WALLE2 Cluster

General Information

• Walle2 is a private cluster. Access is restricted to the ek436_0001 group.
• Head node: walle2.cac.cornell.edu (access via ssh)
  • OpenHPC deployment running Rocky Linux 8
  • Cluster scheduler: slurm (See CAC slurm documentation for more info)
• compute nodes:
  • 4 non-GPU nodes (c0001-c0004)
  • 2 GPU nodes (g0001: 2 x NVidia V100, g0002: 2 x NVidia T4)
• data on the walle cluster storage is NOT backed up
• Please send any questions and report problems to: cac-help@cornell.edu

Scheduler/Queues

Slurm will create the required Red Cloud instance to run the jobs submitted to the partition. If no partition is specified in the sbatch or srun command, the normal partition is the default.

To request access to GPUs for your jobs,

1. Submit the job to the gpu partition using the -p option.
2. • Use --gres=gpu:v100:<number of GPUs> to request NVidia V100 GPUs. The job will land on node g0001.
   • Use --gres=gpu:t4:<number of GPUs> to request NVidia T4 GPUs. The job will land on node g0002.

See the Requesting GPUs section for information on how to request GPUs on compute nodes for your jobs.

Slurm Partitions

Slurm Partition	Description	Time Limit
normal*	Non GPU nodes	None
gpu	Nvidia V100 or T4 GPU nodes	None

Software

Work with Environment Modules

Set up the working environment for each package using the module command. The module command will activate dependent modules if there are any.

To show currently loaded modules: (These modules are loaded by default system configurations)

-bash-4.2$ module list

Currently Loaded Modules:
  1) autotools   3) gnu9/9.3.0   5) libfabric/1.10.1   7) ohpc
  2) prun/2.0    4) ucx/1.8.0    6) openmpi4/4.0.4

To show all available modules:

-bash-4.2$ module avail

------------------- /opt/ohpc/pub/moduledeps/gnu9-openmpi4 --------------------
   adios/1.13.1            netcdf-fortran/4.5.2        py3-mpi4py/3.0.3
   boost/1.75.0            netcdf/4.7.3                py3-scipy/1.5.1
   fftw/3.3.8       (L)    opencoarrays/2.9.2          scalapack/2.1.0
   hypre/2.18.1            petsc/3.14.4                slepc/3.14.2
   jdftx/1.6.0      (L)    petsc/3.15.0         (D)    slepc/3.15.0       (D)
   mfem/4.2                phdf5/1.10.6                superlu_dist/6.1.1
   mumps/5.2.1             pnetcdf/1.12.1              trilinos/13.0.0
   netcdf-cxx/4.3.1        ptscotch/6.0.6

------------------------ /opt/ohpc/pub/moduledeps/gnu9 ------------------------
   gsl/2.6       (L)    mpich/3.3.2-ofi        py3-numpy/1.19.0
   hdf5/1.10.6   (L)    mvapich2/2.3.4         superlu/5.2.1
   impi/2021.2.0        openblas/0.3.7  (L)
   metis/5.1.0          openmpi4/4.0.5  (L)

-------------------------- /opt/ohpc/pub/modulefiles --------------------------
   autotools          (L)    julia/1.6.1             os
   cmake/3.19.4              libfabric/1.11.2 (L)    prun/2.1  (L)
   cuda/11.3                 mkl/2021.2.0.610 (L)    py3-libs
   gnu9/9.3.0         (L)    octave/6.3.0     (L)    ucx/1.9.0 (L)
   intel/2021.2.0.610        ohpc             (L)

  Where:
   D:  Default Module
   L:  Module is loaded

Use "module spider" to find all possible modules and extensions.
Use "module keyword key1 key2 ..." to search for all possible modules matching
any of the "keys".

To load a module and verify:

-bash-4.2$ module load cmake
-bash-4.2$ module list

Currently Loaded Modules:
  1) autotools   3) gnu9/9.3.0   5) libfabric/1.10.1   7) ohpc
  2) prun/2.0    4) ucx/1.8.0    6) openmpi4/4.0.4     8) cmake/3.16.2

Manage Modules in Your Python Virtual Environment

python 3.6.8 is installed. Users can manage their own python environment (including installing needed modules) using virtual environments. Please see the documentation on virtual environments on python.org for details.

