scirs2-metrics 0.3.0

//! GPU runtime interface and implementations
//!
//! This module provides the GPU runtime trait and concrete implementations
//! for different compute backends (CUDA, OpenCL, Metal, Vulkan).

#![allow(clippy::too_many_arguments)]
#![allow(dead_code)]

use crate::error::{MetricsError, Result};
use scirs2_core::ndarray::{Array1, Array2, ArrayView1, ArrayView2};
use scirs2_core::numeric::{Float, NumCast};
use std::collections::HashMap;
use std::time::{Duration, Instant};

/// GPU runtime interface trait for different backends
pub trait GpuRuntime: Send + Sync {
    /// Initialize the GPU runtime
    fn initialize(&mut self) -> Result<()>;

    /// Check if GPU is available
    fn is_available(&self) -> bool;

    /// Get device information
    fn device_info(&self) -> HashMap<String, String>;

    /// Allocate GPU memory
    fn allocate<T: Float>(&mut self, size: usize) -> Result<GpuBuffer>;

    /// Transfer data to GPU
    fn transfer_to_gpu<T: Float>(&mut self, data: &[T], buffer: &GpuBuffer) -> Result<()>;

    /// Transfer data from GPU
    fn transfer_from_gpu<T: Float>(&mut self, buffer: &GpuBuffer, data: &mut [T]) -> Result<()>;

    /// Launch kernel
    fn launch_kernel(
        &mut self,
        kernel_name: &str,
        grid_size: (u32, u32, u32),
        block_size: (u32, u32, u32),
        args: &[GpuKernelArg],
    ) -> Result<()>;

    /// Synchronize GPU execution
    fn synchronize(&mut self) -> Result<()>;

    /// Release GPU memory
    fn deallocate(&mut self, buffer: &GpuBuffer) -> Result<()>;

    /// Get memory usage statistics
    fn memory_stats(&self) -> GpuMemoryStats;

    /// Get performance statistics
    fn performance_stats(&self) -> GpuPerformanceStats;
}

/// GPU buffer handle
#[derive(Debug, Clone)]
pub struct GpuBuffer {
    /// Buffer ID
    pub id: u64,
    /// Size in bytes
    pub size: usize,
    /// Buffer type
    pub buffer_type: GpuBufferType,
    /// Backend-specific handle
    pub handle: GpuBufferHandle,
}

/// GPU buffer type
#[derive(Debug, Clone)]
pub enum GpuBufferType {
    /// Input buffer (read-only)
    Input,
    /// Output buffer (write-only)
    Output,
    /// Input/Output buffer (read-write)
    InputOutput,
    /// Constant buffer
    Constant,
}

/// Backend-specific buffer handle
#[derive(Debug, Clone)]
pub enum GpuBufferHandle {
    /// CUDA device pointer
    Cuda(u64),
    /// OpenCL memory object
    OpenCL(u64),
    /// Metal buffer
    Metal(u64),
    /// Vulkan buffer
    Vulkan(u64),
}

/// GPU kernel argument
#[derive(Debug, Clone)]
pub enum GpuKernelArg {
    /// Buffer argument
    Buffer(GpuBuffer),
    /// Scalar value
    Scalar(GpuScalar),
}

/// GPU scalar value
#[derive(Debug, Clone)]
pub enum GpuScalar {
    /// 32-bit float
    F32(f32),
    /// 64-bit float
    F64(f64),
    /// 32-bit integer
    I32(i32),
    /// 64-bit integer
    I64(i64),
    /// 32-bit unsigned integer
    U32(u32),
    /// 64-bit unsigned integer
    U64(u64),
}

/// GPU memory statistics
#[derive(Debug, Clone)]
pub struct GpuMemoryStats {
    /// Total memory in bytes
    pub total_memory: u64,
    /// Free memory in bytes
    pub free_memory: u64,
    /// Used memory in bytes
    pub used_memory: u64,
    /// Number of allocations
    pub allocation_count: u64,
}

/// GPU performance statistics
#[derive(Debug, Clone)]
pub struct GpuPerformanceStats {
    /// Total kernel execution time
    pub total_kernel_time: Duration,
    /// Memory transfer time
    pub memory_transfer_time: Duration,
    /// Number of kernel launches
    pub kernel_launches: u64,
    /// GPU utilization percentage
    pub gpu_utilization: f64,
    /// Memory bandwidth utilization
    pub memory_bandwidth_utilization: f64,
}

