scirs2-linalg 0.4.0

//! Advanced GPU tensor core scheduling and task execution
//!
//! This module implements sophisticated scheduling algorithms including:
//! - Tensor core-aware operation scheduling
//! - Performance monitoring and optimization
//! - Bandwidth prediction and resource management
//! - Multi-objective scheduling strategies

use super::kernels::{ElementType, GpuOperationType, TensorShape};
use super::memory::{MemoryAccessPattern, TensorCorePrecision};
use crate::error::{LinalgError, LinalgResult};
use scirs2_core::ndarray::Array2;
use std::collections::VecDeque;
use std::time::Instant;

/// Advanced GPU tensor core scheduler
#[derive(Debug)]
pub struct AdvancedGpuTensorCoreScheduler<T>
where
    T: Clone,
{
    /// Tensor core units
    tensor_core_units: Vec<TensorCoreUnit>,
    /// Scheduling algorithm
    scheduling_algorithm: TensorCoreSchedulingAlgorithm,
    /// Operation queue
    operation_queue: VecDeque<TensorCoreOperation<T>>,
    /// Performance monitor
    performance_monitor: TensorCorePerformanceMonitor,
}

/// Tensor core unit information
#[derive(Debug, Clone)]
pub struct TensorCoreUnit {
    /// Unit ID
    pub id: usize,
    /// Supported data types
    pub supported_types: Vec<ElementType>,
    /// Peak throughput (TOPS)
    pub peak_throughput: f64,
    /// Current utilization
    pub utilization: f64,
    /// Temperature
    pub temperature: f64,
}

/// Tensor core scheduling algorithms
#[derive(Debug, Clone)]
pub enum TensorCoreSchedulingAlgorithm {
    /// Round-robin scheduling
    RoundRobin,
    /// Priority-based scheduling
    PriorityBased,
    /// Throughput-optimal scheduling
    ThroughputOptimal,
    /// Energy-efficient scheduling
    EnergyEfficient,
    /// Latency-optimal scheduling
    LatencyOptimal,
    /// Load-balanced scheduling
    LoadBalanced,
    /// Latency-minimizing scheduling
    LatencyMinimizing,
    /// Machine learning driven scheduling
    MLDriven,
}

/// Tensor core operation
#[derive(Debug, Clone)]
pub struct TensorCoreOperation<T>
where
    T: Clone,
{
    /// Operation ID
    pub id: usize,
    /// Operation type
    pub operation_type: TensorCoreOpType,
    /// Input tensor shapes
    pub input_shapes: Vec<TensorShape>,
    /// Input tensors
    pub inputs: Vec<Array2<T>>,
    /// Output tensor
    pub output: Array2<T>,
    /// Precision requirement
    pub precision: TensorCorePrecision,
    /// Priority
    pub priority: u32,
    /// Deadline
    pub deadline: Option<Instant>,
}

/// Tensor core operation types
#[derive(Debug, Clone)]
pub enum TensorCoreOpType {
    /// Matrix multiplication
    MatrixMultiplication,
    /// Convolutional layer
    ConvolutionalLayer,
    /// Attention mechanism
    AttentionMechanism,
    /// Batch normalization
    BatchNormalization,
    /// Layer normalization
    LayerNormalization,
    /// Custom operation
    Custom(String),
}

/// Performance monitor for tensor cores
#[derive(Debug)]
pub struct TensorCorePerformanceMonitor {
    /// Throughput measurements
    pub throughput_history: VecDeque<f64>,
    /// Latency measurements
    pub latency_history: VecDeque<f64>,
    /// Energy consumption
    pub energy_history: VecDeque<f64>,
    /// Error rates
    pub error_rates: VecDeque<f64>,
}

