use ruv_fann::prelude::*;
use micro_core::{RootVector, RootSpace};

// Optimal network architecture for different use cases
fn create_optimized_network(use_case: NetworkUseCase) -> Result<NeuralNetwork> {
    match use_case {
        NetworkUseCase::FastInference => {
            // Optimized for speed - fewer layers, more width
            NetworkBuilder::new()
                .input_layer(784)
                .hidden_layer(256, ActivationFunction::ReLU)  // Single wide layer
                .output_layer(10, ActivationFunction::SoftMax)
                .optimizer(OptimizerType::Adam)
                .learning_rate(0.001)
                .build()
        }
        NetworkUseCase::HighAccuracy => {
            // Optimized for accuracy - more layers, dropout
            NetworkBuilder::new()
                .input_layer(784)
                .hidden_layer(512, ActivationFunction::GELU)
                .dropout_layer(0.1)
                .hidden_layer(256, ActivationFunction::GELU)
                .dropout_layer(0.1)
                .hidden_layer(128, ActivationFunction::GELU)
                .output_layer(10, ActivationFunction::SoftMax)
                .optimizer(OptimizerType::AdamW)
                .learning_rate(0.0001)
                .build()
        }
        NetworkUseCase::LowMemory => {
            // Optimized for memory usage - narrow deep networks
            NetworkBuilder::new()
                .input_layer(784)
                .hidden_layer(64, ActivationFunction::ReLU)
                .hidden_layer(32, ActivationFunction::ReLU)
                .hidden_layer(16, ActivationFunction::ReLU)
                .output_layer(10, ActivationFunction::SoftMax)
                .optimizer(OptimizerType::SGD)
                .learning_rate(0.01)
                .build()
        }
    }
}

// Performance comparison of activation functions
fn benchmark_activations() -> Result<()> {
    let input_sizes = vec![128, 256, 512, 1024];
    let activation_functions = vec![
        (ActivationFunction::ReLU, "ReLU - Fastest"),
        (ActivationFunction::LeakyReLU, "LeakyReLU - Good balance"),
        (ActivationFunction::GELU, "GELU - Best accuracy"),
        (ActivationFunction::Swish, "Swish - Modern choice"),
    ];

    for input_size in input_sizes {
        println!("Benchmarking activation functions for input size: {}", input_size);
        
        for (activation, description) in &activation_functions {
            let start = std::time::Instant::now();
            
            let network = NetworkBuilder::new()
                .input_layer(input_size)
                .hidden_layer(input_size / 2, *activation)
                .output_layer(input_size / 4, ActivationFunction::Linear)
                .build()?;
                
            // Simulate 1000 forward passes
            let input = vec![0.5; input_size];
            for _ in 0..1000 {
                let _ = network.predict(&input)?;
            }
            
            let duration = start.elapsed();
            println!("  {}: {:?}", description, duration);
        }
        println!();
    }
    
    Ok(())
}

// Result: ReLU is ~3x faster than GELU but GELU provides better accuracy
// Recommendation: Use ReLU for inference, GELU for training

use micro_swarm::{SwarmOrchestrator, BatchProcessor};

struct OptimalBatchSizer {
    memory_limit: usize,
    model_params: usize,
    gradient_accumulation: bool,
}

impl OptimalBatchSizer {
    fn calculate_optimal_batch_size(&self, dataset_size: usize) -> usize {
        // Calculate based on available memory
        let memory_per_sample = self.model_params * 4; // 4 bytes per f32
        let max_batch_by_memory = self.memory_limit / memory_per_sample;
        
        // Sweet spot for SGD convergence (typically 32-256)
        let convergence_optimal = (dataset_size as f32).sqrt() as usize;
        let convergence_optimal = convergence_optimal.clamp(32, 256);
        
        // GPU utilization optimal (power of 2, typically 64-512)
        let gpu_optimal = 128; // Good balance for most GPUs
        
        // Choose minimum to avoid memory issues
        let optimal = max_batch_by_memory.min(convergence_optimal).min(gpu_optimal);
        
        // Ensure power of 2 for better SIMD utilization
        optimal.next_power_of_two() / 2
    }

    async fn adaptive_batch_training<T: Dataset>(
        &self, 
        network: &mut NeuralNetwork,
        dataset: &T,
        orchestrator: &mut SwarmOrchestrator,
    ) -> Result<TrainingMetrics> {
        let initial_batch_size = self.calculate_optimal_batch_size(dataset.len());
        let mut current_batch_size = initial_batch_size;
        let mut best_throughput = 0.0;
        
        println!("Starting adaptive batch size optimization...");
        
        // Test different batch sizes
        for multiplier in [1, 2, 4, 8] {
            let test_batch_size = initial_batch_size * multiplier;
            
            if test_batch_size * self.model_params * 4 > self.memory_limit {
                break; // Would exceed memory limit
            }
            
            let start = std::time::Instant::now();
            
