sklears_compose/

parallel_execution.rs

//! Parallel pipeline execution components
//!
//! This module provides parallel pipeline components, async execution,
//! thread-safe composition, and work-stealing schedulers.

use scirs2_core::ndarray::{s, Array1, Array2, ArrayView1, ArrayView2};
use scirs2_core::random::thread_rng;
use sklears_core::{
    error::Result as SklResult,
    prelude::{Predict, SklearsError, Transform},
    traits::{Estimator, Fit, Untrained},
    types::Float,
};
use std::collections::{HashMap, VecDeque};
use std::future::Future;
use std::pin::Pin;
use std::sync::{Arc, Condvar, Mutex, RwLock};
use std::task::{Context, Poll};
use std::thread::{self, JoinHandle, ThreadId};
use std::time::{Duration, Instant, SystemTime};

use crate::{PipelinePredictor, PipelineStep};

/// Parallel execution configuration
#[derive(Debug, Clone)]
pub struct ParallelConfig {
    /// Number of worker threads
    pub num_workers: usize,
    /// Thread pool type
    pub pool_type: ThreadPoolType,
    /// Work stealing enabled
    pub work_stealing: bool,
    /// Load balancing strategy
    pub load_balancing: LoadBalancingStrategy,
    /// Task scheduling strategy
    pub scheduling: SchedulingStrategy,
    /// Maximum queue size per worker
    pub max_queue_size: usize,
    /// Worker idle timeout
    pub idle_timeout: Duration,
}

impl Default for ParallelConfig {
    fn default() -> Self {
        Self {
            num_workers: num_cpus::get(),
            pool_type: ThreadPoolType::FixedSize,
            work_stealing: true,
            load_balancing: LoadBalancingStrategy::RoundRobin,
            scheduling: SchedulingStrategy::FIFO,
            max_queue_size: 1000,
            idle_timeout: Duration::from_secs(60),
        }
    }
}

/// Thread pool types
#[derive(Debug, Clone)]
pub enum ThreadPoolType {
    /// Fixed number of threads
    FixedSize,
    /// Dynamic thread pool that adapts to load
    Dynamic {
        min_threads: usize,
        max_threads: usize,
    },
    /// Single-threaded execution
    SingleThreaded,
}

/// Load balancing strategies
#[derive(Debug, Clone)]
pub enum LoadBalancingStrategy {
    /// Round-robin task distribution
    RoundRobin,
    /// Least loaded worker
    LeastLoaded,
    /// Random distribution
    Random,
    /// Locality-aware distribution
    LocalityAware,
}

/// Task scheduling strategies
#[derive(Debug, Clone)]
pub enum SchedulingStrategy {
    /// First-In-First-Out
    FIFO,
    /// Last-In-First-Out
    LIFO,
    /// Priority-based scheduling
    Priority,
    /// Work-stealing deque
    WorkStealing,
}

/// Parallel task wrapper
pub struct ParallelTask {
    /// Task identifier
    pub id: String,
    /// Task function
    pub task_fn: Box<dyn FnOnce() -> SklResult<TaskResult> + Send>,
    /// Task priority
    pub priority: u32,
    /// Estimated execution time
    pub estimated_duration: Duration,
    /// Task dependencies
    pub dependencies: Vec<String>,
    /// Task metadata
    pub metadata: HashMap<String, String>,
}

impl std::fmt::Debug for ParallelTask {
    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
        f.debug_struct("ParallelTask")
            .field("id", &self.id)
            .field("task_fn", &"<function>")
            .field("priority", &self.priority)
            .field("estimated_duration", &self.estimated_duration)
            .field("dependencies", &self.dependencies)
            .field("metadata", &self.metadata)
            .finish()
    }
}

/// Task execution result
#[derive(Debug, Clone)]
pub struct TaskResult {
    /// Task identifier
    pub task_id: String,
    /// Result data
    pub data: Vec<u8>,
    /// Execution duration
    pub duration: Duration,
    /// Worker thread ID
    pub worker_id: ThreadId,
    /// Success flag
    pub success: bool,
    /// Error message (if any)
    pub error: Option<String>,
}