/// CUDA runtime implementation
#[derive(Debug)]
pub struct CudaRuntime {
    /// Device ID
    device_id: i32,
    /// Context handle
    context: Option<u64>,
    /// Stream handle
    stream: Option<u64>,
    /// Memory statistics
    memory_stats: GpuMemoryStats,
    /// Performance statistics
    performance_stats: GpuPerformanceStats,
}

impl CudaRuntime {
    /// Create new CUDA runtime
    pub fn new(device_id: i32) -> Self {
        Self {
            device_id,
            context: None,
            stream: None,
            memory_stats: GpuMemoryStats::default(),
            performance_stats: GpuPerformanceStats::default(),
        }
    }
}

impl GpuRuntime for CudaRuntime {
    fn initialize(&mut self) -> Result<()> {
        // Initialize CUDA context and stream
        // This would use actual CUDA API calls
        self.context = Some(0x12345678); // Placeholder
        self.stream = Some(0x87654321); // Placeholder
        Ok(())
    }

    fn is_available(&self) -> bool {
        // Check CUDA availability
        true // Placeholder
    }

    fn device_info(&self) -> HashMap<String, String> {
        let mut info = HashMap::new();
        info.insert("backend".to_string(), "CUDA".to_string());
        info.insert("device_id".to_string(), self.device_id.to_string());
        info.insert("compute_capability".to_string(), "8.0".to_string());
        info.insert("memory".to_string(), "8GB".to_string());
        info
    }

    fn allocate<T: Float>(&mut self, size: usize) -> Result<GpuBuffer> {
        let buffer_size = size * std::mem::size_of::<T>();
        let buffer = GpuBuffer {
            id: scirs2_core::random::random::<u64>(),
            size: buffer_size,
            buffer_type: GpuBufferType::InputOutput,
            handle: GpuBufferHandle::Cuda(0x11111111), // Placeholder
        };
        self.memory_stats.used_memory += buffer_size as u64;
        self.memory_stats.allocation_count += 1;
        Ok(buffer)
    }

    fn transfer_to_gpu<T: Float>(&mut self, _data: &[T], _buffer: &GpuBuffer) -> Result<()> {
        // Transfer data to GPU
        Ok(())
    }

    fn transfer_from_gpu<T: Float>(&mut self, _buffer: &GpuBuffer, _data: &mut [T]) -> Result<()> {
        // Transfer data from GPU
        Ok(())
    }

    fn launch_kernel(
        &mut self,
        _kernel_name: &str,
        _grid_size: (u32, u32, u32),
        _block_size: (u32, u32, u32),
        _args: &[GpuKernelArg],
    ) -> Result<()> {
        // Launch CUDA kernel
        self.performance_stats.kernel_launches += 1;
        Ok(())
    }

    fn synchronize(&mut self) -> Result<()> {
        // Synchronize CUDA stream
        Ok(())
    }

    fn deallocate(&mut self, buffer: &GpuBuffer) -> Result<()> {
        self.memory_stats.used_memory = self
            .memory_stats
            .used_memory
            .saturating_sub(buffer.size as u64);
        self.memory_stats.allocation_count = self.memory_stats.allocation_count.saturating_sub(1);
        Ok(())
    }

    fn memory_stats(&self) -> GpuMemoryStats {
        self.memory_stats.clone()
    }

    fn performance_stats(&self) -> GpuPerformanceStats {
        self.performance_stats.clone()
    }
}

/// OpenCL runtime implementation
#[derive(Debug)]
pub struct OpenClRuntime {
    /// Platform ID
    platform_id: u64,
    /// Device ID
    device_id: u64,
    /// Context handle
    context: Option<u64>,
    /// Command queue handle
    command_queue: Option<u64>,
    /// Memory statistics
    memory_stats: GpuMemoryStats,
    /// Performance statistics
    performance_stats: GpuPerformanceStats,
}

impl OpenClRuntime {
    /// Create new OpenCL runtime
    pub fn new(platform_id: u64, device_id: u64) -> Self {
        Self {
            platform_id,
            device_id,
            context: None,
            command_queue: None,
            memory_stats: GpuMemoryStats::default(),
            performance_stats: GpuPerformanceStats::default(),
        }
    }
}

