Troubleshooting Common Issues in OpenMM ZephyrOpenMM Zephyr is a high-performance branch/extension of the OpenMM molecular simulation toolkit designed to leverage modern hardware and provide advanced features for running molecular dynamics (MD). While powerful, Zephyr introduces additional complexity that can lead to configuration, performance, and correctness issues. This article walks through common problems users encounter with OpenMM Zephyr, diagnostic steps, and practical fixes — from installation and build issues to runtime errors, numerical instabilities, and performance tuning.
1. Installation and build problems
Common symptoms
- Build fails with missing dependencies or compiler errors.
- Python package installation via pip fails or Zephyr import raises ModuleNotFoundError.
- GPU backend not detected or CUDA/OpenCL errors appear.
Diagnosis steps
- Verify system requirements: supported OS, GPU drivers, CUDA toolkit (if using CUDA), and matching compiler toolchain.
- Check which OpenMM and Zephyr versions you attempted to install and whether prebuilt wheels are available for your platform.
- Inspect pip/conda output for missing libraries (e.g., CUDA runtime, libOpenCL, Eigen) and check ldconfig (Linux) or PATH/DYLD_LIBRARY_PATH environment variables.
- Use python -c “import openmm; print(openmm.Platform.getPlatformByName(‘CUDA’))” (or similar) to confirm backend availability; catch exceptions to read error messages.
Fixes
- Use the recommended Python distribution (often CPython 3.8–3.11 depending on the release) and create a clean virtual environment.
- Install matching GPU drivers and CUDA toolkit versions required by the Zephyr build. For CUDA, ensure nvcc and the CUDA runtime libraries are on PATH/LD_LIBRARY_PATH.
- If pip wheel not available, build from source following Zephyr’s README: install build-essential, CMake, SWIG, Eigen, and other dev packages. Use CMake options to point to CUDA or OpenCL SDKs.
- On macOS with Apple Silicon, prefer CPU builds or Metal backend support if Zephyr provides it; ensure correct Homebrew-installed dependencies.
- For import errors related to shared libraries, use ldd (Linux) or otool (macOS) on the openmm shared objects to find missing dependencies.
2. Platform/backend selection issues
Common symptoms
- Simulation runs on CPU instead of GPU.
- Errors selecting CUDA/OpenCL/Metal platforms.
- Unexpected fallback to Reference or CPU platform with poor performance.
Diagnosis steps
- Query available platforms with Python:
from simtk.openmm import Platform for i in range(Platform.getNumPlatforms()): p = Platform.getPlatform(i) print(i, p.getName()) - Check environment variables like OPENMM_CUDA_DEVICE or CUDA_VISIBLE_DEVICES that influence GPU selection.
- Read platform-specific error messages which often explain why a device was rejected (e.g., unsupported compute capability, insufficient memory).
Fixes
- Explicitly select the desired platform and set properties:
platform = Platform.getPlatformByName('CUDA') properties = {'CudaDeviceIndex':'0', 'Precision':'mixed'} system = openmm.System() integrator = openmm.LangevinIntegrator(...) simulation = app.Simulation(topology, system, integrator, platform, properties) - Ensure GPU has sufficient memory for your system; reduce PME grid, decrease cutoffs, or use fewer atoms per replica.
- Update drivers/CUDA to match the Zephyr/CUDA toolkit compatibility matrix.
- For multi-GPU machines, set CUDA_VISIBLE_DEVICES to control which GPUs Zephyr sees.
3. Precision and numerical stability problems
Common symptoms
- Energy drift over NVE simulations.
- Simulation crashes or NaNs appear in energies/forces.
- Diverging temperatures or unstable trajectories.
Diagnosis steps
- Determine simulation precision: single, mixed, or double. Mixed precision is usually best for speed/stability tradeoff.
- Run short energy/force checks after system creation: minimize and report energy; compare single-step energies across platforms/precisions.
- Monitor maximum force magnitudes and look for NaNs or infinities.
Fixes
- Use mixed or double precision for sensitive simulations (e.g., long NVE runs or where energy conservation matters).
- Tighten tolerance for integrator or constraints; use smaller time steps (e.g., 1 fs instead of 2 fs) to recover stability.
- Check the topology and force field for errors: missing bonds, zero-mass particles, overlapping atoms, or unrealistic parameters. Visualize initial structure.
- Apply constraint algorithms (SHAKE/Hamiltonian constraints) correctly when using rigid bonds; avoid incompatible combinations of constraints and integrators.
- If NaNs appear after restarting from a saved state, ensure the checkpoint file matches the Zephyr/OpenMM version and precision.
4. Force field and parameterization errors
Common symptoms
- Unexpected energies or forces for bonded/nonbonded interactions.
- Large bond/angle/torsion forces causing instability.
- Differences between other MD engines (e.g., AMBER, GROMACS) and Zephyr results.
Diagnosis steps
- Perform energy decomposition: compute and print individual force group energies to isolate problem terms.
- Compare topology and parameters (charges, atom types, exclusions, scaling factors) between the source and the Zephyr-loaded system.
- Validate PME/reciprocal space parameters: grid spacing, spline order, and switching functions.
Fixes
- Confirm that input files were converted correctly (AMBER/CHARMM/GROMACS → OpenMM). Use official parsers (app.PDBFile, AMBERPrmtopFile, etc.) or vetted conversion tools.
