Advanced Usage & Optimization๏ƒ

1. Configuration & Precision๏ƒ

HPFRACC allows for fine-grained control over numerical precision and logging.

from hpfracc.core.utilities import set_default_precision, setup_logging

# Set to 64-bit precision for high-stakes research
set_default_precision(64)

# Enable detailed logging to a file
setup_logging(level="DEBUG", log_file="research_run.log")

2. Intelligent Backend Selection๏ƒ

The library includes an autonomous system that selects the best computational engine based on your hardware and data size.

  • Learning Mode: If enabled, the library benchmarks operations on your specific hardware and โ€œremembersโ€ which backend was fastest for a given data size.

  • Manual Override: You can force a specific backend if required (e.g., for debugging).

from hpfracc.ml.backends import BackendManager, BackendType

# Force JAX for XLA-optimized spectral operations
BackendManager.set_backend(BackendType.JAX)

3. Known Limitations & Workarounds๏ƒ

We maintain high standards of mathematical rigor. Where a feature is not yet fully mature, it is documented here:

  • Matrix Diffusion in fSDEs: Full matrix noise is not yet implemented. Use Scalar or Vector (diagonal) noise as a substitute.

  • FFT Convolution Axis: Currently only supported on the first dimension (time axis). Transpose your tensors accordingly.

  • Adaptive ODE Solving: This feature is currently in โ€œexperimentalโ€ status. We recommend using Fixed-Step Solvers with a small $h$ for production research.

4. Troubleshooting๏ƒ

  • PyTorch/JAX CUDA Conflict: If you have version mismatches between backends, use the included environment setup scripts:

    source scripts/setup_jax_gpu_env.sh

  • Memory Issues: For large 3D datasets, use the Stochastic Layers to reduce the cache size.