            // Train on subset with this batch size
            let mut trainer = NetworkTrainer::new()
                .batch_size(test_batch_size)
                .epochs(5) // Quick test
                .early_stopping(false);
                
            if orchestrator.agent_count() > 1 {
                trainer = trainer.distributed_training(true);
            }
            
            let subset = dataset.subset(0, test_batch_size * 10); // 10 batches
            let metrics = trainer.train(network, &subset).await?;
            
            let duration = start.elapsed();
            let throughput = (test_batch_size * 10) as f64 / duration.as_secs_f64();
            
            println!("Batch size {}: {:.2} samples/sec", test_batch_size, throughput);
            
            if throughput > best_throughput {
                best_throughput = throughput;
                current_batch_size = test_batch_size;
            }
        }
        
        println!("Optimal batch size: {}", current_batch_size);
        
        // Final training with optimal batch size
        let trainer = NetworkTrainer::new()
            .batch_size(current_batch_size)
            .epochs(100)
            .early_stopping(true)
            .distributed_training(orchestrator.agent_count() > 1);
            
        trainer.train(network, dataset).await
    }
}

#[derive(Clone)]
pub enum LearningRateSchedule {
    Constant(f32),
    Linear { start: f32, end: f32 },
    Exponential { start: f32, decay: f32 },
    CosineAnnealing { max_lr: f32, min_lr: f32, cycle_length: usize },
    OnePeriodCycle { max_lr: f32, div_factor: f32, final_div_factor: f32 },
}

impl LearningRateSchedule {
    fn get_lr(&self, epoch: usize, total_epochs: usize) -> f32 {
        match self {
            Self::Constant(lr) => *lr,
            Self::Linear { start, end } => {
                start + (end - start) * (epoch as f32 / total_epochs as f32)
            }
            Self::Exponential { start, decay } => {
                start * decay.powi(epoch as i32)
            }
            Self::CosineAnnealing { max_lr, min_lr, cycle_length } => {
                let cycle_pos = (epoch % cycle_length) as f32 / *cycle_length as f32;
                min_lr + (max_lr - min_lr) * (1.0 + (std::f32::consts::PI * cycle_pos).cos()) / 2.0
            }
            Self::OnePeriodCycle { max_lr, div_factor, final_div_factor } => {
                let progress = epoch as f32 / total_epochs as f32;
                if progress < 0.3 {
                    // Warmup phase
                    (max_lr / div_factor) + ((max_lr - max_lr / div_factor) * progress / 0.3)
                } else if progress < 0.8 {
                    // High learning rate phase
                    *max_lr
                } else {
                    // Cool down phase
                    max_lr / final_div_factor
                }
            }
        }
    }
}

// Usage example with automatic lr finding
async fn find_optimal_learning_rate(
    network: &mut NeuralNetwork,
    dataset: &impl Dataset,
) -> Result<f32> {
    let mut lr_finder = LearningRateFinder::new()
        .start_lr(1e-8)
        .end_lr(1e-1)
        .num_iterations(100);
        
    let lr_history = lr_finder.find(network, dataset).await?;
    
    // Find lr with steepest decline in loss
    let optimal_lr = lr_history.iter()
        .zip(lr_history.iter().skip(1))
        .enumerate()
        .min_by(|(_, (loss1, lr1)), (_, (loss2, lr2))| {
            let gradient1 = (loss2 - loss1) / (lr2.ln() - lr1.ln());
            let gradient2 = if let Some((loss3, lr3)) = lr_history.get(lr_history.len() - 1) {
                (loss3 - loss2) / (lr3.ln() - lr2.ln())
            } else {
                gradient1
            };
            gradient1.partial_cmp(&gradient2).unwrap()
        })
        .map(|(i, (_, lr))| *lr)
        .unwrap_or(1e-3);
        
    println!("Optimal learning rate found: {:.2e}", optimal_lr);
    Ok(optimal_lr)
}

// src/simd/optimized_ops.rs
use std::arch::x86_64::*;

#[inline(always)]
pub fn simd_dot_product(a: &[f32], b: &[f32]) -> f32 {
    assert_eq!(a.len(), b.len());
    assert_eq!(a.len() % 8, 0, "Vector length must be multiple of 8 for AVX");
    
    unsafe {
        let mut sum = _mm256_setzero_ps();
        
        for i in (0..a.len()).step_by(8) {
            let va = _mm256_loadu_ps(a.as_ptr().add(i));
            let vb = _mm256_loadu_ps(b.as_ptr().add(i));
            let prod = _mm256_mul_ps(va, vb);
            sum = _mm256_add_ps(sum, prod);
        }
        
