Software

ARC provides pre-built software as lmod modules for the Intel CPUs used on Great Lakes., However, Artemis uses AMD CPUs. Thus the provided modules may or may not work on Artemis.

• Software built for a generic x86/x64 architecture should work
• CPU-heavy codes should target the specific microarchitecture they run on

ARC will not provide pre-built software for our CPUs

Instead, you’ll need to build your own software. I’d recommend using spack

• UM ARC’s Spack Documentation
• Spack’s Documentation
• Spack’s Tutorial
• List of Available Spack Packages
• Public Build Cache

To get spack: module load spack to use the ARC provided instance or install your own

Spack

The following is a suggested configuration for using Spack on Artemis. See spack documentation for more information.

In your ~/.bashrc or equivalent:

# Load the spack module and unset SPACK_DISABLE_LOCAL_CONFIG
# to enable user configuration files in ~/.spack/
module load spack/0.21
unset SPACK_DISABLE_LOCAL_CONFIG

In ~/.spack/concretizer.yaml

concretizer:
  targets:
    # Lighthouse Login Nodes are Haswell, not Zen4
    # Allow host to be incompatible so concretizing on the login
    # nodes is possible
    host_compatible: false

In ~/.spack/packages.yaml

packages:
  openssl:
    # Don't build your own OpenSSL.
    # See: https://spack.readthedocs.io/en/latest/getting_started.html#openssl
    externals:
    - spec: openssl@1.1.1k
      prefix: /usr
    buildable: False

  # Restrict to CUDA provided by ARC
  # Realistically, you could install your own version of CUDA, but it's pretty heavy
  # Note: 'cuda::' overrides the ARC provided defaults, this is needed cause cuda@12.3.0
  # is not currently installed, but listed, causing issues.
  # Note: This should be updated as new versions of CUDA are installed
  cuda::
    buildable: False
    externals:
    - spec: cuda@10.2.89
      modules:
      - cuda/10.2.89
    - spec: cuda@11.2.2
      modules:
      - cuda/11.2.2
    - spec: cuda@11.3.0
      modules:
      - cuda/11.3.0
    - spec: cuda@11.5.1
      modules:
      - cuda/11.5.1
    - spec: cuda@11.6.2
      modules:
      - cuda/11.6.3
    - spec: cuda@11.7.1
      modules:
      - cuda/11.7.1
    - spec: cuda@11.8.0
      modules:
      - cuda/11.8.0
    - spec: cuda@12.1.1
      modules:
      - cuda/12.1.1

  # Versions of cuDNN provided by ARCH
  # Again, could install but a) space and b) get the actual files is tricky (install is easy)
  # Note: This should be updated as new versions of cuDNN are installed
  cudnn:
    buildable: False
    externals:
    - spec: cudnn@8.3.1-10.2
      modules:
      - cudnn/10.2-v8.3.1
    - spec: cudnn@8.1.1-11.2
      modules:
      - cudnn/11.2-v8.1.1
    - spec: cudnn@8.2.1-11.3
      modules:
      - cudnn/11.3-v8.2.1
    - spec: cudnn@8.3.1-11.5
      modules:
      - cudnn/11.5-v8.3.1
    - spec: cudnn@8.4.1-11.6
      modules:
      - cudnn/11.6-v8.4.1
    - spec: cudnn@8.7.0-11.7
      modules:
      - cudnn/11.7-v8.7.0
    - spec: cudnn@8.7.0-11.8
      modules:
      - cudnn/11.8-v8.7.0
    - spec: cudnn@8.9.0-12.1
      modules:
      - cudnn/12.1-v8.9.0

  # The following applies for all packages
  all:
    # Check cuda_arch specification
    require:
    - one_of: ["cuda_arc=80,90", cuda_arch=80, cuda_arch=90]
      when: +cuda
      message: cuda_arch should be 80 (A100) or 90 (H100)

    # Enable certain variants by default
    variants:
    - +cuda             # Enable cuda support when possible

    # Artemis Worker Nodes are Zen4, keep x86_64 as a fallback
    target: [zen4, x84_64]

    # Sets preference for compilers
    compiler:
    - gcc
    - clang
    - aocc

    # The following biases Spack towards AMD specific providers
    providers:
      blas: [amdblis, openblas]
      fftw-api: [amdfftw, fftw]
      flame: [amdlibflame, libflame]
      lapack: [amdlibflame, openblas]
      scalapack: [amdsclapack, netlib-scalapack]

Julia

Minimizing Redundant Precompilation

As Artemis is a heterogeneous cluster, switching between different partitions can trigger frequent precompilation, as Julia attempts to recompile for the current microarchitecture. To avoid this set JULIA_CPU_TARGET to an appropriate setting.

For example, within your ~/.bashrc or equivalent, put:

export JULIA_CPU_TARGET="generic;znver4,clone_all;znver3,clone_all;haswell"

This will instruct Julia to compile for Zen4 (Most Worker Nodes), Zen3 (A100 Nodes) and haswell (Head node), with a generic CPU target as a fallback. This will increase the size of ~/.julia/compiled as 3 sets of system and package images will now need to be stored. For more information see the Julia docs.

Example Precompile Job Script

#!/bin/bash
#SBATCH -p venkvis-cpu
#SBATCH --cpus-per-task 4
#SBATCH --mem-per-cpu 1800M
#SBATCH --time=0:20:00
set -x
my_job_header

# Configure Julia
export JULIA_CPU_TARGET="generic;znver4,clone_all;znver3,clone_all;haswell"
export JULIA_PKG_PRECOMPILE_AUTO=0                        # Disable automatic precompilation so we control when it happens (Mainly for MPI)
export JULIA_NUM_PRECOMPILE_TASKS=$SLURM_CPUS_ON_NODE     # Limit precompilation tasks to the number of CPUs

# Uncomment if using MPI.jl
# module --ignore_cache load openmpi/4.1.6
# julia --color=no --startup-file=no --project -e 'using MPIPreferences; MPIPreferences.use_system_binary()'

# Precompile the Project
julia --color=no --startup-file=no --project -e 'using Pkg; Pkg.resolve(); Pkg.instantiate(); Pkg.precompile(timing=true)'

The job script would then look like:

# [Job Header Goes Here]

export JULIA_CPU_TARGET="generic;znver4,clone_all;znver3,clone_all;haswell"

julia --startup-file=no --project ...

Note: --startup-file=no is needed to avoid loading packages from your base environment (i.e. Revise) and