scirs2-linalg 0.4.0

//! GPU kernel compilation and optimization system

use crate::error::{LinalgError, LinalgResult};
use std::collections::HashMap;

/// Advanced GPU kernel compilation and optimization system
pub struct GpuKernelManager {
    kernel_cache: std::collections::HashMap<String, CompiledKernel>,
    optimization_level: OptimizationLevel,
    device_capabilities: DeviceCapabilities,
    kernel_templates: std::collections::HashMap<String, KernelTemplate>,
}

#[derive(Debug, Clone)]
pub struct CompiledKernel {
    source: String,
    binary: Option<Vec<u8>>,
    metadata: KernelMetadata,
    performance_data: KernelPerformanceData,
}

#[derive(Debug, Clone)]
struct KernelMetadata {
    name: String,
    data_types: Vec<String>,
    work_groupsize: Option<usize>,
    local_memory_usage: usize,
    register_usage: usize,
    optimization_level: OptimizationLevel,
    target_architecture: String,
}

#[derive(Debug, Clone)]
struct KernelPerformanceData {
    compile_time_ms: f64,
    theoretical_peak_gflops: f64,
    memory_bandwidth_efficiency: f64,
    occupancy_percentage: f64,
    optimal_work_groupsizes: Vec<usize>,
}

#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum OptimizationLevel {
    None,
    Basic,
    Aggressive,
    Advanced,
}

#[derive(Debug, Clone)]
pub struct DeviceCapabilities {
    max_work_groupsize: usize,
    max_work_item_dimensions: usize,
    local_memorysize: usize,
    supports_fp64: bool,
    supports_fp16: bool,
    compute_units: u32,
    simd_width: u32,
    has_tensor_cores: bool,
}

#[derive(Debug, Clone)]
struct KernelTemplate {
    template_source: String,
    parameters: Vec<TemplateParameter>,
    specializations: std::collections::HashMap<String, String>,
}

#[derive(Debug, Clone)]
struct TemplateParameter {
    name: String,
    param_type: ParameterType,
    default_value: Option<String>,
    constraints: Vec<ParameterConstraint>,
}

#[derive(Debug, Clone, PartialEq, Eq)]
enum ParameterType {
    Integer,
    Float,
    Boolean,
    String,
    DataType,
}

#[derive(Debug, Clone)]
enum ParameterConstraint {
    Range(i64, i64),
    OneOf(Vec<String>),
    PowerOfTwo,
    MultipleOf(i64),
}

impl GpuKernelManager {
    /// Create a new advanced kernel manager
    pub fn new() -> Self {
        let mut manager = Self {
            kernel_cache: std::collections::HashMap::new(),
            optimization_level: OptimizationLevel::Aggressive,
            device_capabilities: DeviceCapabilities::default(),
            kernel_templates: std::collections::HashMap::new(),
        };

        manager.load_builtin_templates();
        manager
    }

    /// Create manager with device capabilities
    pub fn with_device_capabilities(capabilities: DeviceCapabilities) -> Self {
        let mut manager = Self::new();
        manager.device_capabilities = capabilities;
        manager
    }

    /// Set optimization level
    pub fn set_optimization_level(&mut self, level: OptimizationLevel) {
        self.optimization_level = level;
    }

    /// Load and compile a kernel with advanced optimizations
    pub fn load_optimized_kernel(&mut self, name: &str, source: &str) -> LinalgResult<()> {
        let optimized_source = self.optimize_kernel_source(source)?;

        let metadata = self.analyze_kernel(&optimized_source)?;
        let performance_data = self.estimate_performance(&metadata)?;

        let compiled_kernel = CompiledKernel {
            source: optimized_source,
            binary: None, // Would be populated by actual compilation
            metadata,
            performance_data,
        };

        self.kernel_cache.insert(name.to_string(), compiled_kernel);
        Ok(())
    }

    /// Generate specialized kernel from template
    pub fn generate_specialized_kernel(
        &mut self,
        template_name: &str,
        parameters: &std::collections::HashMap<String, String>,
    ) -> LinalgResult<String> {
        let template = self.kernel_templates.get(template_name).ok_or_else(|| {
            LinalgError::InvalidInput(format!("Template '{}' not found", template_name))
        })?;

        // Validate parameters
        self.validate_template_parameters(template, parameters)?;

        // Generate specialized source
        let specialized_source = self.instantiate_template(template, parameters)?;

        // Auto-optimize based on device capabilities
        let optimized_source = self.optimize_for_device(&specialized_source)?;

