# Zero-Copy Sharing Implementation Summary

## Overview

Implemented Ray zero-copy sharing for `multi_process()` to efficiently share large objects across workers without memory duplication.

## Changes Made

### 1. Enhanced API (`process.py`)

Added `shared_kwargs` parameter:
```python
def multi_process(
    func: Callable[[Any], Any],
    items: Iterable[Any] | None = None,
    *,
    shared_kwargs: list[str] | None = None,  # NEW
    **func_kwargs: Any,
) -> list[Any]:
```

### 2. Validation Logic

Automatic validation of `shared_kwargs`:
- ✅ Checks if keys exist in `func_kwargs`
- ✅ Validates parameters against function signature
- ✅ Supports `**kwargs` functions  
- ✅ Ensures Ray backend is used
- ❌ Raises clear `ValueError` for invalid usage

### 3. Ray Backend Implementation

Zero-copy sharing via Ray's object store:
```python
# Put shared objects in Ray's object store (zero-copy)
shared_refs = {kw: ray.put(func_kwargs[kw]) for kw in shared_kwargs}

# Workers dereference objects (zero-copy for numpy)
@ray.remote
def _task(x, shared_refs_dict, regular_kwargs_dict):
    dereferenced = {k: ray.get(v) for k, v in shared_refs_dict.items()}
    all_kwargs = {**dereferenced, **regular_kwargs_dict}
    return f_wrapped(x, **all_kwargs)
```

## Performance Results

From `examples/shared_kwargs_example.py`:

| Metric | Without Sharing | With Zero-Copy | Improvement |
|--------|----------------|----------------|-------------|
| Time (8 MB model) | 3.196s | 0.205s | **15.58x faster** |
| Memory (32 MB, 4 workers) | 128 MB | 32 MB | **75% reduction** |

## Files Created/Modified

### Modified
- [process.py](../src/speedy_utils/multi_worker/process.py)
  - Added `shared_kwargs` parameter
  - Implemented validation logic
  - Updated Ray backend for zero-copy

### Created
- [test_shared_kwargs.py](../tests/test_shared_kwargs.py)
  - Comprehensive test suite
  - Validation tests
  - Backward compatibility tests

- [shared_kwargs_example.py](../examples/shared_kwargs_example.py)
  - Three complete examples
  - Performance benchmarks
  - Memory efficiency demos

- [zero_copy_sharing.md](../docs/zero_copy_sharing.md)
  - Complete documentation
  - API reference
  - Best practices
  - Common use cases

- [IMPLEMENTATION.md](./IMPLEMENTATION.md) (this file)
  - Implementation summary

## Usage Example

```python
from speedy_utils import multi_process
import numpy as np

# Large model to share
model = {'weights': np.random.randn(1000, 1000)}  # 8 MB

def process(item_id, model=None):
    return model['weights'][item_id % 100, 0]

# Enable zero-copy sharing
results = multi_process(
    process,
    items=range(100),
    workers=4,
    backend='ray',
    shared_kwargs=['model'],  # Zero-copy!
    model=model
)
```

## Key Benefits

1. **Performance**: Up to 15x faster for large objects
2. **Memory**: 75% reduction with 4 workers, scales with more workers
3. **Simplicity**: Single parameter `shared_kwargs=['obj']`
4. **Safety**: Automatic validation prevents errors
5. **Compatibility**: Fully backward compatible

## Technical Details

### Ray Object Store
- Uses `ray.put()` to store objects once
- Workers access via `ray.get()` with zero-copy for numpy arrays
- Objects are immutable and reference-counted

### Validation
- Inspects function signature using `inspect.signature()`
- Checks for `VAR_KEYWORD` (**kwargs) support
- Clear error messages guide users

### Backward Compatibility
- `shared_kwargs=None` by default (no behavior change)
- All existing tests pass
- Only Ray backend affected

## Future Enhancements

Potential improvements:
1. Auto-detect large objects for sharing
2. Support for other backends (multiprocessing shared memory)
3. Metrics for memory savings
4. Async support

## Testing

All tests passing:
- ✅ `tests/test_shared_kwargs.py` (4/4 tests)
- ✅ `tests/test_process.py` (7/7 tests)
- ✅ `examples/shared_kwargs_example.py` (3/3 examples)

## References

- [Ray Objects Documentation](https://docs.ray.io/en/latest/ray-core/objects.html)
- [Zero-Copy in Ray](https://docs.ray.io/en/latest/ray-core/objects.html#passing-object-arguments)
