# Enhanced CUDA Event Coordination System

This document describes the comprehensive event coordination system for the ToRSh CUDA backend, providing operation-level synchronization, dependency tracking, and advanced coordination patterns for high-performance GPU computing.

## Overview

The enhanced event coordination system extends basic CUDA event functionality with sophisticated operation management, automatic dependency resolution, deadlock detection, and performance monitoring. It provides fine-grained control over GPU operation coordination while maintaining ease of use.

## Key Features

### 1. **Event Pool Management**
- Efficient reuse of CUDA events to minimize allocation overhead
- Separate pools for regular and timing-enabled events
- Automatic capacity management and utilization tracking
- Thread-safe acquisition and release mechanisms

### 2. **Operation-Level Coordination**
- Automatic dependency tracking between operations
- Priority-based operation scheduling
- Operation metadata and performance monitoring
- Completion callbacks for asynchronous notification

### 3. **Cross-Stream Synchronization**
- Global barriers across multiple streams
- Automatic event insertion for dependency resolution
- Stream-aware operation coordination
- Deadlock detection and prevention

### 4. **Asynchronous Event Handling**
- Non-blocking event waiting with callbacks
- Background worker for callback execution
- Event cancellation and timeout support
- Scalable async coordination patterns

## Architecture

```
┌─────────────────────────────────────────────────────────────────┐
│                Enhanced Event Coordination System               │
├─────────────────────────────────────────────────────────────────┤
│  EventPool                                                      │
│  ├── Regular event pool (non-timing)                            │
│  ├── Timing event pool (high-precision timing)                  │
│  ├── Usage tracking and metrics                                 │
│  └── Automatic capacity management                              │
├─────────────────────────────────────────────────────────────────┤
│  OperationCoordinator                                           │
│  ├── Operation registration and metadata                        │
│  ├── Dependency graph management                                │
│  ├── Deadlock detection algorithms                              │
│  ├── Priority-based scheduling                                  │
│  ├── Completion callback system                                 │
│  └── Performance metrics collection                             │
├─────────────────────────────────────────────────────────────────┤
│  CrossStreamBarrier                                             │
│  ├── Multi-stream synchronization                               │
│  ├── Barrier event coordination                                 │
│  ├── Stream-specific waiting                                    │
│  └── Timing and performance measurement                         │
├─────────────────────────────────────────────────────────────────┤
│  AsyncEventWaiter                                               │
│  ├── Background worker thread                                   │
│  ├── Non-blocking event monitoring                              │
│  ├── Callback execution system                                  │
│  └── Wait cancellation support                                  │
└─────────────────────────────────────────────────────────────────┘
```

## Core Components

### Event Pool

The `EventPool` provides efficient management of CUDA events with automatic reuse:

```rust
use torsh_backend::cuda::event_coordination::EventPool;

// Create event pool with 20 regular events and 10 timing events
let event_pool = Arc::new(EventPool::new(20, 10)?);

// Acquire events
let regular_event = event_pool.acquire_event(false)?;
let timing_event = event_pool.acquire_event(true)?;

// Use events...

// Return to pool (automatic via Drop trait)
event_pool.release_event(regular_event);
event_pool.release_event(timing_event);

// Monitor utilization
let (available, timing, in_use) = event_pool.utilization();
println!("Pool status: {} available, {} timing, {} in use", available, timing, in_use);
```

### Operation Coordinator

The `OperationCoordinator` manages complex operation dependencies and scheduling:

```rust
use torsh_backend::cuda::event_coordination::{
    OperationCoordinator, OperationType, EventPriority
};

let event_pool = Arc::new(EventPool::new(50, 25)?);
let coordinator = OperationCoordinator::new(event_pool);

// Register operations with dependencies
let data_transfer = coordinator.register_operation(
    OperationType::MemoryTransfer,
    EventPriority::High,
    &stream1,
    vec![], // No dependencies
    "Load input data".to_string(),
)?;

let computation = coordinator.register_operation(
    OperationType::Kernel,
    EventPriority::Normal,
    &stream2,
    vec![data_transfer], // Depends on data transfer
    "Matrix multiplication".to_string(),
)?;

// Add completion callbacks
coordinator.add_completion_callback(data_transfer, || {
    println!("Data transfer completed");
});

// Execute operations
coordinator.begin_operation(data_transfer, &stream1)?;
// ... perform actual GPU operation ...
coordinator.complete_operation(data_transfer)?;

coordinator.begin_operation(computation, &stream2)?;
// ... perform actual GPU operation ...
coordinator.complete_operation(computation)?;
```

### Cross-Stream Barriers

The `CrossStreamBarrier` enables global synchronization across multiple streams:

```rust
use torsh_backend::cuda::event_coordination::CrossStreamBarrier;

let streams = vec![stream1, stream2, stream3, stream4];
let barrier = CrossStreamBarrier::new(streams.clone(), event_pool)?;

// Execute operations on different streams concurrently
for (i, stream) in streams.iter().enumerate() {
    thread::spawn(move || {
        // Perform work on stream
        perform_gpu_work(&stream);
    });
}

// Synchronize all streams at barrier point
let sync_duration = barrier.synchronize()?;
println!("Barrier synchronization took {:?}", sync_duration);
```

