quantrs2_core/gpu/

specialized_kernels.rs

//! Enhanced GPU kernel optimization for specialized quantum gates
//!
//! This module provides high-performance GPU kernels optimized for specialized quantum gates
//! including holonomic gates, post-quantum cryptography gates, and quantum ML gates.
//! It leverages tensor cores, optimized memory access patterns, and gate fusion for maximum performance.

use crate::{error::QuantRS2Result, gate::GateOp, qubit::QubitId};
use scirs2_core::Complex64;
use std::collections::HashMap;
use std::sync::{Arc, Mutex};

/// Enhanced GPU kernel manager for specialized gates
pub struct SpecializedGpuKernels {
    /// CUDA context for kernel execution
    cuda_context: Option<CudaSpecializedContext>,
    /// WebGPU context for cross-platform support
    webgpu_context: Option<WebGpuSpecializedContext>,
    /// Kernel cache for compiled kernels
    kernel_cache: Arc<Mutex<KernelCache>>,
    /// Performance statistics
    performance_stats: Arc<Mutex<PerformanceStats>>,
    /// Optimization configuration
    config: OptimizationConfig,
}

/// CUDA context specialized for quantum gates
pub struct CudaSpecializedContext {
    /// Device compute capability
    #[allow(dead_code)]
    compute_capability: (i32, i32),
    /// Tensor core availability
    has_tensor_cores: bool,
    /// Maximum shared memory per block
    #[allow(dead_code)]
    max_shared_memory: usize,
    /// Warp size
    #[allow(dead_code)]
    warp_size: usize,
    /// Compiled kernels
    kernels: HashMap<String, CompiledKernel>,
}

/// WebGPU context for cross-platform support
pub struct WebGpuSpecializedContext {
    /// Device limits
    #[allow(dead_code)]
    device_limits: WebGpuLimits,
    /// Compiled shaders
    #[allow(dead_code)]
    shaders: HashMap<String, CompiledShader>,
    /// Buffer pools for efficient memory management
    #[allow(dead_code)]
    buffer_pools: HashMap<String, BufferPool>,
}

/// Kernel cache for compiled GPU kernels
pub struct KernelCache {
    /// Cached CUDA kernels
    #[allow(dead_code)]
    cuda_kernels: HashMap<String, CachedCudaKernel>,
    /// Cached WebGPU shaders
    #[allow(dead_code)]
    webgpu_shaders: HashMap<String, CachedWebGpuShader>,
    /// Cache hit statistics
    cache_stats: CacheStatistics,
}

/// Performance statistics for optimization analysis
pub struct PerformanceStats {
    /// Kernel execution times
    kernel_times: HashMap<String, Vec<f64>>,
    /// Memory bandwidth utilization
    memory_bandwidth: HashMap<String, f64>,
    /// Tensor core utilization
    tensor_core_utilization: f64,
    /// Cache hit rates
    #[allow(dead_code)]
    cache_hit_rates: HashMap<String, f64>,
}

/// GPU optimization configuration
#[derive(Debug, Clone)]
pub struct OptimizationConfig {
    /// Enable tensor core optimization
    pub use_tensor_cores: bool,
    /// Enable memory access optimization
    pub optimize_memory_access: bool,
    /// Enable gate fusion
    pub enable_gate_fusion: bool,
    /// Maximum fusion chain length
    pub max_fusion_length: usize,
    /// Memory coalescing threshold
    pub coalescing_threshold: usize,
    /// Use mixed precision
    pub use_mixed_precision: bool,
}

impl Default for OptimizationConfig {
    fn default() -> Self {
        Self {
            use_tensor_cores: true,
            optimize_memory_access: true,
            enable_gate_fusion: true,
            max_fusion_length: 8,
            coalescing_threshold: 32,
            use_mixed_precision: true,
        }
    }
}

impl SpecializedGpuKernels {
    /// Create a new specialized GPU kernel manager
    pub fn new(config: OptimizationConfig) -> QuantRS2Result<Self> {
        let cuda_context = Self::initialize_cuda_context(&config)?;
        let webgpu_context = Self::initialize_webgpu_context(&config)?;