        Ok(optimized_source)
    }

    /// Get compiled kernel with performance metadata
    pub fn get_compiled_kernel(&self, name: &str) -> Option<&CompiledKernel> {
        self.kernel_cache.get(name)
    }

    /// Benchmark kernel performance
    pub fn benchmark_kernel(
        &mut self,
        name: &str,
        problemsizes: &[usize],
    ) -> LinalgResult<BenchmarkResults> {
        let kernel = self
            .kernel_cache
            .get(name)
            .ok_or_else(|| LinalgError::InvalidInput(format!("Kernel '{}' not found", name)))?;

        let mut results = BenchmarkResults::new(name);

        for &size in problemsizes {
            let runtime = self.simulate_kernel_execution(kernel, size)?;
            let gflops = self.calculate_gflops(kernel, size, runtime);
            let efficiency = self.calculate_efficiency(kernel, runtime);

            results.add_measurement(size, runtime, gflops, efficiency);
        }

        // Update performance data based on benchmark
        if let Some(kernel) = self.kernel_cache.get_mut(name) {
            kernel.performance_data.theoretical_peak_gflops = results.peak_gflops();
            kernel.performance_data.memory_bandwidth_efficiency = results.avg_efficiency();
        }

        Ok(results)
    }

    /// Auto-tune kernel parameters for optimal performance
    pub fn auto_tune_kernel(
        &mut self,
        name: &str,
        target_problemsize: usize,
    ) -> LinalgResult<AutoTuneResults> {
        let kernel = self
            .kernel_cache
            .get(name)
            .ok_or_else(|| LinalgError::InvalidInput(format!("Kernel '{}' not found", name)))?
            .clone();

        let mut best_config = AutoTuneConfig::default();
        let mut best_performance = 0.0;

        // Search space for work group sizes
        let work_groupsizes = self.generate_work_group_candidates();

        for work_groupsize in &work_groupsizes {
            if *work_groupsize > self.device_capabilities.max_work_groupsize {
                continue;
            }

            let config = AutoTuneConfig {
                work_groupsize: *work_groupsize,
                local_memory_usage: self.estimate_optimal_local_memory(*work_groupsize),
                unroll_factor: self.estimate_optimal_unroll_factor(*work_groupsize),
                vectorization_width: self.estimate_optimal_vectorization(*work_groupsize),
            };

            let performance = self.evaluate_configuration(&kernel, &config, target_problemsize)?;

            if performance > best_performance {
                best_performance = performance;
                best_config = config;
            }
        }

        Ok(AutoTuneResults {
            optimal_config: best_config,
            performance_improvement: best_performance,
            tuning_iterations: work_groupsizes.len(),
        })
    }

    // Private implementation methods

    fn load_builtin_templates(&mut self) {
        // Load matrix multiplication template
        let matmul_template = KernelTemplate {
            template_source: r#"
_kernel void matmul_{{PRECISION}}_{{TILE_SIZE}}(
    _global const {{TYPE}}* A,
    _global const {{TYPE}}* B,
    _global {{TYPE}}* C,
    const int M, const int N, const int K
) {
    _local {{TYPE}} As[{{TILE_SIZE}}][{{TILE_SIZE}}];
    _local {{TYPE}} Bs[{{TILE_SIZE}}][{{TILE_SIZE}}];

    int globalRow = get_global_id(0);
    int globalCol = get_global_id(1);
    int localRow = get_local_id(0);
    int localCol = get_local_id(1);

    {{TYPE}} sum = 0.0;

    for (int t = 0; t < (K + {{TILE_SIZE}} - 1) / {{TILE_SIZE}}; t++) {
        // Load tiles into local memory
        if (globalRow < M && t * {{TILE_SIZE}} + localCol < K) {
            As[localRow][localCol] = A[globalRow * K + t * {{TILE_SIZE}} + localCol];
        } else {
            As[localRow][localCol] = 0.0;
        }

        if (t * {{TILE_SIZE}} + localRow < K && globalCol < N) {
            Bs[localRow][localCol] = B[(t * {{TILE_SIZE}} + localRow) * N + globalCol];
        } else {
            Bs[localRow][localCol] = 0.0;
        }

        barrier(CLK_LOCAL_MEM_FENCE);

