Intermittent failure of default CPU binding
The default CPU binding for ORCA jobs can fail sporadically. The failure is almost immediate and produces a cryptic error message, e.g.
Software
OpenMPI-HPCX 4.1.x hangs on writing files on a shared file system
Your job utilizing openmpi/4.1.x-hpcx (or 4.1.x on Ascend) might hang while writing files on a shared file system. This issue is caused by a bug stemming from the default OMPIO I/O module and UCX library. We have identified ORCA as being affected by this problem. If you are experiencing this issue, please consider the following solutions:
OpenMPI 4 and NVHPC MPI Compatibility Issues with SLURM HWLOC
A pure MPI application using mpirun or mpiexec with more ranks than the number of NUMA nodes may encounter an error similar to the following:
LS-DYNA mpp-dyna Cardinal: Remote access error on mlx5_0:1, RDMA_READ
You may encounter the following error while running mpp-dyna jobs with multiple nodes:
GCC 13 compilation errors due to missing headers
Users may encounter the following errors when compiling a C++ program with GCC 13:
error: 'uint64_t' in namespace 'std' does not name a type
or
HCOLL-related failures in OpenMPI applications
Several applications using OpenMPI, including HDF5, Boost, Rmpi, ORCA, and CP2K, may fail with errors such as
mca_coll_hcoll_module_enable() coll_hcol: mca_coll_hcoll_save_coll_handlers failed
or
Caught signal 11: segmentation fault
We have identified that the issue is related to HCOLL (Hierarchical Collectives) being enabled in OpenMPI.
Unavailability of intel channel among the conda channel list:
If you are getting an error:
UnavailableInvalidChannel: HTTP 403 FORBIDDEN for channel intel <https://conda.anaconda.org/intel> while creating a python environment or installing python packages, you can solve it by running the command
conda config --remove channels intel.
If you would like to use intel hosted packages for python environment, you can access them by running the following command
Compatibility Issues with NumPy 2.0
The newly released version of NumPy 2.0 includes substantial internal changes, including migrating code from C to C++. These modifications have led to significant issues with backwards compatibility, resulting in numerous breaking changes to both the Python and C APIs. As a consequence, packages built against NumPy 1.xx may encounter ImportError messages. To ensure compatibility, these packages must be rebuilt against NumPy 2.0.
Recommendation for Addressing the Issue:
