# Potential Extensions for scirs2-core

This document outlines potential enhancements and extensions for the scirs2-core module.

## Memory Efficiency Enhancements

- Add more zero-copy operations throughout the codebase
- Expand SIMD optimization coverage to more numeric operations
- Further enhance memory-mapped arrays with optimized slicing and indexing operations
- Implement adaptive chunking strategies based on workload patterns

## Parallel Processing Enhancements

- Further optimize parallel chunk processing with better load balancing
- Implement custom partitioning strategies for different data distributions
- Add work-stealing scheduler for more efficient thread utilization
- Support for nested parallelism with controlled resource usage

## GPU Integration

- Expand CUDA/OpenCL support for more operations
- Enhance GPU memory management with smarter caching mechanisms
- Optimize data transfer between CPU and GPU memory
- Support for running same algorithms transparently on both CPU and GPU

## Numerical Computation Enhancements

- Support for arbitrary precision numerical computation
- Improved algorithms for numerical stability
- More efficient implementations of special mathematical functions
- Better handling of edge cases in numeric operations

## I/O and Persistence

- Integration with distributed file systems
- Enhanced serialization/deserialization capabilities
- Improved data format conversion utilities
- Support for more efficient streaming operations on large datasets

## Error Handling and Logging

- More context-rich error reporting
- Enhanced structured logging
- Integration of performance and error diagnostic tools
- Better debugging information for numerical operations

## Type System Extensions

- Richer numerical type hierarchy
- Improved generic code generation
- Enhanced compile-time optimizations
- More ergonomic trait implementations

## Testing Functionality

- Integration of property-based testing
- Enhanced benchmarking capabilities
- Integration of test coverage tools
- Comparison benchmarks against equivalent Python libraries

## Distributed Computing Support

- Building on the memory-mapped chunking capabilities for distributed processing
- Support for multi-node computation
- Resource management across compute clusters
- Integration with distributed compute frameworks

## Memory Metrics and Profiling

- Expand memory metrics collection
- Add more detailed profiling for memory operations
- Visual representations of memory usage patterns
- Memory optimization suggestions based on usage patterns