/// Worker thread state
#[derive(Debug)]
pub struct WorkerState {
    /// Worker ID
    pub worker_id: usize,
    /// Thread handle
    pub thread_handle: Option<JoinHandle<()>>,
    /// Task queue
    pub task_queue: Arc<Mutex<VecDeque<ParallelTask>>>,
    /// Worker status
    pub status: WorkerStatus,
    /// Statistics
    pub stats: WorkerStatistics,
    /// Work stealing deque
    pub steal_deque: Arc<Mutex<VecDeque<ParallelTask>>>,
}

/// Worker status
#[derive(Debug, Clone, PartialEq)]
pub enum WorkerStatus {
    /// Idle
    Idle,
    /// Working
    Working,
    /// Stealing
    Stealing,
    /// Terminated
    Terminated,
}

/// Worker statistics
#[derive(Debug, Clone)]
pub struct WorkerStatistics {
    /// Tasks completed
    pub tasks_completed: u64,
    /// Tasks failed
    pub tasks_failed: u64,
    /// Total execution time
    pub total_execution_time: Duration,
    /// Average task duration
    pub avg_task_duration: Duration,
    /// Last activity timestamp
    pub last_activity: SystemTime,
    /// Work stolen from others
    pub work_stolen: u64,
    /// Work given to others
    pub work_given: u64,
}

impl Default for WorkerStatistics {
    fn default() -> Self {
        Self {
            tasks_completed: 0,
            tasks_failed: 0,
            total_execution_time: Duration::ZERO,
            avg_task_duration: Duration::ZERO,
            last_activity: SystemTime::now(),
            work_stolen: 0,
            work_given: 0,
        }
    }
}

/// Parallel executor for pipeline tasks
#[derive(Debug)]
pub struct ParallelExecutor {
    /// Configuration
    config: ParallelConfig,
    /// Worker threads
    workers: Vec<WorkerState>,
    /// Task dispatcher
    dispatcher: TaskDispatcher,
    /// Global task queue
    global_queue: Arc<Mutex<VecDeque<ParallelTask>>>,
    /// Completed tasks
    completed_tasks: Arc<Mutex<HashMap<String, TaskResult>>>,
    /// Executor statistics
    statistics: Arc<RwLock<ExecutorStatistics>>,
    /// Running flag
    is_running: Arc<Mutex<bool>>,
    /// Shutdown signal
    shutdown_signal: Arc<Condvar>,
}

/// Task dispatcher for load balancing
#[derive(Debug)]
pub struct TaskDispatcher {
    /// Round-robin index
    round_robin_index: Mutex<usize>,
    /// Load balancing strategy
    strategy: LoadBalancingStrategy,
    /// Worker load tracking
    worker_loads: Arc<RwLock<Vec<usize>>>,
}

/// Executor statistics
#[derive(Debug, Clone)]
pub struct ExecutorStatistics {
    /// Total tasks submitted
    pub tasks_submitted: u64,
    /// Total tasks completed
    pub tasks_completed: u64,
    /// Total tasks failed
    pub tasks_failed: u64,
    /// Average task duration
    pub avg_task_duration: Duration,
    /// Throughput (tasks per second)
    pub throughput: f64,
    /// Worker utilization
    pub worker_utilization: f64,
    /// Queue depth
    pub queue_depth: usize,
    /// Last update timestamp
    pub last_updated: SystemTime,
}

impl Default for ExecutorStatistics {
    fn default() -> Self {
        Self {
            tasks_submitted: 0,
            tasks_completed: 0,
            tasks_failed: 0,
            avg_task_duration: Duration::ZERO,
            throughput: 0.0,
            worker_utilization: 0.0,
            queue_depth: 0,
            last_updated: SystemTime::now(),
        }
    }
}

impl TaskDispatcher {
    /// Create a new task dispatcher
    #[must_use]
    pub fn new(strategy: LoadBalancingStrategy, num_workers: usize) -> Self {
        Self {
            round_robin_index: Mutex::new(0),
            strategy,
            worker_loads: Arc::new(RwLock::new(vec![0; num_workers])),
        }
    }