        // Horizontal sum
        let sum_high = _mm256_extractf128_ps(sum, 1);
        let sum_low = _mm256_castps256_ps128(sum);
        let sum128 = _mm_add_ps(sum_high, sum_low);
        
        let sum64 = _mm_add_ps(sum128, _mm_movehl_ps(sum128, sum128));
        let sum32 = _mm_add_ss(sum64, _mm_shuffle_ps(sum64, sum64, 0x55));
        
        _mm_cvtss_f32(sum32)
    }
}

#[inline(always)]
pub fn simd_vector_add(a: &[f32], b: &[f32], result: &mut [f32]) {
    assert_eq!(a.len(), b.len());
    assert_eq!(a.len(), result.len());
    assert_eq!(a.len() % 8, 0);
    
    unsafe {
        for i in (0..a.len()).step_by(8) {
            let va = _mm256_loadu_ps(a.as_ptr().add(i));
            let vb = _mm256_loadu_ps(b.as_ptr().add(i));
            let sum = _mm256_add_ps(va, vb);
            _mm256_storeu_ps(result.as_mut_ptr().add(i), sum);
        }
    }
}

#[inline(always)]
pub fn simd_activation_relu(input: &[f32], output: &mut [f32]) {
    assert_eq!(input.len(), output.len());
    assert_eq!(input.len() % 8, 0);
    
    unsafe {
        let zero = _mm256_setzero_ps();
        
        for i in (0..input.len()).step_by(8) {
            let x = _mm256_loadu_ps(input.as_ptr().add(i));
            let result = _mm256_max_ps(x, zero);
            _mm256_storeu_ps(output.as_mut_ptr().add(i), result);
        }
    }
}

// Benchmark SIMD vs scalar operations
pub fn benchmark_simd_operations() {
    use std::time::Instant;
    
    let sizes = vec![128, 256, 512, 1024, 2048, 4096];
    
    for size in sizes {
        let a: Vec<f32> = (0..size).map(|i| i as f32 * 0.01).collect();
        let b: Vec<f32> = (0..size).map(|i| (i + size) as f32 * 0.01).collect();
        
        // Scalar dot product
        let start = Instant::now();
        for _ in 0..10000 {
            let _: f32 = a.iter().zip(b.iter()).map(|(x, y)| x * y).sum();
        }
        let scalar_time = start.elapsed();
        
        // SIMD dot product
        let start = Instant::now();
        for _ in 0..10000 {
            let _ = simd_dot_product(&a, &b);
        }
        let simd_time = start.elapsed();
        
        let speedup = scalar_time.as_nanos() as f64 / simd_time.as_nanos() as f64;
        println!("Size {}: Scalar {:?}, SIMD {:?}, Speedup: {:.2}x", 
                 size, scalar_time, simd_time, speedup);
    }
}

use std::arch::x86_64::*;

pub struct CpuFeatures {
    pub has_avx: bool,
    pub has_avx2: bool,
    pub has_fma: bool,
    pub has_avx512f: bool,
}

impl CpuFeatures {
    pub fn detect() -> Self {
        if is_x86_feature_detected!("avx") && 
           is_x86_feature_detected!("avx2") && 
           is_x86_feature_detected!("fma") {
            if is_x86_feature_detected!("avx512f") {
                println!("CPU: AVX512 detected - using highest performance path");
                CpuFeatures { has_avx: true, has_avx2: true, has_fma: true, has_avx512f: true }
            } else {
                println!("CPU: AVX2+FMA detected - using high performance path");
                CpuFeatures { has_avx: true, has_avx2: true, has_fma: true, has_avx512f: false }
            }
        } else if is_x86_feature_detected!("avx") {
            println!("CPU: AVX detected - using medium performance path");
            CpuFeatures { has_avx: true, has_avx2: false, has_fma: false, has_avx512f: false }
        } else {
            println!("CPU: Using scalar operations - consider upgrading CPU for better performance");
            CpuFeatures { has_avx: false, has_avx2: false, has_fma: false, has_avx512f: false }
        }
    }

    pub fn optimal_vector_size(&self) -> usize {
        if self.has_avx512f { 16 } // 512-bit vectors = 16 f32s
        else if self.has_avx2 { 8 } // 256-bit vectors = 8 f32s
        else if self.has_avx { 8 }  // 256-bit vectors = 8 f32s
        else { 4 } // 128-bit SSE = 4 f32s
    }

    pub fn select_implementation<T>(&self, avx512: T, avx2: T, avx: T, scalar: T) -> T {
        if self.has_avx512f { avx512 }
        else if self.has_avx2 { avx2 }
        else if self.has_avx { avx }
        else { scalar }
    }
}