        // Compute partial result
        {{UNROLL_PRAGMA}}
        for (int k = 0; k < {{TILE_SIZE}}; k++) {
            sum += As[localRow][k] * Bs[k][localCol];
        }

        barrier(CLK_LOCAL_MEM_FENCE);
    }

    if (globalRow < M && globalCol < N) {
        C[globalRow * N + globalCol] = sum;
    }
}
"#
            .to_string(),
            parameters: vec![
                TemplateParameter {
                    name: "PRECISION".to_string(),
                    param_type: ParameterType::String,
                    default_value: Some("f32".to_string()),
                    constraints: vec![ParameterConstraint::OneOf(vec![
                        "f16".to_string(),
                        "f32".to_string(),
                        "f64".to_string(),
                    ])],
                },
                TemplateParameter {
                    name: "TILE_SIZE".to_string(),
                    param_type: ParameterType::Integer,
                    default_value: Some("16".to_string()),
                    constraints: vec![
                        ParameterConstraint::PowerOfTwo,
                        ParameterConstraint::Range(4, 64),
                    ],
                },
                TemplateParameter {
                    name: "TYPE".to_string(),
                    param_type: ParameterType::DataType,
                    default_value: Some("float".to_string()),
                    constraints: vec![],
                },
            ],
            specializations: std::collections::HashMap::new(),
        };

        self.kernel_templates
            .insert("optimized_matmul".to_string(), matmul_template);

        // Add more sophisticated templates...
        self.load_advanced_templates();
    }

    fn load_advanced_templates(&mut self) {
        // Tensor contraction template with advanced optimizations
        let tensor_contract_template = KernelTemplate {
            template_source: r#"
// Advanced tensor contraction kernel with memory coalescing and compute optimization
_kernel void tensor_contract_{{PRECISION}}_{{BLOCK_SIZE}}(
    _global const {{TYPE}}* tensor_a,
    _global const {{TYPE}}* tensor_b,
    _global {{TYPE}}* result,
    const int* dims_a,
    const int* dims_b,
    const int* contract_dims,
    const int num_contract_dims
) {
    {{VECTORIZATION_PRAGMA}}

    _local {{TYPE}} shared_a[{{BLOCK_SIZE}} * {{BLOCK_SIZE}}];
    _local {{TYPE}} shared_b[{{BLOCK_SIZE}} * {{BLOCK_SIZE}}];

    const int gid_x = get_global_id(0);
    const int gid_y = get_global_id(1);
    const int lid_x = get_local_id(0);
    const int lid_y = get_local_id(1);

    {{TYPE}} accumulator = 0.0;

    // Advanced blocking strategy for memory efficiency
    {{BLOCKING_STRATEGY}}

    // Tensor contraction with optimized memory access patterns
    {{CONTRACTION_LOOP}}

    result[gid_y * get_global_size(0) + gid_x] = accumulator;
}
"#
            .to_string(),
            parameters: vec![
                TemplateParameter {
                    name: "PRECISION".to_string(),
                    param_type: ParameterType::String,
                    default_value: Some("f32".to_string()),
                    constraints: vec![ParameterConstraint::OneOf(vec![
                        "f16".to_string(),
                        "f32".to_string(),
                        "f64".to_string(),
                    ])],
                },
                TemplateParameter {
                    name: "BLOCK_SIZE".to_string(),
                    param_type: ParameterType::Integer,
                    default_value: Some("32".to_string()),
                    constraints: vec![
                        ParameterConstraint::PowerOfTwo,
                        ParameterConstraint::Range(8, 128),
                    ],
                },
                TemplateParameter {
                    name: "VECTORIZATION_WIDTH".to_string(),
                    param_type: ParameterType::Integer,
                    default_value: Some("4".to_string()),
                    constraints: vec![
                        ParameterConstraint::PowerOfTwo,
                        ParameterConstraint::Range(1, 16),
                    ],
                },
            ],
            specializations: std::collections::HashMap::new(),
        };

        self.kernel_templates.insert(
            "advanced_tensor_contract".to_string(),
            tensor_contract_template,
        );
    }

    fn optimize_kernel_source(&self, source: &str) -> LinalgResult<String> {
        let mut optimized = source.to_string();

        match self.optimization_level {
            OptimizationLevel::None => return Ok(optimized),
            OptimizationLevel::Basic => {
                optimized = self.apply_basic_optimizations(optimized)?;
            }
            OptimizationLevel::Aggressive => {
                optimized = self.apply_basic_optimizations(optimized)?;
                optimized = self.apply_aggressive_optimizations(optimized)?;
            }
            OptimizationLevel::Advanced => {
                optimized = self.apply_basic_optimizations(optimized)?;
                optimized = self.apply_aggressive_optimizations(optimized)?;
                optimized = self.apply_advanced_optimizations(optimized)?;
            }
        }

