you for reporting this interesting numerical issue; I'll try to replicate some similar behaviour on a similarly dimensioned artificial dataset when I have some time (which might not be soon). As for now,

I've been updating the information on tuned BLAS for R-admin in R-patched and R-devel. We have ATLAS (widely available, including for Windows) MKL (licensed on ix86 and x86_64 Linux and Windows) ACML (by AMD, but for all ix86 and x86_64 chips, Linux and Windows. Now available for gfortran.) Goto (academic use only, only some chips, only Linux) MKL and ACML provide full LAPACK,

Greetings - A quick perusal of some of the posts to this maillist suggest the level of the questions is probably beyond someone working at my level, but at the risk of looking foolish publicly (something I find I get increasingly comfortable with as I get older), here goes: My research group recently purchased a multi-Opteron system (bunch of 880 chips), running 64-bit RHEL 4 (which we have

Greetings - I'm trying to compile R under GNU/Linux (RHEL 4) on a multi-Opteron box, with ACML support. First, I downloaded and installed ACML 3.5 - GNU version, although I'm not entirely sure what the differences are - from the AMD website. The ACML libraries were installed to /opt/acml3.5.0/ Second, I ran ./configure --with-blas='-lacml' The configure went fine,

So, tried again from scratch. Again, CentOS 5.3, which is essentially RHEL 5.3. ./configure --with-blas="-L/opt/acml4.3.0/gfortran64/lib -lacml" In config.log, get things like configure:37199: checking for dgemm_ in -L/opt/acml4.3.0/gfortran64/lib -lacml configure:37230: gcc -std=gnu99 -o conftest -g -O2 -I/usr/local/include -L/usr/local/lib64 conftest.c

Dear R helpers, I am working with R 2.4.1 GUI 1.18 (4038) for MacOSX. I have a matrix of 10 000 genes and try to run the following commands: > model.mix<-makeModel (data=data, formula=~Dye+Array+Sample+Time, random=~Array+Sample) > anova.mix<-fitmaanova (data, model.mix) > test.mix<-matest (data, model=model.mix, term="Time", n.perm=100, test.method=c(1,0,1,1))

Error in La.svd(x, nu, nv) : error code 1 from Lapack routine ‘dgesdd’ what resources are there to track down errors like this [[alternative HTML version deleted]]

Dear all I have a big matrix of standardized values (dimensions 285x5829) and R fails to calculate the principal components using prcomp() with the following error message: pc <- prcomp(my.matrix) Error in La.svd(x, nu, nv, method) : error code 1 from Lapack routine dgesdd Is the matrix too big? I'm using R-1.8.1 under Unix (Solaris8) and Linux(Suse 8.2). I tried to perform a principal

Hello R-users, during one of my analyses that involve a SVD, I get the following error message: Error in La.svd(x, nu, nv, method) : error code 1 from Lapack routine dgesdd With a search on the R web site, I only found references to error codes 17 and 3 for this particular routine. I also found the Lapack web site, but could not find a list of the possible error messages. If somebody knows what

For grins, tried rebuilding R 2.9.0 without using ACML 4.3.0. Config goes fine, make runs without any errors. make check - and - ta-dah! - no errors for stats. Everything seems to check out just fine. So, it seems as if R 2.9.0, ACML 4.3.0, and perhaps one/more things under CentOS don't play nice. Will trying unloading 4.3.0, installing 4.2.0 (which worked before on the Fedora box), and

Hi, I'm having problems using ACML with R. I made two changes in config.site by setting LDFLAGS="-L/opt/acml3.1.0/gnu64/lib" BLAS_LIBS="-lacml" ./config and make go through but when I try to use the lm() function, I get the error message Error in chol2inv(Qr$qr[p1, p1, drop = FALSE]) : lapack routines cannot be loaded In addition: Warning message: unable to load

I am encountering segfaults when checking the lme4 package on an Athlon64 system if I use the acml blas. R was built as a 64-bit application using the GCC 4.0.3 compiler suite including gfortran. The version of acml is 3.5.0 gfortran64. I do not encounter the segfaults when I compile R with R's built-in BLAS. The errors occur in the first example in lme4 in a call to lmer. It looks like