### Asynchronous Event Waiting

The `AsyncEventWaiter` provides non-blocking event coordination:

```rust
use torsh_backend::cuda::event_coordination::AsyncEventWaiter;

let async_waiter = AsyncEventWaiter::new();

// Set up async waiting with callback
let wait_id = async_waiter.wait_async(event, || {
    println!("Event completed asynchronously!");
});

// Continue with other work...

// Optionally cancel the wait
if should_cancel {
    async_waiter.cancel_wait(wait_id);
}
```

## Operation Types and Priorities

### Operation Types

The system supports different operation types for intelligent scheduling:

- **`OperationType::Kernel`**: GPU compute kernels
- **`OperationType::MemoryTransfer`**: Host↔device memory operations
- **`OperationType::Synchronization`**: Explicit sync points
- **`OperationType::Reduction`**: Collective reduction operations
- **`OperationType::Broadcast`**: Data distribution operations
- **`OperationType::AllReduce`**: Distributed reductions
- **`OperationType::Barrier`**: Global synchronization barriers

### Event Priorities

Operations can be assigned different priorities for scheduling:

- **`EventPriority::Critical`**: Must execute immediately
- **`EventPriority::High`**: High-priority operations (default for compute)
- **`EventPriority::Normal`**: Standard operations
- **`EventPriority::Low`**: Background operations
- **`EventPriority::Cleanup`**: Maintenance and cleanup tasks

## Advanced Features

### Deadlock Detection

The system automatically detects circular dependencies:

```rust
// The coordinator will detect if operations form cycles
let deadlocks = coordinator.detect_deadlocks();

if !deadlocks.is_empty() {
    for cycle in deadlocks {
        eprintln!("Deadlock detected: {:?}", cycle);
    }
}
```

### Performance Monitoring

Comprehensive metrics are collected automatically:

```rust
let metrics = coordinator.metrics();
println!("Operations: {} total, {} completed", 
         metrics.total_operations, 
         metrics.completed_operations);
println!("Average coordination time: {:?}", metrics.average_coordination_time);
println!("Deadlock detections: {}", metrics.deadlock_detections);
```

### Event Metadata

Rich metadata is maintained for each operation:

```rust
pub struct EventMetadata {
    pub operation_type: OperationType,
    pub priority: EventPriority,
    pub stream_id: u64,
    pub operation_id: u64,
    pub creation_time: Instant,
    pub dependencies: Vec<u64>,
    pub description: String,
}
```

## Usage Patterns

### Basic Operation Coordination

```rust
// Simple sequential operations
let op1 = coordinator.register_operation(
    OperationType::MemoryTransfer, EventPriority::High, &stream, vec![], "Load data"
)?;

let op2 = coordinator.register_operation(
    OperationType::Kernel, EventPriority::Normal, &stream, vec![op1], "Process data"
)?;

coordinator.begin_operation(op1, &stream)?;
coordinator.complete_operation(op1)?;

coordinator.begin_operation(op2, &stream)?;
coordinator.complete_operation(op2)?;
```

### Parallel Pipeline with Synchronization

```rust
// Data preparation phase
let prep_ops: Vec<_> = (0..N).map(|i| {
    coordinator.register_operation(
        OperationType::MemoryTransfer,
        EventPriority::High,
        &memory_stream,
        vec![],
        format!("Prepare data {}", i),
    )
}).collect::<Result<Vec<_>, _>>()?;

// Parallel computation phase
let compute_ops: Vec<_> = compute_streams.iter().enumerate().map(|(i, stream)| {
    coordinator.register_operation(
        OperationType::Kernel,
        EventPriority::Normal,
        stream,
        prep_ops.clone(), // Depend on all prep operations
        format!("Compute {}", i),
    )
}).collect::<Result<Vec<_>, _>>()?;

// Final reduction
let reduction = coordinator.register_operation(
    OperationType::AllReduce,
    EventPriority::Critical,
    &reduction_stream,
    compute_ops.clone(),
    "Final reduction".to_string(),
)?;
```

### Multi-Stream Barrier Synchronization

```rust
let streams = vec![stream1, stream2, stream3];
let barrier = CrossStreamBarrier::new(streams.clone(), event_pool)?;

// Launch operations on all streams
for stream in &streams {
    launch_kernel_on_stream(stream);
}

// Wait for all streams to reach barrier
barrier.synchronize()?;

// Continue with synchronized execution
for stream in &streams {
    launch_next_kernel_on_stream(stream);
}
```

### Asynchronous Coordination

```rust
let async_waiter = AsyncEventWaiter::new();

// Set up multiple async waits
let results = Arc::new(Mutex::new(Vec::new()));

for (i, event) in events.iter().enumerate() {
    let results = Arc::clone(&results);
    async_waiter.wait_async(Arc::clone(event), move || {
        results.lock().unwrap().push(i);
        println!("Operation {} completed", i);
    });
}

// Continue with other work while operations complete asynchronously
```

