Performance Optimization¶
This document provides guidance on optimizing the performance of TorchEBM for both development and usage.
Performance Considerations¶
Key Performance Areas
When working with TorchEBM, pay special attention to these performance-critical areas:
- Sampling algorithms: These are iterative and typically the most compute-intensive
- Gradient calculations: Computing energy gradients is fundamental to many algorithms
- Batch processing: Effective vectorization for parallel processing
- GPU utilization: Proper device management and memory usage
Vectorization Techniques¶
Batched Operations¶
TorchEBM extensively uses batching to improve performance:
GPU Acceleration¶
TorchEBM is designed to work efficiently on GPUs:
Device Management¶
Memory Management¶
Memory management is critical for performance, especially on GPUs:
Custom CUDA Kernels (to be Added--See Also: cuRBLAS)¶
Sampling Efficiency¶
Sampling efficiency can be improved using several techniques:
-
Step Size Adaptation
Automatically adjust step sizes based on acceptance rates or other metrics.
-
Burn-in Period
Discard initial samples to reduce the impact of initialization.
-
Thinning
Reduce correlation between samples by keeping only every Nth sample.
-
Warm Starting
Initialize sampling from a distribution close to the target.
Profiling and Benchmarking (Planned)¶
Performance Benchmarks (Planned)¶
Performance Tips and Best Practices¶
General Tips¶
- Use the right device: Always move computation to GPU when available
- Batch processing: Process data in batches rather than individually
- Reuse tensors: Avoid creating new tensors in inner loops
- Monitor memory: Use
torch.cuda.memory_summary()
to track memory usage
Sampling Tips¶
- Tune step sizes: Optimal step sizes balance exploration and stability
- Parallel chains: Use multiple chains to improve sample diversity
- Adaptive methods: Use adaptive samplers for complex distributions
- Mixed precision: Consider using mixed precision for larger models
Common Pitfalls
Avoid these common performance issues:
- Unnecessary CPU-GPU transfers: Keep data on the same device
- Small batch sizes: Too small batches underutilize hardware
- Unneeded gradient tracking: Disable gradients when not training
- Excessive logging: Logging every step can significantly slow down sampling
Algorithm-Specific Optimizations¶
Langevin Dynamics¶
Hamiltonian Monte Carlo¶
Multi-GPU Scaling (Planned)¶
Conclusion¶
Performance optimization in TorchEBM involves careful attention to vectorization, GPU acceleration, memory management, and algorithm-specific tuning. By following these guidelines, you can achieve significant speedups in your energy-based modeling workflows.