/// Operation analysis results for scheduling optimization
#[derive(Debug, Clone)]
pub struct OperationAnalysis {
    /// Computational intensity score
    pub compute_intensity: f64,
    /// Memory bandwidth requirement (0-1 normalized)
    pub memory_bandwidth_requirement: f64,
    /// Precision requirement for the operation
    pub precision_requirement: TensorCorePrecision,
    /// Expected tensor core utilization efficiency
    pub tensor_core_utilization: f64,
    /// Estimated execution time in milliseconds
    pub estimated_execution_time: f64,
    /// Estimated energy consumption
    pub energy_consumption: f64,
    /// Parallelism potential (0-1 score)
    pub parallelism_potential: f64,
}

/// Memory bandwidth predictor
#[derive(Debug)]
pub struct BandwidthPredictor {
    /// Prediction models
    pub models: Vec<BandwidthPredictionModel>,
    /// Historical bandwidth measurements
    pub history: VecDeque<BandwidthMeasurement>,
    /// Prediction accuracy
    pub accuracy: f64,
}

/// Bandwidth prediction models
#[derive(Debug, Clone)]
pub enum BandwidthPredictionModel {
    /// Linear regression model
    LinearRegression,
    /// Neural network model
    NeuralNetwork,
    /// Time series model
    TimeSeries,
    /// Machine learning ensemble
    Ensemble,
}

/// Bandwidth measurement record
#[derive(Debug, Clone)]
pub struct BandwidthMeasurement {
    /// Timestamp of measurement
    pub timestamp: Instant,
    /// Measured bandwidth (GB/s)
    pub bandwidth_gbps: f64,
    /// Memory access pattern
    pub access_pattern: MemoryAccessPattern,
    /// Data size
    pub data_size: usize,
}

impl<T> AdvancedGpuTensorCoreScheduler<T>
where
    T: Clone,
{
    /// Create a new tensor core scheduler
    pub fn new() -> LinalgResult<Self> {
        Ok(Self {
            tensor_core_units: Vec::new(),
            scheduling_algorithm: TensorCoreSchedulingAlgorithm::ThroughputOptimal,
            operation_queue: VecDeque::new(),
            performance_monitor: TensorCorePerformanceMonitor::new(),
        })
    }

    /// Add tensor core unit
    pub fn add_tensor_core_unit(&mut self, unit: TensorCoreUnit) {
        self.tensor_core_units.push(unit);
    }

    /// Schedule operations using the current algorithm
    pub fn schedule_operations(
        &mut self,
        operations: &[TensorCoreOperation<T>],
    ) -> LinalgResult<Vec<usize>> {
        // Analyze each operation
        let mut analyses: Vec<(usize, OperationAnalysis)> = operations
            .iter()
            .enumerate()
            .map(|(idx, op)| (idx, self.analyze_operation_requirements(op)))
            .collect();

        // Apply scheduling algorithm
        let schedule = match self.scheduling_algorithm {
            TensorCoreSchedulingAlgorithm::ThroughputOptimal => {
                self.schedule_for_throughput(&mut analyses)?
            }
            TensorCoreSchedulingAlgorithm::LatencyOptimal => {
                self.schedule_for_latency(&mut analyses)?
            }
            TensorCoreSchedulingAlgorithm::EnergyEfficient => {
                self.schedule_for_energy_efficiency(&mut analyses)?
            }
            TensorCoreSchedulingAlgorithm::LoadBalanced => {
                self.schedule_for_load_balance(&mut analyses)?
            }
            _ => {
                // Default to simple ordering
                (0..operations.len()).collect()
            }
        };

        // Update performance metrics
        self.update_scheduling_metrics(&schedule, operations)?;

        // Add operations to queue
        for &op_idx in &schedule {
            if let Some(op) = operations.get(op_idx) {
                self.operation_queue.push_back((*op).clone());
            }
        }