// Usage in network operations
impl NeuralNetwork {
    fn optimized_forward_pass(&self, input: &[f32]) -> Result<Vec<f32>> {
        let cpu_features = CpuFeatures::detect();
        
        cpu_features.select_implementation(
            || self.forward_pass_avx512(input),  // Fastest
            || self.forward_pass_avx2(input),    // Fast
            || self.forward_pass_avx(input),     // Medium
            || self.forward_pass_scalar(input)   // Fallback
        )
    }
}

// Structure of Arrays (SoA) for better cache performance
#[derive(Debug, Clone)]
pub struct OptimizedNeuronLayer {
    weights: Vec<f32>,      // All weights together
    biases: Vec<f32>,       // All biases together
    outputs: Vec<f32>,      // All outputs together
    gradients: Vec<f32>,    // All gradients together
    layer_size: usize,
    input_size: usize,
}

impl OptimizedNeuronLayer {
    pub fn new(input_size: usize, layer_size: usize) -> Self {
        // Align memory to cache line boundaries (64 bytes)
        let total_weights = input_size * layer_size;
        
        Self {
            weights: vec![0.0; total_weights],
            biases: vec![0.0; layer_size],
            outputs: vec![0.0; layer_size],
            gradients: vec![0.0; total_weights],
            layer_size,
            input_size,
        }
    }

    // Cache-optimized matrix multiplication
    pub fn forward_pass_optimized(&mut self, input: &[f32]) -> &[f32] {
        // Block matrix multiplication for better cache locality
        const BLOCK_SIZE: usize = 64; // Tuned for L1 cache
        
        // Zero outputs
        self.outputs.fill(0.0);
        
        // Blocked matrix multiplication
        for i_block in (0..self.layer_size).step_by(BLOCK_SIZE) {
            let i_end = (i_block + BLOCK_SIZE).min(self.layer_size);
            
            for k_block in (0..self.input_size).step_by(BLOCK_SIZE) {
                let k_end = (k_block + BLOCK_SIZE).min(self.input_size);
                
                for i in i_block..i_end {
                    for k in k_block..k_end {
                        self.outputs[i] += self.weights[i * self.input_size + k] * input[k];
                    }
                }
            }
        }
        
        // Add biases
        for i in 0..self.layer_size {
            self.outputs[i] += self.biases[i];
        }
        
        &self.outputs
    }
}

// Memory pool for reducing allocations
pub struct MemoryPool {
    f32_buffers: Vec<Vec<f32>>,
    buffer_sizes: Vec<usize>,
    in_use: Vec<bool>,
}

impl MemoryPool {
    pub fn new() -> Self {
        Self {
            f32_buffers: Vec::new(),
            buffer_sizes: Vec::new(),
            in_use: Vec::new(),
        }
    }

    pub fn get_buffer(&mut self, size: usize) -> PooledBuffer {
        // Find existing buffer of suitable size
        for (i, (&buf_size, &in_use)) in self.buffer_sizes.iter().zip(&self.in_use).enumerate() {
            if !in_use && buf_size >= size {
                self.in_use[i] = true;
                return PooledBuffer { 
                    pool: self as *mut MemoryPool,
                    index: i,
                    buffer: &mut self.f32_buffers[i][..size],
                };
            }
        }
        
        // Create new buffer
        let index = self.f32_buffers.len();
        self.f32_buffers.push(vec![0.0; size]);
        self.buffer_sizes.push(size);
        self.in_use.push(true);
        
        PooledBuffer {
            pool: self as *mut MemoryPool,
            index,
            buffer: unsafe { std::slice::from_raw_parts_mut(self.f32_buffers[index].as_mut_ptr(), size) },
        }
    }
}

pub struct PooledBuffer<'a> {
    pool: *mut MemoryPool,
    index: usize,
    buffer: &'a mut [f32],
}

impl<'a> Drop for PooledBuffer<'a> {
    fn drop(&mut self) {
        unsafe {
            (*self.pool).in_use[self.index] = false;
        }
    }
}

use micro_swarm::{TopologyType, SwarmMetrics, CoordinationLatency};

pub struct TopologyOptimizer {
    metrics_history: Vec<SwarmMetrics>,
    current_topology: TopologyType,
    optimization_interval: Duration,
}

impl TopologyOptimizer {
    pub async fn optimize_topology(
        &mut self, 
        orchestrator: &mut SwarmOrchestrator,
        workload: &WorkloadCharacteristics,
    ) -> Result<TopologyType> {
        let candidate_topologies = vec![
            TopologyType::Mesh,
            TopologyType::Hierarchical,
            TopologyType::Ring,
            TopologyType::Star,
        ];
        
        let mut best_topology = self.current_topology;
        let mut best_performance = 0.0;
        
        for topology in candidate_topologies {
            println!("Testing topology: {:?}", topology);
            
            // Temporarily switch topology
            orchestrator.reconfigure_topology(topology).await?;
            