    /// Dispatch task to appropriate worker
    pub fn dispatch_task(&self, task: ParallelTask, workers: &mut [WorkerState]) -> SklResult<()> {
        let worker_index = match self.strategy {
            LoadBalancingStrategy::RoundRobin => {
                let mut index = self.round_robin_index.lock().unwrap();
                let selected = *index;
                *index = (*index + 1) % workers.len();
                selected
            }
            LoadBalancingStrategy::LeastLoaded => self.find_least_loaded_worker(workers),
            LoadBalancingStrategy::Random => {
                let mut rng = thread_rng();
                rng.gen_range(0..workers.len())
            }
            LoadBalancingStrategy::LocalityAware => {
                // Simplified locality-aware selection
                self.find_best_locality_worker(workers, &task)
            }
        };

        // Add task to selected worker's queue
        let mut queue = workers[worker_index].task_queue.lock().unwrap();
        queue.push_back(task);

        // Update worker load
        let mut loads = self.worker_loads.write().unwrap();
        loads[worker_index] += 1;

        Ok(())
    }

    /// Find least loaded worker
    fn find_least_loaded_worker(&self, workers: &[WorkerState]) -> usize {
        let loads = self.worker_loads.read().unwrap();
        loads
            .iter()
            .enumerate()
            .min_by_key(|(_, &load)| load)
            .map_or(0, |(index, _)| index)
    }

    /// Find best worker based on locality
    fn find_best_locality_worker(&self, workers: &[WorkerState], _task: &ParallelTask) -> usize {
        // Simplified implementation - prefer first available worker
        workers
            .iter()
            .position(|worker| worker.status == WorkerStatus::Idle)
            .unwrap_or(0)
    }

    /// Update worker load
    pub fn update_worker_load(&self, worker_index: usize, delta: i32) {
        let mut loads = self.worker_loads.write().unwrap();
        if delta < 0 {
            loads[worker_index] = loads[worker_index].saturating_sub((-delta) as usize);
        } else {
            loads[worker_index] += delta as usize;
        }
    }
}

impl ParallelExecutor {
    /// Create a new parallel executor
    #[must_use]
    pub fn new(config: ParallelConfig) -> Self {
        let num_workers = config.num_workers;
        let dispatcher = TaskDispatcher::new(config.load_balancing.clone(), num_workers);

        Self {
            config,
            workers: Vec::with_capacity(num_workers),
            dispatcher,
            global_queue: Arc::new(Mutex::new(VecDeque::new())),
            completed_tasks: Arc::new(Mutex::new(HashMap::new())),
            statistics: Arc::new(RwLock::new(ExecutorStatistics::default())),
            is_running: Arc::new(Mutex::new(false)),
            shutdown_signal: Arc::new(Condvar::new()),
        }
    }

    /// Start the parallel executor
    pub fn start(&mut self) -> SklResult<()> {
        {
            let mut running = self.is_running.lock().unwrap();
            if *running {
                return Ok(());
            }
            *running = true;
        }

        // Initialize workers
        for worker_id in 0..self.config.num_workers {
            let worker = self.create_worker(worker_id)?;
            self.workers.push(worker);
        }

        // Start worker threads
        for i in 0..self.workers.len() {
            self.start_worker_by_index(i)?;
        }

        Ok(())
    }

    /// Stop the parallel executor
    pub fn stop(&mut self) -> SklResult<()> {
        {
            let mut running = self.is_running.lock().unwrap();
            *running = false;
        }

        // Signal shutdown to all workers
        self.shutdown_signal.notify_all();

        // Wait for all workers to finish
        for worker in &mut self.workers {
            if let Some(handle) = worker.thread_handle.take() {
                handle.join().map_err(|_| SklearsError::InvalidData {
                    reason: "Failed to join worker thread".to_string(),
                })?;
            }
        }

        Ok(())
    }

    /// Create a new worker
    fn create_worker(&self, worker_id: usize) -> SklResult<WorkerState> {
        Ok(WorkerState {
            worker_id,
            thread_handle: None,
            task_queue: Arc::new(Mutex::new(VecDeque::new())),
            status: WorkerStatus::Idle,
            stats: WorkerStatistics::default(),
            steal_deque: Arc::new(Mutex::new(VecDeque::new())),
        })
    }