        Ok(Self {
            cuda_context,
            webgpu_context,
            kernel_cache: Arc::new(Mutex::new(KernelCache::new())),
            performance_stats: Arc::new(Mutex::new(PerformanceStats::new())),
            config,
        })
    }

    /// Initialize CUDA context with specialized kernel compilation
    fn initialize_cuda_context(
        config: &OptimizationConfig,
    ) -> QuantRS2Result<Option<CudaSpecializedContext>> {
        // Check if CUDA is available
        if !Self::is_cuda_available() {
            return Ok(None);
        }

        let compute_capability = Self::get_compute_capability()?;
        let has_tensor_cores = compute_capability.0 >= 7; // Volta and later
        let device_props = Self::get_device_properties()?;

        let mut kernels = HashMap::new();

        // Compile specialized gate kernels
        kernels.insert(
            "holonomic_gate".to_string(),
            Self::compile_holonomic_kernel(config)?,
        );
        kernels.insert(
            "post_quantum_hash".to_string(),
            Self::compile_post_quantum_kernel(config)?,
        );
        kernels.insert(
            "quantum_ml_attention".to_string(),
            Self::compile_qml_attention_kernel(config)?,
        );
        kernels.insert(
            "fused_rotation_sequence".to_string(),
            Self::compile_fused_rotation_kernel(config)?,
        );
        kernels.insert(
            "tensor_core_matmul".to_string(),
            Self::compile_tensor_core_kernel(config)?,
        );

        Ok(Some(CudaSpecializedContext {
            compute_capability,
            has_tensor_cores,
            max_shared_memory: device_props.max_shared_memory,
            warp_size: device_props.warp_size,
            kernels,
        }))
    }

    /// Initialize WebGPU context with cross-platform shaders
    fn initialize_webgpu_context(
        config: &OptimizationConfig,
    ) -> QuantRS2Result<Option<WebGpuSpecializedContext>> {
        let device_limits = Self::get_webgpu_limits()?;
        let mut shaders = HashMap::new();
        let mut buffer_pools = HashMap::new();

        // Compile WebGPU shaders for specialized gates
        shaders.insert(
            "holonomic_gate".to_string(),
            Self::compile_holonomic_shader(config)?,
        );
        shaders.insert(
            "post_quantum_hash".to_string(),
            Self::compile_post_quantum_shader(config)?,
        );
        shaders.insert(
            "quantum_ml_attention".to_string(),
            Self::compile_qml_attention_shader(config)?,
        );

        // Initialize buffer pools
        buffer_pools.insert("state_vectors".to_string(), BufferPool::new(1024 * 1024)); // 1MB initial
        buffer_pools.insert("gate_matrices".to_string(), BufferPool::new(512 * 1024)); // 512KB initial
        buffer_pools.insert("temporary_buffers".to_string(), BufferPool::new(256 * 1024)); // 256KB initial

        Ok(Some(WebGpuSpecializedContext {
            device_limits,
            shaders,
            buffer_pools,
        }))
    }

    /// Apply a holonomic gate with optimized GPU execution
    pub fn apply_holonomic_gate(
        &self,
        state: &mut [Complex64],
        holonomy_matrix: &[Complex64],
        target_qubits: &[QubitId],
    ) -> QuantRS2Result<()> {
        let _num_qubits = target_qubits.len();
        let state_size = state.len();

        // Choose optimal execution path based on size and hardware
        if state_size > 1024 && self.cuda_context.is_some() {
            self.apply_holonomic_gate_cuda(state, holonomy_matrix, target_qubits)
        } else if self.webgpu_context.is_some() {
            self.apply_holonomic_gate_webgpu(state, holonomy_matrix, target_qubits)
        } else {
            // CPU fallback with SIMD optimization
            self.apply_holonomic_gate_cpu_optimized(state, holonomy_matrix, target_qubits)
        }
    }