        Ok(schedule)
    }

    /// Analyze individual operation requirements
    fn analyze_operation_requirements(
        &self,
        operation: &TensorCoreOperation<T>,
    ) -> OperationAnalysis {
        OperationAnalysis {
            compute_intensity: self.calculate_compute_intensity(operation),
            memory_bandwidth_requirement: self.calculate_memory_requirement(operation),
            precision_requirement: operation.precision.clone(),
            tensor_core_utilization: self.estimate_tensor_core_utilization(operation),
            estimated_execution_time: self.estimate_execution_time(operation),
            energy_consumption: self.estimate_energy_consumption(operation),
            parallelism_potential: self.analyze_parallelism(operation),
        }
    }

    /// Schedule operations for maximum throughput
    fn schedule_for_throughput(
        &self,
        analyses: &mut [(usize, OperationAnalysis)],
    ) -> LinalgResult<Vec<usize>> {
        // Sort by compute intensity (high first) and tensor core utilization
        analyses.sort_by(|a, b| {
            let score_a = a.1.compute_intensity * a.1.tensor_core_utilization;
            let score_b = b.1.compute_intensity * b.1.tensor_core_utilization;
            score_b
                .partial_cmp(&score_a)
                .unwrap_or(std::cmp::Ordering::Equal)
        });

        // Group operations with similar characteristics for batching
        let mut schedule = Vec::new();
        let mut current_batch = Vec::new();
        let mut last_compute_intensity = -1.0;

        for (idx, analysis) in analyses {
            // Start new batch if compute intensity differs significantly
            if (analysis.compute_intensity - last_compute_intensity).abs() > 0.3
                && !current_batch.is_empty()
            {
                schedule.extend(current_batch.drain(..));
            }

            current_batch.push(*idx);
            last_compute_intensity = analysis.compute_intensity;

            // Limit batch size for optimal tensor core utilization
            if current_batch.len() >= 8 {
                schedule.extend(current_batch.drain(..));
            }
        }

        // Add remaining operations
        schedule.extend(current_batch);
        Ok(schedule)
    }

    /// Schedule operations for minimum latency
    fn schedule_for_latency(
        &self,
        analyses: &mut [(usize, OperationAnalysis)],
    ) -> LinalgResult<Vec<usize>> {
        // Sort by estimated execution time (shortest first)
        analyses.sort_by(|a, b| {
            a.1.estimated_execution_time
                .partial_cmp(&b.1.estimated_execution_time)
                .unwrap_or(std::cmp::Ordering::Equal)
        });

        // Prioritize operations that can overlap with memory transfers
        let mut priority_ops = Vec::new();
        let mut regular_ops = Vec::new();

        for (idx, analysis) in analyses {
            if analysis.memory_bandwidth_requirement < 0.5 && analysis.parallelism_potential > 0.7 {
                priority_ops.push(*idx);
            } else {
                regular_ops.push(*idx);
            }
        }

        // Interleave high-priority and regular operations for optimal pipeline utilization
        let mut schedule = Vec::new();
        let mut priority_iter = priority_ops.into_iter();
        let mut regular_iter = regular_ops.into_iter();

        loop {
            match (priority_iter.next(), regular_iter.next()) {
                (Some(p), Some(r)) => {
                    schedule.push(p);
                    schedule.push(r);
                }
                (Some(p), None) => schedule.push(p),
                (None, Some(r)) => schedule.push(r),
                (None, None) => break,
            }
        }

        Ok(schedule)
    }

    /// Schedule operations for energy efficiency
    fn schedule_for_energy_efficiency(
        &self,
        analyses: &mut [(usize, OperationAnalysis)],
    ) -> LinalgResult<Vec<usize>> {
        // Sort by energy efficiency ratio (compute/energy)
        analyses.sort_by(|a, b| {
            let efficiency_a = a.1.compute_intensity / (a.1.energy_consumption + 1e-6);
            let efficiency_b = b.1.compute_intensity / (b.1.energy_consumption + 1e-6);
            efficiency_b
                .partial_cmp(&efficiency_a)
                .unwrap_or(std::cmp::Ordering::Equal)
        });