        Ok(optimized)
    }

    fn apply_basic_optimizations(&self, source: String) -> LinalgResult<String> {
        let mut optimized = source;

        // Add vectorization hints
        optimized = optimized.replace("for (int i = 0;", "#pragma unroll 4\n    for (int i = 0;");

        // Add memory access optimizations
        optimized = optimized.replace(
            "_global",
            "_global _attribute_((reqd_work_groupsize(16,16,1)))",
        );

        Ok(optimized)
    }

    fn apply_aggressive_optimizations(&self, source: String) -> LinalgResult<String> {
        let mut optimized = source;

        // Add advanced vectorization
        if self.device_capabilities.simd_width >= 8 {
            optimized = optimized.replace(
                "{{VECTORIZATION_PRAGMA}}",
                "#pragma unroll 8\n#pragma vector aligned",
            );
        }

        // Add memory prefetching
        optimized = optimized.replace(
            "// Memory access",
            "// Prefetch next iteration data\n    prefetch(data + offset, CLK_GLOBAL_MEM_FENCE);",
        );

        Ok(optimized)
    }

    fn apply_advanced_optimizations(&self, source: String) -> LinalgResult<String> {
        let mut optimized = source;

        // Add tensor core utilization if available
        if self.device_capabilities.has_tensor_cores {
            optimized = optimized.replace(
                "{{TYPE}} sum = 0.0;",
                "{{TYPE}} sum = 0.0;\n    // Use tensor cores for mixed precision\n    #pragma use_tensor_cores"
            );
        }

        // Add advanced loop optimizations
        optimized = optimized.replace(
            "{{UNROLL_PRAGMA}}",
            "#pragma unroll 16\n#pragma ivdep\n#pragma vector always",
        );

        Ok(optimized)
    }

    fn analyze_kernel(&self, source: &str) -> LinalgResult<KernelMetadata> {
        // Mock kernel analysis - in practice would parse OpenCL/CUDA source
        Ok(KernelMetadata {
            name: "analyzed_kernel".to_string(),
            data_types: vec!["float".to_string()],
            work_groupsize: Some(256),
            local_memory_usage: 4096,
            register_usage: 32,
            optimization_level: self.optimization_level,
            target_architecture: "generic".to_string(),
        })
    }

    fn estimate_performance(
        &self,
        metadata: &KernelMetadata,
    ) -> LinalgResult<KernelPerformanceData> {
        // Mock performance estimation
        Ok(KernelPerformanceData {
            compile_time_ms: 150.0,
            theoretical_peak_gflops: 1200.0,
            memory_bandwidth_efficiency: 0.85,
            occupancy_percentage: 75.0,
            optimal_work_groupsizes: vec![16, 32, 64, 128, 256],
        })
    }

    // Additional helper methods for auto-tuning and optimization...
    fn validate_template_parameters(
        &self,
        template: &KernelTemplate,
        _parameters: &std::collections::HashMap<String, String>,
    ) -> LinalgResult<()> {
        // Validation logic
        Ok(())
    }

    fn instantiate_template(
        &self,
        template: &KernelTemplate,
        parameters: &std::collections::HashMap<String, String>,
    ) -> LinalgResult<String> {
        let mut source = template.template_source.clone();

        for (key, value) in parameters {
            source = source.replace(&format!("{{{{{}}}}}", key), value);
        }

        Ok(source)
    }

    fn optimize_for_device(&self, source: &str) -> LinalgResult<String> {
        // Device-specific optimizations
        Ok(source.to_string())
    }

    fn simulate_kernel_execution(
        &self,
        kernel: &CompiledKernel,
        problemsize: usize,
    ) -> LinalgResult<f64> {
        // Mock execution simulation
        Ok(0.001 * problemsize as f64 / 1000000.0) // Mock runtime in seconds
    }

    fn calculate_gflops(&self, kernel: &CompiledKernel, problemsize: usize, runtime: f64) -> f64 {
        // Mock GFLOPS calculation
        let operations = problemsize as f64 * problemsize as f64 * 2.0; // Mock operation count
        operations / (0.001 * 1e9) // Mock with fixed runtime
    }

    fn calculate_efficiency(&self, kernel: &CompiledKernel, runtime: f64) -> f64 {
        // Mock efficiency calculation
        kernel.performance_data.memory_bandwidth_efficiency * 0.9
    }

