Performance optimization and modern ML backends #64
Description
Summary
Optimize HTFA implementation for maximum performance using modern ML frameworks and hardware acceleration.
Tasks
Modern Framework Integration
- Investigate JAX implementation for automatic differentiation
- Explore NumPy array API standard compliance
- Consider PyTorch backend for GPU acceleration
- Evaluate Numba JIT compilation for critical loops
- Research CuPy for direct CUDA implementations
Algorithm Optimizations
- Profile existing implementation to identify bottlenecks
- Optimize matrix operations and memory usage
- Implement efficient sparse matrix support
- Add support for mini-batch processing
- Investigate alternative optimization algorithms (ADAM, etc.)
Hardware Acceleration
- Add GPU support via CuPy or PyTorch
- Implement Apple Metal Performance Shaders support
- Add multi-threading support for CPU-bound operations
- Optimize for modern CPU architectures (AVX, etc.)
Scalability Improvements
- Add distributed computing support (Dask/Ray)
- Implement online/streaming algorithms for large datasets
- Add checkpointing for long-running optimizations
- Implement progressive refinement strategies
Performance Targets
- 10x speedup over current implementation
- Support for datasets with >100k voxels
- GPU acceleration for compatible operations
- Memory usage scaling improvements
Acceptance Criteria
- Comprehensive performance benchmarks
- Backward compatibility maintained
- Optional dependencies for acceleration frameworks
- Performance improvements validated against BrainIAK