Create Virtual Environment

You can create as many virtual environments, each in their own directory, as needed.

python3 -m venv <your virtual environment directory>

Activate Virtual Environment

You need to activate a virtual environment before using it:

source <your virtual environment directory>/bin/activate

Install Python Modules Using pip

After activating your virtual environment, you can now install python modules for the activated environment:

• It's always a good idea to update pip first:

pip install --upgrade pip

• Install the module:

pip install <module name>

• List installed python modules in the environment:

pip list modules

• Examples: Install tensorflow and keras like this:

-bash-4.2$ python3 -m venv tensorflow
-bash-4.2$ source tensorflow/bin/activate
(tensorflow) -bash-4.2$ pip install --upgrade pip
Collecting pip
  Using cached https://files.pythonhosted.org/packages/30/db/9e38760b32e3e7f40cce46dd5fb107b8c73840df38f0046d8e6514e675a1/pip-19.2.3-py2.py3-none-any.whl
Installing collected packages: pip
  Found existing installation: pip 18.1
    Uninstalling pip-18.1:
      Successfully uninstalled pip-18.1
Successfully installed pip-19.2.3
(tensorflow) -bash-4.2$ pip install tensorflow keras
Collecting tensorflow
  Using cached https://files.pythonhosted.org/packages/de/f0/96fb2e0412ae9692dbf400e5b04432885f677ad6241c088ccc5fe7724d69/tensorflow-1.14.0-cp36-cp36m-manylinux1_x86_64.whl
:
:
:
Successfully installed absl-py-0.8.0 astor-0.8.0 gast-0.2.2 google-pasta-0.1.7 grpcio-1.23.0 h5py-2.9.0 keras-2.2.5 keras-applications-1.0.8 keras-preprocessing-1.1.0 markdown-3.1.1 numpy-1.17.1 protobuf-3.9.1 pyyaml-5.1.2 scipy-1.3.1 six-1.12.0 tensorboard-1.14.0 tensorflow-1.14.0 tensorflow-estimator-1.14.0 termcolor-1.1.0 werkzeug-0.15.5 wheel-0.33.6 wrapt-1.11.2
(tensorflow) -bash-4.2$ pip list modules
Package              Version
-------------------- -------
absl-py              0.8.0  
astor                0.8.0  
gast                 0.2.2  
google-pasta         0.1.7  
grpcio               1.23.0 
h5py                 2.9.0  
Keras                2.2.5  
Keras-Applications   1.0.8  
Keras-Preprocessing  1.1.0  
Markdown             3.1.1  
numpy                1.17.1 
pip                  19.2.3 
protobuf             3.9.1  
PyYAML               5.1.2  
scipy                1.3.1  
setuptools           40.6.2 
six                  1.12.0 
tensorboard          1.14.0 
tensorflow           1.14.0 
tensorflow-estimator 1.14.0 
termcolor            1.1.0  
Werkzeug             0.15.5 
wheel                0.33.6 
wrapt                1.11.2 

Software List

Software	Path	Notes
*GNU Compilers 9.3.0	/opt/ohpc/pub/compiler/gcc/9.3.0	module load gnu9/9.3.0
Intel Compilers (2021 Update 2)	/opt/intel/oneapi/compiler/2021.2.0	module load intel/2021.2.0.610
MKL 2021.2.0.610 (2021 Update 2)	/opt/intel/oneapi/mkl/2021.2.0	module load mkl/2021.2.0.610
*openmpi 4.0.5	/opt/ohpc/pub/mpi/openmpi4-gnu9 /opt/ohpc/pub/mpi/openmpi4-intel	module load openmpi4
Intel MPI 2021.2.0	/opt/intel/oneapi/mpi/2021.2.0	module load impi/2021.2.0
Julia 1.6.1	/opt/ohpc/pub/compiler/julia/1.6.1	module load julia/1.6.1
JDFTx 1.6.0	/opt/ohpc/pub/apps/jdftx/1.6.0	module load jdftx/1.6.0
CUDA 11.5	/usr/local/cuda-11.5	module load cuda/11.5
petsc / petsc4py 3.15.0	/opt/ohpc/pub/libs/gnu9/openmpi4/petsc/3.15.0 /opt/ohpc/pub/libs/intel/impi/petsc/3.15.0	module load petsc/3.15.0
slepc / slepc4py 3.15.0	/opt/ohpc/pub/libs/gnu9/openmpi4/slepc/3.15.0 /opt/ohpc/pub/libs/intel/impi/slepc/3.15.0	module load slepc/3.15.0
Python3 Modules (pytorch with CUDA support, numpy, scipy, matplotlib, pandas tensorflow, keras, sklearn, umap)	/opt/ohpc/pub/utils/py3-libs/	module load py3-libs