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Current known issues


Perlmutter is not a production resource

Perlmutter is not a production resource and usage is not charged against your allocation of time. While we will attempt to make the system available to users as much as possible, it is subject to unannounced and unexpected outages, reconfigurations, and periods of restricted access. Please visit the timeline page for more information about changes we've made in our recent upgrades.

NERSC has automated monitoring that tracks failed nodes, so please only open tickets for node failures if the node consistently has poor performance relative to other nodes in the job or if the node repeatedly causes your jobs to fail.

New issues

  • Building software fails with message like cannot find -lcublas in PrgEnv-gnu. This is because the cublas (and other CUDA math) libraries are in a different location to the main cudatoolkit libraries, and the modulefiles are missing the environment variables that would enable the compiler and linker to find them. You can work around this by setting some environment variables after loading cudatoolkit:
perlmutter$ export LIBRARY_PATH="${LIBRARY_PATH}:${CUDATOOLKIT_HOME}/../../math_libs/lib64"
perlmutter$ export CPATH="${CPATH}:${CUDATOOLKIT_HOME}/../../math_libs/include"

Ongoing issues

  • The default CPU architecture module is craype-x86-milan, which corresponds to the CPU architecture on Perlmutter login and compute nodes. However, users may see compiler errors in some circumstances:
    • Currently the Cray Wrappers in PrgEnv-nvidia are hardcoded to optimize code for AMD Rome (zen2), to generate code optimized for AMD Milan you must pass -march=znver3 or -tp=zen3 to your compilation step.
  • MPI users may hit segmentation fault errors when trying to launch an MPI job with many ranks due to incorrect allocation of GPU memory. We provide more information and a suggested workaround.
  • Some users may see messages like -bash: /usr/common/usg/bin/nersc_host: No such file or directory when you login. This means you have outdated dotfiles that need to be updated. To stop this message, you can either delete this line from your dot files or check if NERSC_HOST is set before overwriting it. Please see our environment page for more details.
  • Known issues for Machine Learning applications

Be careful with NVIDIA Unified Memory to avoid crashing nodes

In your code, NVIDIA Unified Memory might look something like cudaMallocManaged. At the moment, we do not have the ability to control this kind of memory and keep it under a safe limit. Users who allocate a large pool of this kind of memory may end up crashing nodes if the UVM memory does not leave enough room for necessary system tools like our filesystem client. We expect a fix in early 2022. In the meantime, please keep the size of memory pools allocated via UVM relatively small. If you have questions about this, please contact us.


The Burst Buffer on Cori has a number of known issues, documented at Cori Burst Buffer.