## Performance Considerations

### Benefits

1. **Reduced Event Allocation Overhead**: Event pooling eliminates repeated allocations
2. **Optimal Dependency Resolution**: Automatic dependency tracking minimizes unnecessary synchronization
3. **Priority-Based Scheduling**: Critical operations get preferential treatment
4. **Deadlock Prevention**: Early detection prevents system hangs
5. **Fine-Grained Coordination**: Operation-level control enables complex patterns

### Best Practices

#### Event Pool Sizing
- Size regular event pool based on maximum concurrent operations
- Size timing event pool based on performance monitoring needs
- Monitor utilization to adjust pool sizes dynamically

#### Operation Registration
- Register operations just before execution to minimize memory usage
- Use descriptive operation names for debugging and profiling
- Group related operations with appropriate priorities

#### Dependency Management
- Keep dependency chains as short as possible
- Avoid unnecessary dependencies between independent operations
- Use barriers sparingly for global synchronization points

#### Priority Assignment
- Use `Critical` priority only for operations that must execute immediately
- Assign `High` priority to compute-intensive kernels
- Use `Low` priority for background operations and cleanup

#### Asynchronous Patterns
- Use async waiting for operations that don't need immediate results
- Cancel unused async waits to free resources
- Batch async operations where possible

## Integration with Existing Code

### Migration from Basic Events

```rust
// Old approach with basic events
let event = CudaEvent::new()?;
stream.record_event(&event)?;
event.synchronize()?;

// New approach with coordination
let event_pool = Arc::new(EventPool::new(10, 5)?);
let coordinator = OperationCoordinator::new(event_pool);

let op_id = coordinator.register_operation(
    OperationType::Kernel,
    EventPriority::Normal,
    &stream,
    vec![],
    "GPU operation".to_string(),
)?;

coordinator.begin_operation(op_id, &stream)?;
// ... perform operation ...
coordinator.complete_operation(op_id)?;
```

### Integration with Stream Management

```rust
use torsh_backend::cuda::{
    AdvancedStreamPool, WorkloadType,
    event_coordination::{EventPool, OperationCoordinator, OperationType, EventPriority}
};

// Combine advanced streams with event coordination
let stream_pool = AdvancedStreamPool::new_with_strategy(8, AllocationStrategy::Workload)?;
let event_pool = Arc::new(EventPool::new(20, 10)?);
let coordinator = OperationCoordinator::new(event_pool);

// Get appropriate stream for workload
let compute_stream = stream_pool.get_stream_for_workload(WorkloadType::Compute);

// Register coordinated operation
let op_id = coordinator.register_operation(
    OperationType::Kernel,
    EventPriority::High,
    &compute_stream,
    dependencies,
    "High-priority computation".to_string(),
)?;
```

## Error Handling

The system provides comprehensive error handling for all coordination operations:

```rust
// Handle operation registration errors
match coordinator.register_operation(operation_type, priority, &stream, deps, desc) {
    Ok(op_id) => {
        // Use operation ID
    },
    Err(e) => {
        eprintln!("Failed to register operation: {}", e);
    }
}

// Handle coordination errors
if let Err(e) = coordinator.begin_operation(op_id, &stream) {
    eprintln!("Failed to begin operation: {}", e);
}

// Handle barrier synchronization errors
match barrier.synchronize() {
    Ok(duration) => println!("Barrier completed in {:?}", duration),
    Err(e) => eprintln!("Barrier synchronization failed: {}", e),
}
```

## Thread Safety

All coordination components are designed for safe concurrent access:

- **EventPool**: Thread-safe event acquisition and release
- **OperationCoordinator**: Safe concurrent operation registration and execution
- **CrossStreamBarrier**: Safe multi-threaded barrier operations
- **AsyncEventWaiter**: Safe async callback execution

## Future Enhancements

### Planned Features

1. **Machine Learning-Based Optimization**: AI-driven dependency prediction
2. **Hierarchical Event Coordination**: Multi-level coordination strategies
3. **Dynamic Priority Adjustment**: Adaptive priority based on performance metrics
4. **Distributed Event Coordination**: Cross-GPU coordination support
5. **Event Compression**: Efficient event batching for high-frequency operations

### Research Areas

1. **Predictive Dependency Tracking**: ML-based dependency prediction
2. **Adaptive Pool Sizing**: Dynamic pool resizing based on usage patterns
3. **Energy-Aware Coordination**: Power-conscious operation scheduling
4. **Real-Time Coordination**: Hard real-time guarantees for critical operations

## Conclusion

The enhanced CUDA event coordination system provides comprehensive operation-level synchronization for the ToRSh framework. By combining efficient event pooling, intelligent dependency tracking, and advanced coordination patterns, it enables sophisticated GPU computing workflows while maintaining excellent performance and ease of use.

This system brings ToRSh's CUDA backend coordination capabilities beyond those available in other frameworks, leveraging Rust's safety guarantees and performance characteristics to provide robust, high-performance GPU operation management.