    /// Start a worker thread by index
    fn start_worker_by_index(&mut self, worker_index: usize) -> SklResult<()> {
        // First collect all the data we need without holding mutable references
        let worker_id = self.workers[worker_index].worker_id;
        let task_queue = Arc::clone(&self.workers[worker_index].task_queue);
        let steal_deque = Arc::clone(&self.workers[worker_index].steal_deque);
        let completed_tasks = Arc::clone(&self.completed_tasks);
        let is_running = Arc::clone(&self.is_running);
        let shutdown_signal = Arc::clone(&self.shutdown_signal);
        let statistics = Arc::clone(&self.statistics);
        let config = self.config.clone();

        // Create worker threads for other workers (for work stealing)
        let other_workers: Vec<Arc<Mutex<VecDeque<ParallelTask>>>> = self
            .workers
            .iter()
            .enumerate()
            .filter(|(i, _)| *i != worker_id)
            .map(|(_, w)| Arc::clone(&w.task_queue))
            .collect();

        let handle = thread::spawn(move || {
            Self::worker_loop(
                worker_id,
                task_queue,
                steal_deque,
                other_workers,
                completed_tasks,
                is_running,
                shutdown_signal,
                statistics,
                config,
            );
        });

        // Now get the mutable reference to the worker to set the handle
        let worker = &mut self.workers[worker_index];
        worker.thread_handle = Some(handle);
        Ok(())
    }

    /// Worker thread main loop
    fn worker_loop(
        worker_id: usize,
        task_queue: Arc<Mutex<VecDeque<ParallelTask>>>,
        steal_deque: Arc<Mutex<VecDeque<ParallelTask>>>,
        other_workers: Vec<Arc<Mutex<VecDeque<ParallelTask>>>>,
        completed_tasks: Arc<Mutex<HashMap<String, TaskResult>>>,
        is_running: Arc<Mutex<bool>>,
        shutdown_signal: Arc<Condvar>,
        statistics: Arc<RwLock<ExecutorStatistics>>,
        config: ParallelConfig,
    ) {
        let mut local_stats = WorkerStatistics::default();

        while *is_running.lock().unwrap() {
            // Try to get task from local queue
            let task = {
                let mut queue = task_queue.lock().unwrap();
                queue.pop_front()
            };

            let task = if let Some(task) = task {
                Some(task)
            } else if config.work_stealing {
                // Try to steal work from other workers
                Self::steal_work(&other_workers, worker_id, &mut local_stats)
            } else {
                // Wait for work
                let queue = task_queue.lock().unwrap();
                let _guard = shutdown_signal
                    .wait_timeout(queue, config.idle_timeout)
                    .unwrap();
                continue;
            };

            if let Some(task) = task {
                let start_time = Instant::now();
                let task_id = task.id.clone();

                // Execute task
                let result = match (task.task_fn)() {
                    Ok(mut result) => {
                        result.task_id = task_id.clone();
                        result.worker_id = thread::current().id();
                        result.duration = start_time.elapsed();
                        result.success = true;
                        local_stats.tasks_completed += 1;
                        result
                    }
                    Err(e) => {
                        local_stats.tasks_failed += 1;
                        /// TaskResult
                        TaskResult {
                            task_id: task_id.clone(),
                            data: Vec::new(),
                            duration: start_time.elapsed(),
                            worker_id: thread::current().id(),
                            success: false,
                            error: Some(format!("{e:?}")),
                        }
                    }
                };

                // Update statistics
                let execution_time = start_time.elapsed();
                local_stats.total_execution_time += execution_time;
                local_stats.avg_task_duration = local_stats.total_execution_time
                    / (local_stats.tasks_completed + local_stats.tasks_failed) as u32;
                local_stats.last_activity = SystemTime::now();

                // Store completed task
                {
                    let mut completed = completed_tasks.lock().unwrap();
                    completed.insert(task_id, result);
                }

                // Update global statistics
                {
                    let mut stats = statistics.write().unwrap();
                    stats.tasks_completed += 1;
                    stats.last_updated = SystemTime::now();
                }
            }
        }
    }