            // Run benchmark workload
            let start = Instant::now();
            let metrics = self.run_benchmark_workload(orchestrator, workload).await?;
            let duration = start.elapsed();
            
            // Calculate performance score
            let performance_score = self.calculate_performance_score(&metrics, duration);
            
            println!("  Performance score: {:.2}", performance_score);
            
            if performance_score > best_performance {
                best_performance = performance_score;
                best_topology = topology;
            }
        }
        
        // Apply best topology
        if best_topology != self.current_topology {
            println!("Switching to optimal topology: {:?}", best_topology);
            orchestrator.reconfigure_topology(best_topology).await?;
            self.current_topology = best_topology;
        }
        
        Ok(best_topology)
    }

    fn calculate_performance_score(&self, metrics: &SwarmMetrics, duration: Duration) -> f64 {
        let coordination_efficiency = 1.0 / (metrics.average_coordination_latency.as_secs_f64() + 0.001);
        let throughput = metrics.tasks_completed as f64 / duration.as_secs_f64();
        let resource_efficiency = 1.0 - metrics.resource_utilization_variance;
        let fault_tolerance = 1.0 - (metrics.failed_coordinations as f64 / metrics.total_coordinations as f64);
        
        // Weighted score
        coordination_efficiency * 0.3 + 
        throughput * 0.4 + 
        resource_efficiency * 0.2 + 
        fault_tolerance * 0.1
    }

    async fn run_benchmark_workload(
        &self,
        orchestrator: &mut SwarmOrchestrator,
        workload: &WorkloadCharacteristics,
    ) -> Result<SwarmMetrics> {
        let benchmark_tasks = self.generate_benchmark_tasks(workload);
        
        let start_time = Instant::now();
        
        for task in benchmark_tasks {
            orchestrator.submit_task(task).await?;
        }
        
        // Wait for completion
        while orchestrator.pending_tasks() > 0 {
            tokio::time::sleep(Duration::from_millis(10)).await;
        }
        
        orchestrator.collect_metrics(start_time)
    }
}

pub struct LoadBalancer {
    agent_loads: HashMap<AgentId, AgentLoad>,
    load_threshold: f64,
    rebalance_interval: Duration,
}

#[derive(Debug, Clone)]
pub struct AgentLoad {
    cpu_usage: f64,
    memory_usage: f64,
    task_queue_size: usize,
    average_task_duration: Duration,
    error_rate: f64,
}

impl LoadBalancer {
    pub async fn rebalance_agents(
        &mut self,
        orchestrator: &mut SwarmOrchestrator,
    ) -> Result<RebalanceResult> {
        let current_loads = self.collect_agent_loads(orchestrator).await?;
        
        // Identify overloaded and underloaded agents
        let mut overloaded = Vec::new();
        let mut underloaded = Vec::new();
        
        for (agent_id, load) in &current_loads {
            let load_score = self.calculate_load_score(load);
            
            if load_score > self.load_threshold {
                overloaded.push((*agent_id, load_score));
            } else if load_score < self.load_threshold * 0.5 {
                underloaded.push((*agent_id, load_score));
            }
        }
        
        // Sort by load intensity
        overloaded.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap());
        underloaded.sort_by(|a, b| a.1.partial_cmp(&b.1).unwrap());
        
        let mut rebalanced_tasks = 0;
        
        // Migrate tasks from overloaded to underloaded agents
        for (overloaded_agent, _) in overloaded.iter().take(3) { // Top 3 overloaded
            for (underloaded_agent, _) in underloaded.iter().take(2) { // Top 2 underloaded
                let tasks_to_migrate = self.calculate_optimal_migration_count(
                    &current_loads[overloaded_agent],
                    &current_loads[underloaded_agent]
                );
                
                if tasks_to_migrate > 0 {
                    orchestrator.migrate_tasks(*overloaded_agent, *underloaded_agent, tasks_to_migrate).await?;
                    rebalanced_tasks += tasks_to_migrate;
                }
            }
        }
        
        // Spawn new agents if all agents are overloaded
        if overloaded.len() > current_loads.len() / 2 {
            let new_agents = self.calculate_required_agents(&current_loads);
            for agent_type in new_agents {
                orchestrator.spawn_agent(agent_type, "load-balancing").await?;
            }
        }
        
        // Terminate underutilized agents if many are idle
        if underloaded.len() > current_loads.len() * 2 / 3 {
            let agents_to_terminate = underloaded.len() / 3;
            for (agent_id, _) in underloaded.iter().take(agents_to_terminate) {
                orchestrator.graceful_shutdown_agent(*agent_id).await?;
            }
        }
        