        // Group low-energy operations together to enable power scaling
        let mut schedule = Vec::new();
        let low_energy_threshold = 0.3;

        let (low_energy, high_energy): (Vec<_>, Vec<_>) = analyses
            .iter()
            .partition(|(_, analysis)| analysis.energy_consumption < low_energy_threshold);

        // Schedule low-energy operations first to allow for power down periods
        schedule.extend(low_energy.into_iter().map(|(idx, _)| *idx));
        schedule.extend(high_energy.into_iter().map(|(idx, _)| *idx));

        Ok(schedule)
    }

    /// Schedule operations for load balancing across tensor cores
    fn schedule_for_load_balance(
        &self,
        analyses: &mut [(usize, OperationAnalysis)],
    ) -> LinalgResult<Vec<usize>> {
        let num_tensor_cores = self.tensor_core_units.len().max(1);
        let mut core_loads = vec![0.0; num_tensor_cores];
        let mut schedule = vec![Vec::new(); num_tensor_cores];

        // Sort by execution time (longest first) for better load balancing
        analyses.sort_by(|a, b| {
            b.1.estimated_execution_time
                .partial_cmp(&a.1.estimated_execution_time)
                .unwrap_or(std::cmp::Ordering::Equal)
        });

        // Assign each operation to the least loaded tensor core
        for (idx, analysis) in analyses {
            let min_load_core = core_loads
                .iter()
                .enumerate()
                .min_by(|a, b| a.1.partial_cmp(b.1).unwrap_or(std::cmp::Ordering::Equal))
                .map(|(core_idx, _)| core_idx)
                .unwrap_or(0);

            schedule[min_load_core].push(*idx);
            core_loads[min_load_core] += analysis.estimated_execution_time;
        }

        // Flatten schedule maintaining core assignment order
        let mut final_schedule = Vec::new();
        let max_ops_per_core = schedule.iter().map(|s| s.len()).max().unwrap_or(0);

        for i in 0..max_ops_per_core {
            for core_schedule in &schedule {
                if let Some(&op_idx) = core_schedule.get(i) {
                    final_schedule.push(op_idx);
                }
            }
        }

        Ok(final_schedule)
    }

    /// Calculate operation compute intensity
    fn calculate_compute_intensity(&self, operation: &TensorCoreOperation<T>) -> f64 {
        // Estimate based on operation type and matrix dimensions
        match operation.operation_type {
            TensorCoreOpType::MatrixMultiplication => {
                let dims = &operation.input_shapes[0].dimensions;
                if dims.len() >= 2 {
                    (dims[0] * dims[1]) as f64 / 1e6 // Normalize to millions of operations
                } else {
                    1.0
                }
            }
            TensorCoreOpType::ConvolutionalLayer => 2.5, // High compute intensity
            TensorCoreOpType::AttentionMechanism => 3.0, // Very high compute intensity
            TensorCoreOpType::BatchNormalization => 0.5, // Medium compute intensity
            TensorCoreOpType::LayerNormalization => 0.6, // Medium compute intensity
            TensorCoreOpType::Custom(_) => 1.0,
        }
    }

    /// Calculate memory bandwidth requirement
    fn calculate_memory_requirement(&self, operation: &TensorCoreOperation<T>) -> f64 {
        let total_elements: usize = operation
            .input_shapes
            .iter()
            .map(|shape| shape.dimensions.iter().product::<usize>())
            .sum();

        // Normalize to 0-1 range based on typical tensor sizes
        (total_elements as f64 / 1e8).min(1.0)
    }

    /// Estimate tensor core utilization efficiency
    fn estimate_tensor_core_utilization(&self, operation: &TensorCoreOperation<T>) -> f64 {
        match operation.operation_type {
            TensorCoreOpType::MatrixMultiplication => {
                // Check if dimensions are multiples of 16 (optimal for tensor cores)
                let dims = &operation.input_shapes[0].dimensions;
                if dims.len() >= 2 && dims[0] % 16 == 0 && dims[1] % 16 == 0 {
                    0.95
                } else {
                    0.7
                }
            }
            TensorCoreOpType::ConvolutionalLayer => 0.8,
            TensorCoreOpType::AttentionMechanism => 0.85,
            _ => 0.3, // Non-tensor-core operations
        }
    }