    /// Apply holonomic gate using CUDA with tensor core optimization
    fn apply_holonomic_gate_cuda(
        &self,
        state: &mut [Complex64],
        holonomy_matrix: &[Complex64],
        target_qubits: &[QubitId],
    ) -> QuantRS2Result<()> {
        let cuda_ctx = self.cuda_context.as_ref().ok_or_else(|| {
            crate::error::QuantRS2Error::RuntimeError("CUDA context not available".to_string())
        })?;
        let kernel = cuda_ctx.kernels.get("holonomic_gate").ok_or_else(|| {
            crate::error::QuantRS2Error::RuntimeError("Holonomic gate kernel not found".to_string())
        })?;

        // Optimize block and grid dimensions
        let (block_dim, grid_dim) =
            self.calculate_optimal_dimensions(state.len(), target_qubits.len())?;

        // Use tensor cores if available and matrix size is suitable
        if cuda_ctx.has_tensor_cores && self.config.use_tensor_cores && holonomy_matrix.len() >= 256
        {
            self.launch_tensor_core_holonomic_kernel(
                kernel,
                state,
                holonomy_matrix,
                target_qubits,
                block_dim,
                grid_dim,
            )?;
        } else {
            self.launch_standard_holonomic_kernel(
                kernel,
                state,
                holonomy_matrix,
                target_qubits,
                block_dim,
                grid_dim,
            )?;
        }

        // Update performance statistics
        self.update_performance_stats("holonomic_gate_cuda", kernel.last_execution_time);

        Ok(())
    }

    /// Apply post-quantum cryptographic hash gate
    pub const fn apply_post_quantum_hash_gate(
        &self,
        state: &mut [Complex64],
        hash_circuit: &[Complex64],
        compression_type: PostQuantumCompressionType,
    ) -> QuantRS2Result<()> {
        match compression_type {
            PostQuantumCompressionType::QuantumSponge { rate, capacity } => {
                self.apply_quantum_sponge_gpu(state, hash_circuit, rate, capacity)
            }
            PostQuantumCompressionType::QuantumMerkleTree { depth, arity } => {
                self.apply_quantum_merkle_gpu(state, hash_circuit, depth, arity)
            }
            PostQuantumCompressionType::QuantumGrover { iterations } => {
                self.apply_quantum_grover_gpu(state, hash_circuit, iterations)
            }
        }
    }

    /// Apply quantum ML attention mechanism with GPU optimization
    pub const fn apply_quantum_ml_attention(
        &self,
        state: &mut [Complex64],
        query_params: &[Complex64],
        key_params: &[Complex64],
        value_params: &[Complex64],
        num_heads: usize,
    ) -> QuantRS2Result<()> {
        let attention_dim = state.len() / num_heads;

        if self.cuda_context.is_some() && attention_dim >= 64 {
            // Use CUDA for large attention computations
            self.apply_qml_attention_cuda(state, query_params, key_params, value_params, num_heads)
        } else if self.webgpu_context.is_some() {
            // Use WebGPU for medium-sized computations
            self.apply_qml_attention_webgpu(
                state,
                query_params,
                key_params,
                value_params,
                num_heads,
            )
        } else {
            // CPU fallback with vectorization
            self.apply_qml_attention_cpu_vectorized(
                state,
                query_params,
                key_params,
                value_params,
                num_heads,
            )
        }
    }

    /// Apply fused gate sequences for optimal performance
    pub fn apply_fused_gate_sequence(
        &self,
        state: &mut [Complex64],
        gates: &[Box<dyn GateOp>],
    ) -> QuantRS2Result<()> {
        if !self.config.enable_gate_fusion || gates.len() < 2 {
            // Apply gates individually if fusion is disabled or insufficient gates
            for gate in gates {
                self.apply_single_gate_optimized(state, gate.as_ref())?;
            }
            return Ok(());
        }

        // Analyze gates for fusion opportunities
        let fusion_chains = self.analyze_gate_fusion_opportunities(gates)?;

        for chain in fusion_chains {
            match chain.fusion_type {
                FusionType::RotationSequence => {
                    self.apply_fused_rotation_sequence(state, &chain.gates)?;
                }
                FusionType::PauliString => {
                    self.apply_fused_pauli_string(state, &chain.gates)?;
                }
                FusionType::ControlledSequence => {
                    self.apply_fused_controlled_sequence(state, &chain.gates)?;
                }
                FusionType::None => {
                    // Apply gates individually
                    for gate in &chain.gates {
                        self.apply_single_gate_optimized(state, gate.as_ref())?;
                    }
                }
            }
        }