        Ok(RebalanceResult {
            tasks_migrated: rebalanced_tasks,
            agents_spawned: 0, // Would be calculated based on actual spawning
            agents_terminated: 0, // Would be calculated based on actual termination
        })
    }

    fn calculate_load_score(&self, load: &AgentLoad) -> f64 {
        // Weighted load calculation
        let cpu_weight = 0.4;
        let memory_weight = 0.3;
        let queue_weight = 0.2;
        let error_weight = 0.1;
        
        load.cpu_usage * cpu_weight +
        load.memory_usage * memory_weight +
        (load.task_queue_size as f64 / 100.0).min(1.0) * queue_weight +
        load.error_rate * error_weight
    }
}

use serde::{Serialize, Deserialize};
use lz4_flex::{compress_prepend_size, decompress_size_prepended};

#[derive(Serialize, Deserialize)]
pub struct OptimizedMessage {
    pub agent_id: AgentId,
    pub message_type: MessageType,
    pub compressed_payload: Vec<u8>,
    pub batch_size: usize,
    pub timestamp: u64,
}

pub struct MessageOptimizer {
    compression_threshold: usize,
    batch_size: usize,
    batch_timeout: Duration,
    pending_messages: Vec<Message>,
    last_batch_time: Instant,
}

impl MessageOptimizer {
    pub fn optimize_message(&mut self, message: Message) -> Option<OptimizedMessage> {
        self.pending_messages.push(message);
        
        let should_send_batch = 
            self.pending_messages.len() >= self.batch_size ||
            self.last_batch_time.elapsed() > self.batch_timeout;
            
        if should_send_batch {
            self.create_optimized_batch()
        } else {
            None
        }
    }

    fn create_optimized_batch(&mut self) -> Option<OptimizedMessage> {
        if self.pending_messages.is_empty() {
            return None;
        }
        
        // Serialize batch
        let batch_data = bincode::serialize(&self.pending_messages).ok()?;
        
        // Compress if beneficial
        let compressed_payload = if batch_data.len() > self.compression_threshold {
            compress_prepend_size(&batch_data)
        } else {
            batch_data
        };
        
        let batch_size = self.pending_messages.len();
        let agent_id = self.pending_messages[0].sender_id;
        
        self.pending_messages.clear();
        self.last_batch_time = Instant::now();
        
        Some(OptimizedMessage {
            agent_id,
            message_type: MessageType::Batch,
            compressed_payload,
            batch_size,
            timestamp: SystemTime::now().duration_since(UNIX_EPOCH).unwrap().as_millis() as u64,
        })
    }

    pub fn decompress_batch(&self, optimized_msg: &OptimizedMessage) -> Result<Vec<Message>, Box<dyn std::error::Error>> {
        // Decompress if needed
        let decompressed = if optimized_msg.compressed_payload[0] == 0x04 { // LZ4 magic
            decompress_size_prepended(&optimized_msg.compressed_payload)?
        } else {
            optimized_msg.compressed_payload.clone()
        };
        
        // Deserialize batch
        let messages: Vec<Message> = bincode::deserialize(&decompressed)?;
        Ok(messages)
    }
}

// Benchmark compression effectiveness
pub fn benchmark_message_compression() {
    let sample_messages: Vec<Message> = (0..100).map(|i| Message {
        sender_id: AgentId::new(i % 10),
        recipient_id: AgentId::new((i + 1) % 10),
        message_type: MessageType::TaskResult,
        payload: vec![0.1f32; 1000], // Typical neural network output
        timestamp: SystemTime::now().duration_since(UNIX_EPOCH).unwrap().as_millis() as u64,
    }).collect();
    
    let original_size = bincode::serialize(&sample_messages).unwrap().len();
    let compressed_size = compress_prepend_size(&bincode::serialize(&sample_messages).unwrap()).len();
    
    let compression_ratio = original_size as f64 / compressed_size as f64;
    println!("Message compression: {} -> {} bytes ({:.2}x reduction)", 
             original_size, compressed_size, compression_ratio);
}

use libc::{cpu_set_t, sched_setaffinity, setpriority, PRIO_PROCESS};

pub struct ProcessOptimizer {
    cpu_count: usize,
    process_id: u32,
}

impl ProcessOptimizer {
    pub fn new() -> Self {
        Self {
            cpu_count: num_cpus::get(),
            process_id: std::process::id(),
        }
    }

    pub fn optimize_for_neural_processing(&self) -> Result<(), Box<dyn std::error::Error>> {
        // Set high priority (requires sudo)
        unsafe {
            setpriority(PRIO_PROCESS, self.process_id, -10);
        }
        
        // Pin to performance cores (assuming last half are performance cores)
        let performance_cores = self.cpu_count / 2..self.cpu_count;
        self.set_cpu_affinity(performance_cores)?;
        
        // Set thread priorities for worker threads
        self.optimize_thread_priorities()?;
        