    /// Steal work from other workers
    fn steal_work(
        other_workers: &[Arc<Mutex<VecDeque<ParallelTask>>>],
        _worker_id: usize,
        stats: &mut WorkerStatistics,
    ) -> Option<ParallelTask> {
        for other_queue in other_workers {
            if let Ok(mut queue) = other_queue.try_lock() {
                if let Some(task) = queue.pop_back() {
                    stats.work_stolen += 1;
                    return Some(task);
                }
            }
        }
        None
    }

    /// Submit a task for parallel execution
    pub fn submit_task(&mut self, task: ParallelTask) -> SklResult<()> {
        {
            let mut stats = self.statistics.write().unwrap();
            stats.tasks_submitted += 1;
        }

        self.dispatcher.dispatch_task(task, &mut self.workers)?;
        Ok(())
    }

    /// Get task result
    pub fn get_task_result(&self, task_id: &str) -> Option<TaskResult> {
        let completed = self.completed_tasks.lock().unwrap();
        completed.get(task_id).cloned()
    }

    /// Get executor statistics
    pub fn statistics(&self) -> ExecutorStatistics {
        let stats = self.statistics.read().unwrap();
        stats.clone()
    }

    /// Wait for all tasks to complete
    pub fn wait_for_completion(&self, timeout: Option<Duration>) -> SklResult<()> {
        let start_time = Instant::now();

        loop {
            let stats = self.statistics();
            if stats.tasks_submitted == stats.tasks_completed + stats.tasks_failed {
                break;
            }

            if let Some(timeout) = timeout {
                if start_time.elapsed() > timeout {
                    return Err(SklearsError::InvalidData {
                        reason: "Timeout waiting for task completion".to_string(),
                    });
                }
            }

            thread::sleep(Duration::from_millis(10));
        }

        Ok(())
    }
}

/// Parallel pipeline for executing multiple pipeline steps concurrently
#[derive(Debug)]
pub struct ParallelPipeline<S = Untrained> {
    state: S,
    steps: Vec<(String, Box<dyn PipelineStep>)>,
    final_estimator: Option<Box<dyn PipelinePredictor>>,
    executor: Option<ParallelExecutor>,
    parallel_config: ParallelConfig,
    execution_strategy: ParallelExecutionStrategy,
}

/// Parallel execution strategies
#[derive(Debug, Clone)]
pub enum ParallelExecutionStrategy {
    /// Execute all steps in parallel (where dependencies allow)
    FullParallel,
    /// Execute steps in parallel batches
    BatchParallel { batch_size: usize },
    /// Pipeline parallelism (different data through different steps)
    PipelineParallel,
    /// Data parallelism (same step on different data chunks)
    DataParallel { chunk_size: usize },
}

/// Trained state for parallel pipeline
#[derive(Debug)]
pub struct ParallelPipelineTrained {
    fitted_steps: Vec<(String, Box<dyn PipelineStep>)>,
    fitted_estimator: Option<Box<dyn PipelinePredictor>>,
    parallel_config: ParallelConfig,
    execution_strategy: ParallelExecutionStrategy,
    n_features_in: usize,
    feature_names_in: Option<Vec<String>>,
}

impl ParallelPipeline<Untrained> {
    /// Create a new parallel pipeline
    #[must_use]
    pub fn new(parallel_config: ParallelConfig) -> Self {
        Self {
            state: Untrained,
            steps: Vec::new(),
            final_estimator: None,
            executor: None,
            parallel_config,
            execution_strategy: ParallelExecutionStrategy::FullParallel,
        }
    }

    /// Add a pipeline step
    pub fn add_step(&mut self, name: String, step: Box<dyn PipelineStep>) {
        self.steps.push((name, step));
    }

    /// Set the final estimator
    pub fn set_estimator(&mut self, estimator: Box<dyn PipelinePredictor>) {
        self.final_estimator = Some(estimator);
    }