- Adjust nonbonded cutoff and switching functions to match the reference engine for apples-to-apples comparison.
- If using custom forces or UDFs, validate analytic expressions and unit consistency. Add unit tests for small systems with known reference energies.
- Re-parameterize problematic residues or use alternative force field versions if specific residues fail.
5. Performance and scaling issues
Common symptoms
- GPU underutilization or low throughput.
- Poor scaling with system size or number of GPUs.
- Long initialization times or slow neighbor list updates.
Diagnosis steps
- Measure GPU utilization with tools like nvidia-smi, nvprof, or Nsight. Check CPU usage and memory transfer patterns.
- Profile OpenMM Zephyr if profiling hooks are available (timers, verbose logging) to find hotspots.
- Benchmark with different platform properties (precision, verlet integrator vs. legacy), PME settings, and neighbor list frequencies.
Fixes
- Use mixed precision for a balance of performance and accuracy; single precision only for less sensitive runs where speed is paramount.
- Tune CUDA/OpenCL properties: adjust CudaPrecision, CudaDeviceIndex, and force-accumulate settings exposed by Zephyr.
- Increase PME grid spacing or use larger FFT batch sizes when appropriate to reduce PME overhead.
- Reduce frequency of full neighbor list rebuilds where supported; use Verlet/dual-range neighbor lists for better GPU efficiency.
- For multi-GPU, use domain decomposition or replicate-to-multiple-GPUs strategies supported by Zephyr; ensure inter-GPU communication (NVLink) is available and enabled.
6. Reproducibility and random seed issues
Common symptoms
- Different trajectories for the same input and seed.
- Difficulty reproducing a colleague’s results.
Diagnosis steps
- Confirm the same OpenMM/Zephyr version, platform, precision, and hardware.
- Ensure random seed is set explicitly in the integrator or simulation.
- Check whether parallel algorithms or non-deterministic device operations are used.
Fixes
- Use deterministic settings: set the seed in the integrator (e.g., LangevinIntegrator.setRandomNumberSeed).
- For bitwise reproducibility, use the Reference or CPU platform with double precision; GPU platforms may be non-deterministic due to parallel reductions.
- Document and fix all simulation properties (thermostat, barostat, constraint tolerances, PME settings) to enable reproducibility.
7. Checkpointing and restart problems
Common symptoms
- Checkpoint files fail to load.
- Restarted simulations show discontinuities in energy or velocities.
Diagnosis steps
- Confirm checkpoint file compatibility with Zephyr version used to write it.
- Inspect checkpoint timestamps and file integrity.
- Compare simulation state (positions, velocities, box vectors) before and after restart.
Fixes
- Prefer OpenMM’s XML state serialization for portability, or keep Zephyr/OpenMM versions identical when using binary checkpoints.
- When restarting, reinitialize integrators if required and ensure random seeds are handled correctly (to avoid repeating identical random streams).
- Validate that platform-specific properties (device index) are compatible at restart time.
8. Errors with plugins and custom kernels
Common symptoms
- Plugin load failures or symbol collisions.
- Kernel compilation errors or runtime assertion failures.
Diagnosis steps
- Check plugin compatibility with Zephyr/OpenMM versions and the compiler toolchain.
- Read plugin build logs for missing headers, mismatched ABI, or incompatible CUDA architectures.
- Use verbose logging to capture kernel compile output.
Fixes
- Rebuild plugins against the exact Zephyr/OpenMM headers and libraries you are using.
- Ensure consistent compiler versions and C++ ABI settings (e.g., libstdc++ ABI) across OpenMM/Zephyr and plugin builds.
- When possible, prefer Python-level custom forces or OpenMM’s CustomIntegrator instead of binary plugins for portability.
9. I/O, file format, and conversion quirks
Common symptoms
- PDB, PSF, PRMTOP, or GRO files load with incorrect atom order or missing metadata.
- Unit/scale mismatches (e.g., angstroms vs. nanometers).
- Trajectory viewers show distorted structures.
Diagnosis steps
- Inspect headers and units in files; confirm whether tools expect nm vs Å.
- Print atom order from the loaded topology and compare with original files.
- Visualize the trajectory and initial frame in a known viewer to identify systematic scaling or ordering errors.
Fixes
- Normalize units during file conversion: OpenMM’s app layer expects nanometers for distances and kilojoules/mol for energies.
- Use official parsers and keep consistent toolchains for conversion; avoid ad-hoc text edits of binary or formatted files.
- When converting between frameworks, validate small systems first before processing large systems.
10. Community, debugging resources, and reporting bugs
- Check Zephyr/OpenMM release notes and compatibility matrices before upgrading.
- Reproduce issues in a minimal example (small system, short run) to isolate the problem.
- Collect diagnostics for bug reports: OpenMM/Zephyr version, platform, precision, GPU driver, minimal input files, error messages, and profiler logs.
- Report reproducible bugs to the Zephyr/OpenMM issue tracker with the above artifacts; include commands used to build or run.
If you want, I can:
- Reproduce a specific error from your logs and suggest exact commands to fix it.
- Provide a minimal repro script for a problem you’re seeing (attach the error/traceback and a short description).
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