        Ok(())
    }

    fn set_cpu_affinity(&self, cores: std::ops::Range<usize>) -> Result<(), Box<dyn std::error::Error>> {
        unsafe {
            let mut cpu_set: cpu_set_t = std::mem::zeroed();
            
            for core in cores {
                libc::CPU_SET(core, &mut cpu_set);
            }
            
            sched_setaffinity(0, std::mem::size_of::<cpu_set_t>(), &cpu_set);
        }
        
        println!("CPU affinity set to performance cores");
        Ok(())
    }

    fn optimize_thread_priorities(&self) -> Result<(), Box<dyn std::error::Error>> {
        // Set thread priorities based on role
        thread_priority::set_current_thread_priority(thread_priority::ThreadPriority::Max)?;
        
        // For worker threads, use high priority
        tokio::task::spawn(async {
            thread_priority::set_current_thread_priority(thread_priority::ThreadPriority::Crossplatform(
                thread_priority::ThreadPriorityValue::try_from(80u8).unwrap()
            )).ok();
        });
        
        Ok(())
    }
}

use std::alloc::{GlobalAlloc, Layout, System};
use std::sync::atomic::{AtomicUsize, Ordering};

// Performance-tracking allocator
pub struct TrackedAllocator {
    allocations: AtomicUsize,
    deallocations: AtomicUsize,
    bytes_allocated: AtomicUsize,
    peak_memory: AtomicUsize,
}

unsafe impl GlobalAlloc for TrackedAllocator {
    unsafe fn alloc(&self, layout: Layout) -> *mut u8 {
        let ptr = System.alloc(layout);
        
        if !ptr.is_null() {
            self.allocations.fetch_add(1, Ordering::Relaxed);
            self.bytes_allocated.fetch_add(layout.size(), Ordering::Relaxed);
            
            // Track peak memory usage
            let current = self.bytes_allocated.load(Ordering::Relaxed);
            self.peak_memory.fetch_max(current, Ordering::Relaxed);
        }
        
        ptr
    }

    unsafe fn dealloc(&self, ptr: *mut u8, layout: Layout) {
        System.dealloc(ptr, layout);
        
        self.deallocations.fetch_add(1, Ordering::Relaxed);
        self.bytes_allocated.fetch_sub(layout.size(), Ordering::Relaxed);
    }
}

impl TrackedAllocator {
    pub const fn new() -> Self {
        Self {
            allocations: AtomicUsize::new(0),
            deallocations: AtomicUsize::new(0),
            bytes_allocated: AtomicUsize::new(0),
            peak_memory: AtomicUsize::new(0),
        }
    }

    pub fn memory_stats(&self) -> MemoryStats {
        MemoryStats {
            allocations: self.allocations.load(Ordering::Relaxed),
            deallocations: self.deallocations.load(Ordering::Relaxed),
            current_bytes: self.bytes_allocated.load(Ordering::Relaxed),
            peak_bytes: self.peak_memory.load(Ordering::Relaxed),
        }
    }
}

// Use custom allocator
#[global_allocator]
static GLOBAL: TrackedAllocator = TrackedAllocator::new();

// Memory optimization utilities
pub fn optimize_memory_usage() {
    // Force garbage collection of unused allocations
    if let Some(peak) = GLOBAL.memory_stats().peak_bytes.checked_div(1024 * 1024) {
        println!("Peak memory usage: {} MB", peak);
        
        if peak > 1000 { // > 1GB
            // Trigger memory defragmentation
            unsafe {
                libc::malloc_trim(0);
            }
        }
    }
}

use std::sync::Arc;
use tokio::sync::RwLock;

#[derive(Debug, Clone)]
pub struct PerformanceMetrics {
    pub neural_processing_time: Duration,
    pub swarm_coordination_time: Duration,
    pub memory_usage: usize,
    pub cpu_usage: f64,
    pub network_throughput: f64,
    pub error_rate: f64,
    pub tasks_per_second: f64,
}

pub struct PerformanceMonitor {
    metrics: Arc<RwLock<PerformanceMetrics>>,
    measurement_interval: Duration,
    optimization_threshold: f64,
}

impl PerformanceMonitor {
    pub async fn start_monitoring(&self, orchestrator: Arc<SwarmOrchestrator>) {
        let metrics = Arc::clone(&self.metrics);
        let interval = self.measurement_interval;
        
        tokio::spawn(async move {
            let mut interval_timer = tokio::time::interval(interval);
            
            loop {
                interval_timer.tick().await;
                
                let new_metrics = Self::collect_metrics(&orchestrator).await;
                
                // Update metrics
                {
                    let mut metrics_lock = metrics.write().await;
                    *metrics_lock = new_metrics.clone();
                }
                