    /// Set execution strategy
    pub fn execution_strategy(mut self, strategy: ParallelExecutionStrategy) -> Self {
        self.execution_strategy = strategy;
        self
    }
}

impl Estimator for ParallelPipeline<Untrained> {
    type Config = ();
    type Error = SklearsError;
    type Float = Float;

    fn config(&self) -> &Self::Config {
        &()
    }
}

impl Fit<ArrayView2<'_, Float>, Option<&ArrayView1<'_, Float>>> for ParallelPipeline<Untrained> {
    type Fitted = ParallelPipeline<ParallelPipelineTrained>;

    fn fit(
        mut self,
        x: &ArrayView2<'_, Float>,
        y: &Option<&ArrayView1<'_, Float>>,
    ) -> SklResult<Self::Fitted> {
        // Initialize parallel executor
        let mut executor = ParallelExecutor::new(self.parallel_config.clone());
        executor.start()?;

        // Execute steps based on strategy
        let fitted_steps = match self.execution_strategy {
            ParallelExecutionStrategy::FullParallel => {
                self.fit_steps_parallel(x, y, &mut executor)?
            }
            ParallelExecutionStrategy::BatchParallel { batch_size } => {
                self.fit_steps_batch_parallel(x, y, &mut executor, batch_size)?
            }
            ParallelExecutionStrategy::PipelineParallel => {
                self.fit_steps_pipeline_parallel(x, y, &mut executor)?
            }
            ParallelExecutionStrategy::DataParallel { chunk_size } => {
                self.fit_steps_data_parallel(x, y, &mut executor, chunk_size)?
            }
        };

        // Fit final estimator if present
        let fitted_estimator = if let Some(mut estimator) = self.final_estimator {
            // Apply all transformations sequentially to get final features
            let mut current_x = x.to_owned();
            for (_, step) in &fitted_steps {
                current_x = step.transform(&current_x.view())?;
            }

            if let Some(y_values) = y.as_ref() {
                let mapped_x = current_x.view().mapv(|v| v as Float);
                estimator.fit(&mapped_x.view(), y_values)?;
                Some(estimator)
            } else {
                None
            }
        } else {
            None
        };

        // Stop executor
        executor.stop()?;

        Ok(ParallelPipeline {
            state: ParallelPipelineTrained {
                fitted_steps,
                fitted_estimator,
                parallel_config: self.parallel_config,
                execution_strategy: self.execution_strategy,
                n_features_in: x.ncols(),
                feature_names_in: None,
            },
            steps: Vec::new(),
            final_estimator: None,
            executor: None,
            parallel_config: ParallelConfig::default(),
            execution_strategy: ParallelExecutionStrategy::FullParallel,
        })
    }
}

impl ParallelPipeline<Untrained> {
    /// Fit steps in full parallel mode
    fn fit_steps_parallel(
        &mut self,
        x: &ArrayView2<'_, Float>,
        y: &Option<&ArrayView1<'_, Float>>,
        executor: &mut ParallelExecutor,
    ) -> SklResult<Vec<(String, Box<dyn PipelineStep>)>> {
        let mut fitted_steps = Vec::new();

        // For simplification, fit steps sequentially
        // In a real implementation, this would analyze dependencies and parallelize appropriately
        let mut current_x = x.to_owned();
        for (name, mut step) in self.steps.drain(..) {
            step.fit(&current_x.view(), y.as_ref().copied())?;
            current_x = step.transform(&current_x.view())?;
            fitted_steps.push((name, step));
        }

        Ok(fitted_steps)
    }

    /// Fit steps in batch parallel mode
    fn fit_steps_batch_parallel(
        &mut self,
        x: &ArrayView2<'_, Float>,
        y: &Option<&ArrayView1<'_, Float>>,
        executor: &mut ParallelExecutor,
        batch_size: usize,
    ) -> SklResult<Vec<(String, Box<dyn PipelineStep>)>> {
        let mut fitted_steps = Vec::new();
        let mut steps = self.steps.drain(..).collect::<Vec<_>>();

        while !steps.is_empty() {
            let batch_size = batch_size.min(steps.len());
            let batch: Vec<_> = steps.drain(0..batch_size).collect();
            let mut batch_fitted = Vec::new();

            for (name, mut step) in batch {
                step.fit(x, y.as_ref().copied())?;
                batch_fitted.push((name, step));
            }

            fitted_steps.extend(batch_fitted);
        }

        Ok(fitted_steps)
    }

    /// Fit steps in pipeline parallel mode
    fn fit_steps_pipeline_parallel(
        &mut self,
        x: &ArrayView2<'_, Float>,
        y: &Option<&ArrayView1<'_, Float>>,
        executor: &mut ParallelExecutor,
    ) -> SklResult<Vec<(String, Box<dyn PipelineStep>)>> {
        // Simplified implementation - same as sequential for now
        self.fit_steps_parallel(x, y, executor)
    }

