Unable to load dynamic library

[ntthung]

Hi Dirk,

I am using the package Rfast, which imports RcppZiggurat. I got the following error when running Rfast on a Linux cluster (Red Hat 6.10)

unable to load shared object '/home/users/sutd/R/x86_64-pc-linux-gnu-library/3.6/RcppZiggurat/libs/RcppZiggurat.so':
  libgsl.so.0: cannot open shared object file: No such file or directory

The packages are installed on my local directory. Edited: I've done some diagnostic and found that this only happens when the Rfast functions are being called in parallel, in a foreach() loop. When they are called directly it is OK.

Below are something I've tried but failed:

I know that libgsl.so is located in /usr/lib64/, so I've added the following line at the beginning of my R script

Sys.setenv(LD_LIBRARY_PATH=paste("/usr/lib64/", Sys.getenv("LD_LIBRARY_PATH"), sep = ":"))

I have also try to do dyn.load('/usr/lib64/libgsl.so') but I get the following error

 Error in dyn.load("/usr/lib64/libgsl.so") :
  unable to load shared object '/usr/lib64/libgsl.so':
  /usr/lib64/libgsl.so: undefined symbol: cblas_ctrmv

On Windows everything works fine.

Is this an issue or am I doing something wrong?

Thank you so much.

[eddelbuettel]

I fear that is between you and your RHEL machine as CRAN is happy (and has generally been):

Same of course on, say, my local box (running standard Ubuntu):

edd@rob:~$ ldd /usr/local/lib/R/site-library/RcppZiggurat/libs/RcppZiggurat.so | grep gsl
        libgsl.so.23 => /usr/lib/x86_64-linux-gnu/libgsl.so.23 (0x00007fa8fed3f000)
        libgslcblas.so.0 => /usr/lib/x86_64-linux-gnu/libgslcblas.so.0 (0x00007fa8fe314000)
edd@rob:~$ dpkg -S /usr/lib/x86_64-linux-gnu/libgsl.so.23
libgsl23:amd64: /usr/lib/x86_64-linux-gnu/libgsl.so.23
edd@rob:~$ 

i.e. the standard GSL shared library from the standard GSL package (I happen to be the Debian maintainer for it too) is used.

It really only affects an example, so you could take the scissors to the package and #ifdef out the GSL use.

[eddelbuettel]

And Crossposted to StackOverflow which seems to indicate you did not set up your parallel code right. I think Rui is correct and you need to register the cluster.