    fn generate_work_group_candidates(&self) -> Vec<usize> {
        vec![8, 16, 32, 64, 128, 256, 512]
            .into_iter()
            .filter(|&size| size <= self.device_capabilities.max_work_groupsize)
            .collect()
    }

    fn estimate_optimal_local_memory(&self, work_groupsize: usize) -> usize {
        std::cmp::min(
            work_groupsize * 64,
            self.device_capabilities.local_memorysize,
        )
    }

    fn estimate_optimal_unroll_factor(&self, work_groupsize: usize) -> usize {
        if work_groupsize >= 256 {
            8
        } else if work_groupsize >= 64 {
            4
        } else {
            2
        }
    }

    fn estimate_optimal_vectorization(&self, work_groupsize: usize) -> usize {
        std::cmp::min(self.device_capabilities.simd_width as usize, 8)
    }

    fn evaluate_configuration(
        &self,
        kernel: &CompiledKernel,
        config: &AutoTuneConfig,
        problemsize: usize,
    ) -> LinalgResult<f64> {
        // Mock performance evaluation
        let base_performance = kernel.performance_data.theoretical_peak_gflops;
        let work_group_efficiency = (config.work_groupsize as f64 / 256.0).min(1.0);
        Ok(base_performance * work_group_efficiency)
    }

    /// Load and compile a kernel from source code
    pub fn load_kernel(&mut self, name: &str, source: &str) -> LinalgResult<()> {
        let optimized_source = self.optimize_kernel_source(source)?;

        let metadata = self.analyze_kernel(&optimized_source)?;
        let performance_data = self.estimate_performance(&metadata)?;

        let compiled_kernel = CompiledKernel {
            source: optimized_source,
            binary: None, // Would be populated by actual compilation
            metadata,
            performance_data,
        };

        self.kernel_cache.insert(name.to_string(), compiled_kernel);
        Ok(())
    }

    /// Get a compiled kernel by name
    pub fn get_kernel(&self, name: &str) -> Option<&CompiledKernel> {
        self.kernel_cache.get(name)
    }

    /// List all loaded kernel names
    pub fn list_kernels(&self) -> Vec<String> {
        self.kernel_cache.keys().cloned().collect()
    }
}

impl Default for DeviceCapabilities {
    fn default() -> Self {
        Self {
            max_work_groupsize: 1024,
            max_work_item_dimensions: 3,
            local_memorysize: 48 * 1024, // 48KB
            supports_fp64: true,
            supports_fp16: false,
            compute_units: 32,
            simd_width: 32,
            has_tensor_cores: false,
        }
    }
}

#[derive(Debug, Clone)]
pub struct BenchmarkResults {
    kernel_name: String,
    measurements: Vec<BenchmarkMeasurement>,
}

#[derive(Debug, Clone)]
struct BenchmarkMeasurement {
    problemsize: usize,
    runtime_seconds: f64,
    gflops: f64,
    efficiency: f64,
}

impl BenchmarkResults {
    fn new(kernel_name: &str) -> Self {
        Self {
            kernel_name: kernel_name.to_string(),
            measurements: Vec::new(),
        }
    }

    fn add_measurement(&mut self, size: usize, runtime: f64, gflops: f64, efficiency: f64) {
        self.measurements.push(BenchmarkMeasurement {
            problemsize: size,
            runtime_seconds: runtime,
            gflops,
            efficiency,
        });
    }

    fn peak_gflops(&self) -> f64 {
        self.measurements
            .iter()
            .map(|m| m.gflops)
            .fold(0.0, f64::max)
    }

    fn avg_efficiency(&self) -> f64 {
        if self.measurements.is_empty() {
            return 0.0;
        }
        let sum: f64 = self.measurements.iter().map(|m| m.efficiency).sum();
        sum / self.measurements.len() as f64
    }
}

#[derive(Debug, Clone)]
pub struct AutoTuneConfig {
    work_groupsize: usize,
    local_memory_usage: usize,
    unroll_factor: usize,
    vectorization_width: usize,
}

impl Default for AutoTuneConfig {
    fn default() -> Self {
        Self {
            work_groupsize: 256,
            local_memory_usage: 16384,
            unroll_factor: 4,
            vectorization_width: 4,
        }
    }
}

#[derive(Debug, Clone)]
pub struct AutoTuneResults {
    pub optimal_config: AutoTuneConfig,
    pub performance_improvement: f64,
    pub tuning_iterations: usize,
}

impl Default for GpuKernelManager {
    fn default() -> Self {
        Self::new()
    }
}