    /// Fit steps in data parallel mode
    fn fit_steps_data_parallel(
        &mut self,
        x: &ArrayView2<'_, Float>,
        y: &Option<&ArrayView1<'_, Float>>,
        executor: &mut ParallelExecutor,
        chunk_size: usize,
    ) -> SklResult<Vec<(String, Box<dyn PipelineStep>)>> {
        let mut fitted_steps = Vec::new();

        // Process data in chunks for each step
        let mut current_x = x.to_owned();
        for (name, mut step) in self.steps.drain(..) {
            // For simplification, fit on full data
            // In a real implementation, this would chunk the data and fit in parallel
            step.fit(&current_x.view(), y.as_ref().copied())?;
            current_x = step.transform(&current_x.view())?;
            fitted_steps.push((name, step));
        }

        Ok(fitted_steps)
    }
}

impl ParallelPipeline<ParallelPipelineTrained> {
    /// Transform data using parallel execution
    pub fn transform(&self, x: &ArrayView2<'_, Float>) -> SklResult<Array2<f64>> {
        if let ParallelExecutionStrategy::DataParallel { chunk_size } =
            self.state.execution_strategy
        {
            self.transform_data_parallel(x, chunk_size)
        } else {
            // Sequential transformation for other strategies
            let mut current_x = x.to_owned();
            for (_, step) in &self.state.fitted_steps {
                current_x = step.transform(&current_x.view())?;
            }
            Ok(current_x)
        }
    }

    /// Transform data using data parallelism
    fn transform_data_parallel(
        &self,
        x: &ArrayView2<'_, Float>,
        chunk_size: usize,
    ) -> SklResult<Array2<f64>> {
        let n_rows = x.nrows();
        let n_chunks = (n_rows + chunk_size - 1) / chunk_size;
        let mut results = Vec::with_capacity(n_chunks);

        // Process chunks in parallel (simplified sequential implementation)
        for chunk_start in (0..n_rows).step_by(chunk_size) {
            let chunk_end = std::cmp::min(chunk_start + chunk_size, n_rows);
            let chunk = x.slice(s![chunk_start..chunk_end, ..]);

            let mut current_chunk = chunk.to_owned();
            for (_, step) in &self.state.fitted_steps {
                current_chunk = step.transform(&current_chunk.view())?;
            }

            results.push(current_chunk);
        }

        // Concatenate results
        if results.is_empty() {
            return Ok(Array2::zeros((0, 0)));
        }

        let total_rows: usize = results
            .iter()
            .map(scirs2_core::ndarray::ArrayBase::nrows)
            .sum();
        let n_cols = results[0].ncols();
        let mut combined = Array2::zeros((total_rows, n_cols));

        let mut row_offset = 0;
        for result in results {
            let end_offset = row_offset + result.nrows();
            combined
                .slice_mut(s![row_offset..end_offset, ..])
                .assign(&result);
            row_offset = end_offset;
        }

        Ok(combined)
    }

    /// Predict using parallel execution
    pub fn predict(&self, x: &ArrayView2<'_, Float>) -> SklResult<Array1<f64>> {
        let transformed = self.transform(x)?;

        if let Some(estimator) = &self.state.fitted_estimator {
            let mapped_data = transformed.view().mapv(|v| v as Float);
            estimator.predict(&mapped_data.view())
        } else {
            Err(SklearsError::NotFitted {
                operation: "predict".to_string(),
            })
        }
    }
}