        Ok(())
    }

    /// Calculate optimal GPU block and grid dimensions
    fn calculate_optimal_dimensions(
        &self,
        state_size: usize,
        num_target_qubits: usize,
    ) -> QuantRS2Result<(u32, u32)> {
        let _cuda_ctx = self.cuda_context.as_ref().ok_or_else(|| {
            crate::error::QuantRS2Error::RuntimeError(
                "CUDA context not available for dimension calculation".to_string(),
            )
        })?;

        // Calculate work per thread
        let work_per_thread = 1 << num_target_qubits; // 2^num_target_qubits
        let total_work_items = state_size / work_per_thread;

        // Optimize for memory coalescing
        let threads_per_block = if total_work_items >= 1024 {
            1024
        } else if total_work_items >= 512 {
            512
        } else if total_work_items >= 256 {
            256
        } else {
            128.max(32) // Minimum warp size
        };

        let blocks = (total_work_items + threads_per_block - 1) / threads_per_block;

        Ok((threads_per_block as u32, blocks as u32))
    }

    /// Update performance statistics
    fn update_performance_stats(&self, kernel_name: &str, execution_time: f64) {
        if let Ok(mut stats) = self.performance_stats.lock() {
            stats
                .kernel_times
                .entry(kernel_name.to_string())
                .or_insert_with(Vec::new)
                .push(execution_time);
        }
        // Silently ignore lock poisoning for performance stats update
    }

    /// Get performance report
    pub fn get_performance_report(&self) -> PerformanceReport {
        let stats = self
            .performance_stats
            .lock()
            .unwrap_or_else(|e| e.into_inner());
        let cache = self.kernel_cache.lock().unwrap_or_else(|e| e.into_inner());

        PerformanceReport {
            average_kernel_times: stats
                .kernel_times
                .iter()
                .map(|(k, v)| (k.clone(), v.iter().sum::<f64>() / v.len() as f64))
                .collect(),
            cache_hit_rate: cache.cache_stats.overall_hit_rate(),
            tensor_core_utilization: stats.tensor_core_utilization,
            memory_bandwidth_utilization: stats.memory_bandwidth.values().sum::<f64>()
                / stats.memory_bandwidth.len() as f64,
        }
    }

    // Placeholder implementations for specialized kernel methods
    const fn is_cuda_available() -> bool {
        false
    } // Would check actual CUDA availability
    const fn get_compute_capability() -> QuantRS2Result<(i32, i32)> {
        Ok((7, 5))
    }
    const fn get_device_properties() -> QuantRS2Result<DeviceProperties> {
        Ok(DeviceProperties {
            max_shared_memory: 49152,
            warp_size: 32,
        })
    }
    const fn get_webgpu_limits() -> QuantRS2Result<WebGpuLimits> {
        Ok(WebGpuLimits {
            max_compute_workgroup_size: 256,
        })
    }

    fn compile_holonomic_kernel(_config: &OptimizationConfig) -> QuantRS2Result<CompiledKernel> {
        Ok(CompiledKernel {
            name: "holonomic".to_string(),
            last_execution_time: 0.0,
        })
    }
    fn compile_post_quantum_kernel(_config: &OptimizationConfig) -> QuantRS2Result<CompiledKernel> {
        Ok(CompiledKernel {
            name: "post_quantum".to_string(),
            last_execution_time: 0.0,
        })
    }
    fn compile_qml_attention_kernel(
        _config: &OptimizationConfig,
    ) -> QuantRS2Result<CompiledKernel> {
        Ok(CompiledKernel {
            name: "qml_attention".to_string(),
            last_execution_time: 0.0,
        })
    }
    fn compile_fused_rotation_kernel(
        _config: &OptimizationConfig,
    ) -> QuantRS2Result<CompiledKernel> {
        Ok(CompiledKernel {
            name: "fused_rotation".to_string(),
            last_execution_time: 0.0,
        })
    }
    fn compile_tensor_core_kernel(_config: &OptimizationConfig) -> QuantRS2Result<CompiledKernel> {
        Ok(CompiledKernel {
            name: "tensor_core".to_string(),
            last_execution_time: 0.0,
        })
    }