    /// Estimate execution time for operation
    fn estimate_execution_time(&self, operation: &TensorCoreOperation<T>) -> f64 {
        let complexity = self.calculate_compute_intensity(operation);
        let memory_factor = self.calculate_memory_requirement(operation);

        // Simple model: time = compute_time + memory_time
        let compute_time = complexity * 0.1; // 0.1ms per million ops
        let memory_time = memory_factor * 0.05; // 0.05ms per normalized memory unit

        compute_time + memory_time
    }

    /// Estimate energy consumption
    fn estimate_energy_consumption(&self, operation: &TensorCoreOperation<T>) -> f64 {
        let intensity = self.calculate_compute_intensity(operation);
        let utilization = self.estimate_tensor_core_utilization(operation);

        // Higher utilization is more energy efficient
        intensity * (2.0 - utilization)
    }

    /// Analyze parallelism potential
    fn analyze_parallelism(&self, operation: &TensorCoreOperation<T>) -> f64 {
        match operation.operation_type {
            TensorCoreOpType::MatrixMultiplication => 0.9, // Highly parallelizable
            TensorCoreOpType::ConvolutionalLayer => 0.95,  // Perfectly parallelizable
            TensorCoreOpType::AttentionMechanism => 0.8,   // Good parallelization
            TensorCoreOpType::BatchNormalization => 0.6,   // Limited by reduction
            TensorCoreOpType::LayerNormalization => 0.6,   // Limited by reduction
            TensorCoreOpType::Custom(_) => 0.7,
        }
    }

    /// Update scheduling performance metrics
    fn update_scheduling_metrics(
        &mut self,
        schedule: &[usize],
        operations: &[TensorCoreOperation<T>],
    ) -> LinalgResult<()> {
        let total_time: f64 = schedule
            .iter()
            .filter_map(|&idx| operations.get(idx))
            .map(|op| self.estimate_execution_time(op))
            .sum();

        let avg_utilization: f64 = schedule
            .iter()
            .filter_map(|&idx| operations.get(idx))
            .map(|op| self.estimate_tensor_core_utilization(op))
            .sum::<f64>()
            / schedule.len().max(1) as f64;

        // Update performance history
        self.performance_monitor
            .throughput_history
            .push_back(1.0 / total_time);
        self.performance_monitor
            .latency_history
            .push_back(total_time);

        // Keep history size manageable
        if self.performance_monitor.throughput_history.len() > 1000 {
            self.performance_monitor.throughput_history.pop_front();
            self.performance_monitor.latency_history.pop_front();
        }

        Ok(())
    }

    /// Get scheduling performance statistics
    pub fn get_performance_stats(&self) -> SchedulingStats {
        let avg_throughput = if self.performance_monitor.throughput_history.is_empty() {
            0.0
        } else {
            self.performance_monitor
                .throughput_history
                .iter()
                .sum::<f64>()
                / self.performance_monitor.throughput_history.len() as f64
        };

        let avg_latency = if self.performance_monitor.latency_history.is_empty() {
            0.0
        } else {
            self.performance_monitor.latency_history.iter().sum::<f64>()
                / self.performance_monitor.latency_history.len() as f64
        };

        SchedulingStats {
            average_throughput: avg_throughput,
            average_latency: avg_latency,
            total_operations_scheduled: self.performance_monitor.throughput_history.len(),
            tensor_core_utilization: self.get_average_utilization(),
        }
    }

    fn get_average_utilization(&self) -> f64 {
        if self.tensor_core_units.is_empty() {
            0.0
        } else {
            self.tensor_core_units
                .iter()
                .map(|unit| unit.utilization)
                .sum::<f64>()
                / self.tensor_core_units.len() as f64
        }
    }
}

impl TensorCorePerformanceMonitor {
    fn new() -> Self {
        Self {
            throughput_history: VecDeque::new(),
            latency_history: VecDeque::new(),
            energy_history: VecDeque::new(),
            error_rates: VecDeque::new(),
        }
    }
}

impl BandwidthPredictor {
    /// Create a new bandwidth predictor
    pub fn new() -> Self {
        Self {
            models: vec![BandwidthPredictionModel::LinearRegression],
            history: VecDeque::new(),
            accuracy: 0.85,
        }
    }

