# Advanced CUDA Stream Management

This document describes the advanced stream management implementation for the ToRSh CUDA backend, providing comprehensive async operations, performance optimization, and intelligent coordination.

## Overview

The advanced stream management system extends the basic CUDA stream functionality with sophisticated features designed for high-performance deep learning workloads. It provides automatic optimization, intelligent resource allocation, and comprehensive performance monitoring.

## Key Features

### 1. Priority-Based Stream Scheduling
- **StreamPriority::High**: For critical compute operations
- **StreamPriority::Normal**: For standard operations  
- **StreamPriority::Low**: For background tasks and coordination

### 2. Workload-Aware Stream Allocation
- **WorkloadType::Compute**: Computation-heavy kernels
- **WorkloadType::Memory**: Memory-bound operations
- **WorkloadType::Mixed**: Balanced compute and memory
- **WorkloadType::Coordination**: Synchronization operations

### 3. Advanced Allocation Strategies
- **RoundRobin**: Simple round-robin allocation
- **LoadBalanced**: Based on current stream utilization
- **Priority**: Priority-based allocation
- **Workload**: Optimized for specific workload characteristics

### 4. Async Memory Operations
- Stream-aware memory copying (host↔device, device↔device)
- Asynchronous unified memory prefetching
- Stream-ordered memory operations
- Non-blocking memory set operations

## Architecture

```
┌─────────────────────────────────────────────────────────────┐
│                Advanced Stream Management                    │
├─────────────────────────────────────────────────────────────┤
│  AdvancedStreamPool                                         │
│  ├── Priority-based allocation                              │
│  ├── Workload-aware optimization                            │
│  ├── Load balancing                                         │
│  └── Performance metrics collection                         │
├─────────────────────────────────────────────────────────────┤
│  Enhanced CudaStream                                        │
│  ├── Async memory operations                                │
│  ├── Stream callbacks                                       │
│  ├── Dependency management                                  │
│  ├── Performance metrics                                    │
│  └── Prefetching capabilities                               │
├─────────────────────────────────────────────────────────────┤
│  MultiStreamCoordinator                                     │
│  ├── Dependency graph management                            │
│  ├── Barrier synchronization                                │
│  ├── Parallel execution                                     │
│  ├── Deadlock detection                                     │
│  └── Completion callbacks                                   │
├─────────────────────────────────────────────────────────────┤
│  StreamOrderedAllocator                                     │
│  ├── Stream-specific memory pools                           │
│  ├── Dependency tracking                                    │
│  ├── Automatic cleanup                                      │
│  └── Size-class optimization                                │
├─────────────────────────────────────────────────────────────┤
│  StreamProfiler                                             │
│  ├── Operation timing                                       │
│  ├── Memory transfer tracking                               │
│  ├── Kernel launch monitoring                               │
│  └── Comprehensive reporting                                │
└─────────────────────────────────────────────────────────────┘
```

## Enhanced CudaStream Features

### Priority-Based Construction
```rust
let high_priority_stream = CudaStream::new_with_priority(StreamPriority::High)?;
let normal_stream = CudaStream::new_with_priority(StreamPriority::Normal)?;
let low_priority_stream = CudaStream::new_with_priority(StreamPriority::Low)?;
```

### Async Memory Operations
```rust
// Async host-to-device copy
stream.copy_from_host_async(device_ptr, &host_data)?;

// Async device-to-host copy
stream.copy_to_host_async(&mut host_buffer, device_ptr)?;

// Async device-to-device copy
stream.copy_device_to_device_async(dst_ptr, src_ptr, count)?;

// Async unified memory prefetching
stream.prefetch_to_device_async(unified_ptr, size, Some(device_id))?;
stream.prefetch_to_host_async(unified_ptr, size)?;
```

### Stream Callbacks
```rust
stream.add_callback(|| {
    println!("Stream operation completed!");
});

// Callbacks are executed automatically on synchronization
stream.synchronize()?;
```