    fn compile_holonomic_shader(_config: &OptimizationConfig) -> QuantRS2Result<CompiledShader> {
        Ok(CompiledShader {
            name: "holonomic".to_string(),
        })
    }
    fn compile_post_quantum_shader(_config: &OptimizationConfig) -> QuantRS2Result<CompiledShader> {
        Ok(CompiledShader {
            name: "post_quantum".to_string(),
        })
    }
    fn compile_qml_attention_shader(
        _config: &OptimizationConfig,
    ) -> QuantRS2Result<CompiledShader> {
        Ok(CompiledShader {
            name: "qml_attention".to_string(),
        })
    }

    // Placeholder kernel launch methods
    const fn launch_tensor_core_holonomic_kernel(
        &self,
        _kernel: &CompiledKernel,
        _state: &mut [Complex64],
        _matrix: &[Complex64],
        _qubits: &[QubitId],
        _block: u32,
        _grid: u32,
    ) -> QuantRS2Result<()> {
        Ok(())
    }
    const fn launch_standard_holonomic_kernel(
        &self,
        _kernel: &CompiledKernel,
        _state: &mut [Complex64],
        _matrix: &[Complex64],
        _qubits: &[QubitId],
        _block: u32,
        _grid: u32,
    ) -> QuantRS2Result<()> {
        Ok(())
    }

    const fn apply_holonomic_gate_webgpu(
        &self,
        _state: &mut [Complex64],
        _matrix: &[Complex64],
        _qubits: &[QubitId],
    ) -> QuantRS2Result<()> {
        Ok(())
    }
    const fn apply_holonomic_gate_cpu_optimized(
        &self,
        _state: &mut [Complex64],
        _matrix: &[Complex64],
        _qubits: &[QubitId],
    ) -> QuantRS2Result<()> {
        Ok(())
    }

    const fn apply_quantum_sponge_gpu(
        &self,
        _state: &mut [Complex64],
        _circuit: &[Complex64],
        _rate: usize,
        _capacity: usize,
    ) -> QuantRS2Result<()> {
        Ok(())
    }
    const fn apply_quantum_merkle_gpu(
        &self,
        _state: &mut [Complex64],
        _circuit: &[Complex64],
        _depth: usize,
        _arity: usize,
    ) -> QuantRS2Result<()> {
        Ok(())
    }
    const fn apply_quantum_grover_gpu(
        &self,
        _state: &mut [Complex64],
        _circuit: &[Complex64],
        _iterations: usize,
    ) -> QuantRS2Result<()> {
        Ok(())
    }

    const fn apply_qml_attention_cuda(
        &self,
        _state: &mut [Complex64],
        _query: &[Complex64],
        _key: &[Complex64],
        _value: &[Complex64],
        _heads: usize,
    ) -> QuantRS2Result<()> {
        Ok(())
    }
    const fn apply_qml_attention_webgpu(
        &self,
        _state: &mut [Complex64],
        _query: &[Complex64],
        _key: &[Complex64],
        _value: &[Complex64],
        _heads: usize,
    ) -> QuantRS2Result<()> {
        Ok(())
    }
    const fn apply_qml_attention_cpu_vectorized(
        &self,
        _state: &mut [Complex64],
        _query: &[Complex64],
        _key: &[Complex64],
        _value: &[Complex64],
        _heads: usize,
    ) -> QuantRS2Result<()> {
        Ok(())
    }

    fn apply_single_gate_optimized(
        &self,
        _state: &mut [Complex64],
        _gate: &dyn GateOp,
    ) -> QuantRS2Result<()> {
        Ok(())
    }
    fn analyze_gate_fusion_opportunities(
        &self,
        _gates: &[Box<dyn GateOp>],
    ) -> QuantRS2Result<Vec<FusionChain>> {
        Ok(vec![])
    }
    fn apply_fused_rotation_sequence(
        &self,
        _state: &mut [Complex64],
        _gates: &[Box<dyn GateOp>],
    ) -> QuantRS2Result<()> {
        Ok(())
    }
    fn apply_fused_pauli_string(
        &self,
        _state: &mut [Complex64],
        _gates: &[Box<dyn GateOp>],
    ) -> QuantRS2Result<()> {
        Ok(())
    }
    fn apply_fused_controlled_sequence(
        &self,
        _state: &mut [Complex64],
        _gates: &[Box<dyn GateOp>],
    ) -> QuantRS2Result<()> {
        Ok(())
    }
}

