All code in this repo is for testing. The code may not work and may change. Pull requests and issues welcome.
See Quick Start Notes for a short overview of MP usage.
git clone https://github.com/SouthernMethodistUniversity/mp_testing.git
cd mp_testing/demos/00_nemo
./submit_jobs.sh
- LAMMPS (NGC)
- AMBER
- NAMD (NGC)
- OpenMM
- Gaussian
- VASP
- CRYSTAL
- Q-Chem
- Quantum Espresso
- Memory profiling
- Performance profiling
- Raja
- Magma
- heFFTe
- Pandas
- NumPy
- TensorFlow
- PyTorch
- DALI
- C
- C++
- Python
- Some custom layer in C++/CUDA
- Fortran
- CUDA Fortran
- Julia
- OpenMM
- AMBER
- Desmond
- GROMACS
- Mentioned MIC modes?
- NGC for Keras/TF and Pytorch
- Can't run enroot images directly via
enroot start hello_world.sqsh
. The OS needs squashfuse and fuse-overlayfs installed. I installed these on Easley and it works. - Custom build and final images for containerized Spack environments fails due
to apparently assuming that Spack already exists. See:
01_spack/spack_nvhpc.yaml
. - Spack-blessed NVIDIA container fails to build due to public key error. See:
01_spack/spack_lammps.yaml
. export ENROOT_MOUNT_HOME=1
to bind $HOME.- Default flags and
target=zen2
gave LAMMPS run times of 4:44, whiletarget=zen2 cppflags=-O3
- Running containers or non-hpc-x MPI produces warnings about
Unknown interface name
/An invalid value was given for btl_tcp_if_include
. It appears not to see the Mellanox / IB correctly?
- https://www.nvidia.com/content/dam/en-zz/Solutions/Data-Center/tesla-product-literature/gpu-applications-catalog.pdf
- https://docs.nvidia.com/deeplearning/frameworks/support-matrix/index.html
- https://docs.nvidia.com/deeplearning/dali/user-guide/docs/index.html
- https://secure.cci.rpi.edu/wiki/
- How and when do we decide we're updating Nvidia Drivers / Cuda. I think we need to be very clear about this if we're not going to maintain the latest and greatest. (we're currently on 11.4, but 11.7 and associated drivers are available)