### Performance Metrics
```rust
// Automatic metrics collection
stream.record_kernel_launch();
stream.update_peak_memory(memory_usage);

// Retrieve metrics
let metrics = stream.metrics();
println!("Operations: {}", metrics.operations_count);
println!("Memory transfers: {}", metrics.memory_transfers);
println!("Average latency: {:?}", metrics.average_latency);
```

## Advanced Stream Pool

### Workload-Aware Allocation
```rust
let pool = AdvancedStreamPool::new_with_strategy(8, AllocationStrategy::Workload)?;

// Get streams optimized for specific workloads
let compute_stream = pool.get_stream_for_workload(WorkloadType::Compute);
let memory_stream = pool.get_stream_for_workload(WorkloadType::Memory);
let mixed_stream = pool.get_stream_for_workload(WorkloadType::Mixed);
```

### Priority-Specific Allocation
```rust
let high_priority = pool.get_priority_stream(StreamPriority::High);
let normal_priority = pool.get_priority_stream(StreamPriority::Normal);
let low_priority = pool.get_priority_stream(StreamPriority::Low);
```

### Performance Optimization
```rust
// Record workload completion times for optimization
pool.record_workload_completion(WorkloadType::Compute, duration);

// Get optimization insights
let avg_time = pool.average_workload_time(WorkloadType::Compute);

// Automatically optimize allocation strategy
pool.optimize_configuration()?;
```

### Ready Stream Detection
```rust
// Non-blocking check for ready streams
let has_ready = pool.has_ready_streams();

// Wait for any stream to become ready with timeout
if let Ok(Some(ready_stream)) = pool.wait_for_any_ready(Some(timeout)) {
    // Use the ready stream immediately
    println!("Found ready stream: {}", ready_stream.id());
}
```

## Multi-Stream Coordination

### Dependency Management
```rust
let mut coordinator = MultiStreamCoordinator::new(streams);

// Create dependencies between streams
coordinator.add_dependency(&dependent_stream, &prerequisite_stream)?;

// Deadlock detection
if coordinator.has_cycles() {
    eprintln!("Warning: Dependency cycle detected!");
}
```

### Barrier Synchronization
```rust
// Create barrier across all streams
coordinator.create_barrier()?;

// All streams will wait for each other
```

### Parallel Execution
```rust
// Execute operations across all streams in parallel
coordinator.execute_parallel(|stream| {
    // Your parallel operation here
    stream.record_kernel_launch();
    Ok(())
})?;
```

### Completion Callbacks
```rust
coordinator.add_completion_callback(&stream, || {
    println!("Stream {} completed its work", stream.id());
});

// Execute callbacks when operations complete
coordinator.execute_callbacks(&stream);
```

## Stream-Ordered Memory Allocation

### Allocation for Specific Streams
```rust
let mut allocator = StreamOrderedAllocator::new();

// Allocate memory tied to specific streams
let alloc1 = allocator.allocate_for_stream(&stream1, 1024 * 1024)?;
let alloc2 = allocator.allocate_for_stream(&stream2, 2048 * 1024)?;
```

### Dependency Tracking
```rust
// Add dependencies between streams
let dependency_event = Arc::new(CudaEvent::new()?);
allocator.add_stream_dependency(&dependent_stream, dependency_event);

// Check if dependencies are satisfied
let satisfied = allocator.dependencies_satisfied(&stream)?;
```

### Automatic Cleanup
```rust
// Free memory when stream operations complete
allocator.free_for_stream(&stream)?;

// Clear dependencies
allocator.clear_dependencies(&stream);
```

## Stream Performance Profiling

### Basic Profiling
```rust
let mut profiler = StreamProfiler::new();
profiler.enable();

// Record operations
profiler.record_operation(&stream, "matrix_multiply", duration);
profiler.record_memory_transfer(&stream);
profiler.record_kernel_launch(&stream);
```

