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Reproducible HPC Environments for Crash CAE Teams

30 Chapters

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Reproducibility challenges in crash CAE

sources of nondeterminism, solver version drift, and hardware-dependent results.

Reproducibilitychallenges

HPC environment architecture for crash simulation

cluster topology, job schedulers, and shared storage considerations.

Containerization fundamentals for CAE

Docker, Apptainer/Singularity, and rootless execution on HPC.

ContainerizationCAE

Building reproducible LS-DYNA environments

solver binaries, license integration, and MPI library pinning.

Buildingreproducible

Building reproducible PAM-CRASH environments

version locking, dependency isolation, and runtime configuration.

Buildingreproducible

Environment modules and Spack

managing compiler, MPI, and solver stacks without conflicts.

Environmentmanaging

Conda and Mamba for CAE toolchains

environment export, lockfiles, and cross-cluster portability.

Deterministic MPI configuration

rank mapping, process binding, and network fabric tuning for repeatable crash runs.

DeterministicMPI

Filesystem and I/O reproducibility

parallel file systems, scratch policies, and output checksum verification.

Workflow capture with Snakemake and Nextflow

orchestrating crash simulation pipelines reproducibly.

WorkflowSnakemake

Provenance tracking for crash CAE

capturing inputs, solver flags, and environment metadata automatically.

Provenancetracking

Version control for simulation assets

Git, DVC, and large binary management for deck and result files.

Continuous integration for CAE environments

automated container builds and regression test execution.

Continuousintegration

Reproducible meshing and preprocessing

anchoring ANSA, HyperMesh, and LS-PrePost configurations.

Reproduciblepreprocessing

Material model and card deck reproducibility

parameter versioning, unit consistency, and include-file management.

Materialreproducibility

Solver restart and checkpoint reproducibility

ensuring consistent state recovery across environments.

checkpointreproducibility

Post-processing reproducibility

ParaView, LS-PrePost, and Python scripting for consistent result extraction.

Post-processingreproducibility

Benchmarking and validation suites

designing crash test cases for environment verification.

Benchmarkingvalidation

Cross-platform reproducibility

migrating crash models between on-prem HPC, cloud, and hybrid clusters.

Cross-platformreproducibility

GPU-accelerated crash solvers

reproducibility considerations for CUDA, ROCm, and accelerator libraries.

GPU-acceleratedreproducibility

License server and floating license reproducibility

containerized license clients and fault tolerance.

floatingreproducibility

Security and compliance in reproducible CAE environments

data governance, export control, and audit trails.

Securitycompliance

Performance reproducibility

controlling CPU frequency, turbo boost, and thermal effects on crash results.

Performancereproducibility

Collaborative reproducibility

sharing environments across teams, sites, and external partners.

Collaborativereproducibility

Debugging nondeterministic crash simulations

systematic isolation of environment-induced variability.

Debuggingnondeterministic

Long-term archival and re-execution

preserving environments for regulatory and durability studies.

Long-termarchival

Cloud bursting for crash CAE

reproducible environment portability across schedulers and instance types.

Automated environment validation

smoke tests, golden results, and tolerance-based acceptance criteria.

Automatedenvironment

Integrating reproducible environments with PLM and simulation data management systems.

Integrating reproducible environments with PLM and simulation data management systems.

Integratingreproducible

Capstone project

end-to-end reproducible crash CAE workflow from container build to validated results.

Capstoneend-to-end