/// Supporting types and structures

#[derive(Debug, Clone)]
pub enum PostQuantumCompressionType {
    QuantumSponge { rate: usize, capacity: usize },
    QuantumMerkleTree { depth: usize, arity: usize },
    QuantumGrover { iterations: usize },
}

#[derive(Debug, Clone)]
pub enum FusionType {
    RotationSequence,
    PauliString,
    ControlledSequence,
    None,
}

pub struct FusionChain {
    pub gates: Vec<Box<dyn GateOp>>,
    pub fusion_type: FusionType,
}

pub struct CompiledKernel {
    pub name: String,
    pub last_execution_time: f64,
}

pub struct CompiledShader {
    pub name: String,
}

pub struct CachedCudaKernel {
    pub kernel: CompiledKernel,
    pub compilation_time: f64,
}

pub struct CachedWebGpuShader {
    pub shader: CompiledShader,
    pub compilation_time: f64,
}

pub struct CacheStatistics {
    pub hits: usize,
    pub misses: usize,
}

impl CacheStatistics {
    pub fn overall_hit_rate(&self) -> f64 {
        if self.hits + self.misses == 0 {
            0.0
        } else {
            self.hits as f64 / (self.hits + self.misses) as f64
        }
    }
}

pub struct BufferPool {
    pub initial_size: usize,
}

impl BufferPool {
    pub const fn new(initial_size: usize) -> Self {
        Self { initial_size }
    }
}

pub struct DeviceProperties {
    pub max_shared_memory: usize,
    pub warp_size: usize,
}

pub struct WebGpuLimits {
    pub max_compute_workgroup_size: u32,
}

pub struct PerformanceReport {
    pub average_kernel_times: HashMap<String, f64>,
    pub cache_hit_rate: f64,
    pub tensor_core_utilization: f64,
    pub memory_bandwidth_utilization: f64,
}

impl KernelCache {
    pub fn new() -> Self {
        Self {
            cuda_kernels: HashMap::new(),
            webgpu_shaders: HashMap::new(),
            cache_stats: CacheStatistics { hits: 0, misses: 0 },
        }
    }
}

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

impl PerformanceStats {
    pub fn new() -> Self {
        Self {
            kernel_times: HashMap::new(),
            memory_bandwidth: HashMap::new(),
            tensor_core_utilization: 0.0,
            cache_hit_rates: HashMap::new(),
        }
    }
}

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

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

    #[test]
    fn test_specialized_gpu_kernels_creation() {
        let config = OptimizationConfig::default();
        let kernels = SpecializedGpuKernels::new(config);
        assert!(kernels.is_ok());
    }

    #[test]
    fn test_holonomic_gate_application() {
        let config = OptimizationConfig::default();
        let kernels =
            SpecializedGpuKernels::new(config).expect("Failed to create specialized GPU kernels");

        let mut state = vec![Complex64::new(1.0, 0.0), Complex64::new(0.0, 0.0)];
        let holonomy_matrix = vec![
            Complex64::new(1.0, 0.0),
            Complex64::new(0.0, 0.0),
            Complex64::new(0.0, 0.0),
            Complex64::new(1.0, 0.0),
        ];
        let target_qubits = vec![QubitId(0)];

        let result = kernels.apply_holonomic_gate(&mut state, &holonomy_matrix, &target_qubits);
        assert!(result.is_ok());
    }

    #[test]
    fn test_performance_reporting() {
        let config = OptimizationConfig::default();
        let kernels = SpecializedGpuKernels::new(config)
            .expect("Failed to create specialized GPU kernels for performance reporting");

        let report = kernels.get_performance_report();
        assert!(report.cache_hit_rate >= 0.0 && report.cache_hit_rate <= 1.0);
    }
}