### Individual Stream Reports
```rust
if let Some(report) = profiler.get_stream_report(&stream) {
    println!("Stream {}: {} operations, {:?} total time", 
             report.stream_id, report.operation_count, report.total_time);
    
    for (operation, duration) in &report.operations {
        println!("  {}: {:?}", operation, duration);
    }
}
```

### Comprehensive Analysis
```rust
let comprehensive = profiler.get_comprehensive_report();
println!("Total streams profiled: {}", comprehensive.total_streams);

for stream_report in &comprehensive.streams {
    println!("Stream {}: {} kernels, {} transfers", 
             stream_report.stream_id, 
             stream_report.kernel_launches,
             stream_report.memory_transfers);
}
```

## Performance Considerations

### Benefits
1. **Intelligent Resource Allocation**: Workload-aware stream selection optimizes GPU utilization
2. **Reduced Synchronization Overhead**: Smart dependency management minimizes blocking
3. **Memory Efficiency**: Stream-ordered allocation reduces fragmentation
4. **Performance Insights**: Comprehensive profiling enables optimization
5. **Async Operations**: Non-blocking memory transfers improve throughput

### Best Practices

#### Stream Priority Usage
- Use **High** priority for critical compute kernels
- Use **Normal** priority for standard operations
- Use **Low** priority for background tasks and synchronization

#### Workload Type Selection
- **Compute**: Matrix multiplications, convolutions, complex math
- **Memory**: Large data transfers, memory copies
- **Mixed**: General neural network operations
- **Coordination**: Barriers, synchronization points

#### Memory Management
- Use stream-ordered allocation for better memory locality
- Prefer async memory operations for better overlap
- Monitor memory usage with profiling tools

#### Optimization Strategies
- Record workload completion times for automatic optimization
- Use load-balanced allocation for dynamic workloads
- Profile operations to identify bottlenecks
- Implement proper dependency management to avoid deadlocks

## Integration with Existing Code

### Migration from Basic Streams
```rust
// Old approach
let stream = CudaStream::new()?;
stream.synchronize()?;

// New approach with advanced features
let stream = CudaStream::new_with_priority(StreamPriority::High)?;
stream.add_callback(|| println!("Operation completed"));
stream.record_kernel_launch();
let metrics = stream.metrics();
stream.synchronize()?;
```

### Pool Integration
```rust
// Replace manual stream management
let pool = AdvancedStreamPool::new_with_strategy(8, AllocationStrategy::Workload)?;

// Get optimized streams for different workloads
let stream = pool.get_stream_for_workload(WorkloadType::Compute);
```

## Error Handling

The implementation provides comprehensive error handling for all operations:

```rust
// Handle stream creation errors
match CudaStream::new_with_priority(StreamPriority::High) {
    Ok(stream) => { /* use stream */ },
    Err(e) => eprintln!("Failed to create stream: {}", e),
}

// Handle allocation errors
match allocator.allocate_for_stream(&stream, size) {
    Ok(allocation) => { /* use allocation */ },
    Err(e) => eprintln!("Allocation failed: {}", e),
}
```

## Future Enhancements

### Planned Features
1. **Machine Learning-Based Optimization**: AI-driven stream allocation
2. **Dynamic Pool Resizing**: Automatic pool size adjustment
3. **Multi-GPU Coordination**: Cross-GPU stream management
4. **Hardware-Specific Optimization**: Device-specific tuning

### Research Areas
1. **Predictive Prefetching**: ML-based memory prefetch prediction
2. **Workload Classification**: Automatic workload type detection
3. **Energy Optimization**: Power-aware stream scheduling

## Conclusion

The advanced CUDA stream management system provides a comprehensive solution for high-performance GPU computing in ToRSh. By combining intelligent allocation strategies, performance monitoring, and sophisticated coordination mechanisms, it enables optimal utilization of GPU resources while maintaining ease of use and robust error handling.

This implementation brings ToRSh's CUDA backend performance capabilities in line with PyTorch while providing additional optimization features that leverage Rust's safety and performance advantages.