/// Async task wrapper for future-based execution
pub struct AsyncTask {
    future: Pin<Box<dyn Future<Output = SklResult<TaskResult>> + Send>>,
}

impl AsyncTask {
    /// Create a new async task
    pub fn new<F>(future: F) -> Self
    where
        F: Future<Output = SklResult<TaskResult>> + Send + 'static,
    {
        Self {
            future: Box::pin(future),
        }
    }
}

impl Future for AsyncTask {
    type Output = SklResult<TaskResult>;

    fn poll(mut self: Pin<&mut Self>, cx: &mut Context<'_>) -> Poll<Self::Output> {
        self.future.as_mut().poll(cx)
    }
}

#[allow(non_snake_case)]
#[cfg(test)]
mod tests {
    use super::*;
    use crate::MockTransformer;

    #[test]
    fn test_parallel_config() {
        let config = ParallelConfig::default();
        assert!(config.num_workers > 0);
        assert!(matches!(config.pool_type, ThreadPoolType::FixedSize));
        assert!(config.work_stealing);
    }

    #[test]
    fn test_task_dispatcher() {
        let dispatcher = TaskDispatcher::new(LoadBalancingStrategy::RoundRobin, 4);

        // Test round-robin selection
        let mut workers = vec![
            /// WorkerState
            WorkerState {
                worker_id: 0,
                thread_handle: None,
                task_queue: Arc::new(Mutex::new(VecDeque::new())),
                status: WorkerStatus::Idle,
                stats: WorkerStatistics::default(),
                steal_deque: Arc::new(Mutex::new(VecDeque::new())),
            },
            /// WorkerState
            WorkerState {
                worker_id: 1,
                thread_handle: None,
                task_queue: Arc::new(Mutex::new(VecDeque::new())),
                status: WorkerStatus::Idle,
                stats: WorkerStatistics::default(),
                steal_deque: Arc::new(Mutex::new(VecDeque::new())),
            },
        ];

        let task = ParallelTask {
            id: "test_task".to_string(),
            task_fn: Box::new(|| {
                Ok(TaskResult {
                    task_id: "test_task".to_string(),
                    data: vec![1, 2, 3],
                    duration: Duration::from_millis(10),
                    worker_id: thread::current().id(),
                    success: true,
                    error: None,
                })
            }),
            priority: 1,
            estimated_duration: Duration::from_millis(100),
            dependencies: Vec::new(),
            metadata: HashMap::new(),
        };

        assert!(dispatcher.dispatch_task(task, &mut workers).is_ok());
    }

    #[test]
    fn test_worker_statistics() {
        let mut stats = WorkerStatistics::default();
        assert_eq!(stats.tasks_completed, 0);
        assert_eq!(stats.tasks_failed, 0);
        assert_eq!(stats.work_stolen, 0);
    }

    #[test]
    fn test_parallel_pipeline_creation() {
        let config = ParallelConfig::default();
        let mut pipeline = ParallelPipeline::new(config);

        pipeline.add_step("step1".to_string(), Box::new(MockTransformer::new()));
        pipeline.set_estimator(Box::new(crate::MockPredictor::new()));

        assert_eq!(pipeline.steps.len(), 1);
        assert!(pipeline.final_estimator.is_some());
    }

    #[test]
    fn test_execution_strategies() {
        let strategies = vec![
            ParallelExecutionStrategy::FullParallel,
            ParallelExecutionStrategy::BatchParallel { batch_size: 2 },
            ParallelExecutionStrategy::PipelineParallel,
            ParallelExecutionStrategy::DataParallel { chunk_size: 100 },
        ];

        for strategy in strategies {
            let config = ParallelConfig::default();
            let pipeline = ParallelPipeline::new(config).execution_strategy(strategy);
            // Test that pipeline can be created with different strategies
            assert!(pipeline.steps.is_empty());
        }
    }

    #[test]
    fn test_task_result() {
        let result = TaskResult {
            task_id: "test".to_string(),
            data: vec![1, 2, 3, 4],
            duration: Duration::from_millis(50),
            worker_id: thread::current().id(),
            success: true,
            error: None,
        };

        assert_eq!(result.task_id, "test");
        assert_eq!(result.data.len(), 4);
        assert!(result.success);
        assert!(result.error.is_none());
    }
}