    /// Predict bandwidth for given operations and data sizes
    pub fn predict_bandwidth(
        &self,
        operations: &[GpuOperationType],
        data_sizes: &[usize],
    ) -> LinalgResult<f64> {
        // Advanced bandwidth prediction

        // 1. Calculate operation complexity score
        let complexity_score = operations
            .iter()
            .enumerate()
            .map(|(i, op)| {
                let data_size = data_sizes.get(i).unwrap_or(&1);
                match op {
                    GpuOperationType::MatrixMultiplication => (*data_size as f64).powf(1.5) * 0.8,
                    GpuOperationType::ElementwiseAddition => *data_size as f64 * 0.2,
                    GpuOperationType::Convolution => (*data_size as f64).powf(1.3) * 1.2,
                    GpuOperationType::Reduction => (*data_size as f64).log2() * 0.5,
                    GpuOperationType::Transpose => *data_size as f64 * 0.3,
                    GpuOperationType::Normalization => *data_size as f64 * 0.4,
                    _ => *data_size as f64 * 0.1,
                }
            })
            .sum::<f64>();

        // 2. Memory hierarchy analysis
        let total_data = data_sizes.iter().sum::<usize>() as f64;

        // 3. Predict based on model
        let predicted_bandwidth = match self.models.first() {
            Some(BandwidthPredictionModel::LinearRegression) => {
                // Simple linear model
                let base_bandwidth = 400.0; // GB/s
                let complexity_factor = (complexity_score / 1e6).min(2.0);
                let size_factor = (total_data / 1e9).min(1.5);

                base_bandwidth * complexity_factor * size_factor
            }
            _ => 200.0, // Default fallback
        };

        Ok(predicted_bandwidth.max(10.0).min(1000.0)) // Clamp to reasonable range
    }

    /// Add bandwidth measurement
    pub fn add_measurement(&mut self, measurement: BandwidthMeasurement) {
        self.history.push_back(measurement);

        // Keep history size manageable
        if self.history.len() > 1000 {
            self.history.pop_front();
        }
    }
}

/// Scheduling performance statistics
#[derive(Debug, Clone)]
pub struct SchedulingStats {
    /// Average throughput (operations/second)
    pub average_throughput: f64,
    /// Average latency (seconds)
    pub average_latency: f64,
    /// Total operations scheduled
    pub total_operations_scheduled: usize,
    /// Average tensor core utilization
    pub tensor_core_utilization: f64,
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_tensor_core_scheduler_creation() {
        let scheduler = AdvancedGpuTensorCoreScheduler::<f32>::new().expect("Operation failed");
        assert_eq!(scheduler.tensor_core_units.len(), 0);
    }

    #[test]
    fn test_bandwidth_predictor() {
        let predictor = BandwidthPredictor::new();
        let operations = vec![GpuOperationType::MatrixMultiplication];
        let data_sizes = vec![1024];

        let bandwidth = predictor
            .predict_bandwidth(&operations, &data_sizes)
            .expect("Operation failed");
        assert!(bandwidth > 0.0);
    }

    #[test]
    fn test_tensor_core_unit() {
        let unit = TensorCoreUnit {
            id: 0,
            supported_types: vec![ElementType::F32, ElementType::F16],
            peak_throughput: 100.0,
            utilization: 0.5,
            temperature: 65.0,
        };
        assert_eq!(unit.id, 0);
        assert_eq!(unit.supported_types.len(), 2);
    }
}