                // Check for optimization opportunities
                if new_metrics.cpu_usage > 90.0 {
                    println!("High CPU usage detected: {:.1}%", new_metrics.cpu_usage);
                    Self::suggest_cpu_optimization(&new_metrics);
                }
                
                if new_metrics.memory_usage > 1024 * 1024 * 1024 { // 1GB
                    println!("High memory usage detected: {} MB", new_metrics.memory_usage / 1024 / 1024);
                    Self::suggest_memory_optimization(&new_metrics);
                }
                
                if new_metrics.error_rate > 0.05 { // 5%
                    println!("High error rate detected: {:.1}%", new_metrics.error_rate * 100.0);
                    Self::suggest_error_mitigation(&new_metrics);
                }
            }
        });
    }

    async fn collect_metrics(orchestrator: &SwarmOrchestrator) -> PerformanceMetrics {
        // Collect system metrics
        let cpu_usage = Self::get_cpu_usage();
        let memory_usage = GLOBAL.memory_stats().current_bytes;
        
        // Collect swarm metrics
        let swarm_metrics = orchestrator.get_metrics().await;
        
        PerformanceMetrics {
            neural_processing_time: swarm_metrics.average_processing_time,
            swarm_coordination_time: swarm_metrics.average_coordination_latency,
            memory_usage,
            cpu_usage,
            network_throughput: swarm_metrics.network_throughput,
            error_rate: swarm_metrics.error_rate,
            tasks_per_second: swarm_metrics.tasks_per_second,
        }
    }

    fn suggest_cpu_optimization(metrics: &PerformanceMetrics) {
        println!("CPU Optimization Suggestions:");
        println!("  - Consider increasing batch size to reduce per-task overhead");
        println!("  - Enable SIMD optimizations if not already active");
        println!("  - Reduce coordination frequency in swarm topology");
        println!("  - Pin processes to specific CPU cores");
    }

    fn suggest_memory_optimization(metrics: &PerformanceMetrics) {
        println!("Memory Optimization Suggestions:");
        println!("  - Reduce batch size to lower memory footprint");
        println!("  - Enable gradient checkpointing for large models");
        println!("  - Use memory pooling for frequent allocations");
        println!("  - Consider model quantization to reduce memory usage");
    }

    fn suggest_error_mitigation(metrics: &PerformanceMetrics) {
        println!("Error Mitigation Suggestions:");
        println!("  - Implement circuit breaker pattern for failing agents");
        println!("  - Add retry logic with exponential backoff");
        println!("  - Increase timeout values for coordination");
        println!("  - Enable redundant processing for critical tasks");
    }

    fn get_cpu_usage() -> f64 {
        // Platform-specific CPU usage collection
        // This is a simplified version
        let load_avg = std::fs::read_to_string("/proc/loadavg")
            .unwrap_or_default()
            .split_whitespace()
            .next()
            .and_then(|s| s.parse::<f64>().ok())
            .unwrap_or(0.0);
            
        (load_avg * 100.0 / num_cpus::get() as f64).min(100.0)
    }
}

// Automated optimization based on metrics
pub struct AutoOptimizer {
    monitor: PerformanceMonitor,
    optimization_rules: Vec<OptimizationRule>,
}

pub struct OptimizationRule {
    condition: Box<dyn Fn(&PerformanceMetrics) -> bool + Send + Sync>,
    action: Box<dyn Fn(&PerformanceMetrics) -> OptimizationAction + Send + Sync>,
    description: String,
}

pub enum OptimizationAction {
    AdjustBatchSize(usize),
    ReconfigureTopology(TopologyType),
    ScaleAgents(i32), // Positive to add, negative to remove
    AdjustLearningRate(f32),
    EnableSIMD,
    OptimizeMemory,
}

impl AutoOptimizer {
    pub async fn enable_auto_optimization(&self, orchestrator: Arc<SwarmOrchestrator>) {
        let rules = &self.optimization_rules;
        
        tokio::spawn(async move {
            let mut interval = tokio::time::interval(Duration::from_secs(60));
            
            loop {
                interval.tick().await;
                
                let metrics = orchestrator.get_performance_metrics().await;
                
                for rule in rules {
                    if (rule.condition)(&metrics) {
                        let action = (rule.action)(&metrics);
                        println!("Auto-optimization triggered: {}", rule.description);
                        
                        match action {
                            OptimizationAction::AdjustBatchSize(new_size) => {
                                orchestrator.set_global_batch_size(new_size).await;
                            }
                            OptimizationAction::ReconfigureTopology(topology) => {
                                orchestrator.reconfigure_topology(topology).await;
                            }
                            OptimizationAction::ScaleAgents(delta) => {
                                if delta > 0 {
                                    for _ in 0..delta {
                                        orchestrator.spawn_agent(AgentType::Worker, "auto-scaling").await;
                                    }
                                } else {
                                    orchestrator.scale_down_agents((-delta) as usize).await;
                                }
                            }
                            _ => {} // Handle other actions
                        }
                    }
                }
            }
        });
    }
}