/// Metal runtime implementation for macOS
#[derive(Debug)]
pub struct MetalRuntime {
    /// Device handle
    device: Option<u64>,
    /// Command queue handle
    command_queue: Option<u64>,
    /// Memory statistics
    memory_stats: GpuMemoryStats,
    /// Performance statistics
    performance_stats: GpuPerformanceStats,
}

impl MetalRuntime {
    /// Create new Metal runtime
    pub fn new() -> Self {
        Self {
            device: None,
            command_queue: None,
            memory_stats: GpuMemoryStats::default(),
            performance_stats: GpuPerformanceStats::default(),
        }
    }
}

impl GpuRuntime for MetalRuntime {
    fn initialize(&mut self) -> Result<()> {
        // Initialize Metal device and command queue
        self.device = Some(0x22222222); // Placeholder
        self.command_queue = Some(0x33333333); // Placeholder
        Ok(())
    }

    fn is_available(&self) -> bool {
        // Check Metal availability (macOS only)
        cfg!(target_os = "macos")
    }

    fn device_info(&self) -> HashMap<String, String> {
        let mut info = HashMap::new();
        info.insert("backend".to_string(), "Metal".to_string());
        info.insert("device_name".to_string(), "Apple GPU".to_string());
        info
    }

    fn allocate<T: Float>(&mut self, size: usize) -> Result<GpuBuffer> {
        let buffer_size = size * std::mem::size_of::<T>();
        let buffer = GpuBuffer {
            id: scirs2_core::random::random::<u64>(),
            size: buffer_size,
            buffer_type: GpuBufferType::InputOutput,
            handle: GpuBufferHandle::Metal(0x44444444), // Placeholder
        };
        Ok(buffer)
    }

    fn transfer_to_gpu<T: Float>(&mut self, _data: &[T], _buffer: &GpuBuffer) -> Result<()> {
        Ok(())
    }

    fn transfer_from_gpu<T: Float>(&mut self, _buffer: &GpuBuffer, _data: &mut [T]) -> Result<()> {
        Ok(())
    }

    fn launch_kernel(
        &mut self,
        _kernel_name: &str,
        _grid_size: (u32, u32, u32),
        _block_size: (u32, u32, u32),
        _args: &[GpuKernelArg],
    ) -> Result<()> {
        Ok(())
    }

    fn synchronize(&mut self) -> Result<()> {
        Ok(())
    }

    fn deallocate(&mut self, _buffer: &GpuBuffer) -> Result<()> {
        Ok(())
    }

    fn memory_stats(&self) -> GpuMemoryStats {
        self.memory_stats.clone()
    }

    fn performance_stats(&self) -> GpuPerformanceStats {
        self.performance_stats.clone()
    }
}

/// Vulkan runtime implementation for cross-platform compute
#[derive(Debug)]
pub struct VulkanRuntime {
    /// Instance handle
    instance: Option<u64>,
    /// Device handle
    device: Option<u64>,
    /// Command pool handle
    command_pool: Option<u64>,
    /// Memory statistics
    memory_stats: GpuMemoryStats,
    /// Performance statistics
    performance_stats: GpuPerformanceStats,
}

impl VulkanRuntime {
    /// Create new Vulkan runtime
    pub fn new() -> Self {
        Self {
            instance: None,
            device: None,
            command_pool: None,
            memory_stats: GpuMemoryStats::default(),
            performance_stats: GpuPerformanceStats::default(),
        }
    }
}

impl GpuRuntime for VulkanRuntime {
    fn initialize(&mut self) -> Result<()> {
        // Initialize Vulkan instance, device, and command pool
        self.instance = Some(0x55555555); // Placeholder
        self.device = Some(0x66666666); // Placeholder
        self.command_pool = Some(0x77777777); // Placeholder
        Ok(())
    }

    fn is_available(&self) -> bool {
        // Check Vulkan availability
        true // Placeholder
    }

    fn device_info(&self) -> HashMap<String, String> {
        let mut info = HashMap::new();
        info.insert("backend".to_string(), "Vulkan".to_string());
        info.insert("api_version".to_string(), "1.3".to_string());
        info
    }

    fn allocate<T: Float>(&mut self, size: usize) -> Result<GpuBuffer> {
        let buffer_size = size * std::mem::size_of::<T>();
        let buffer = GpuBuffer {
            id: scirs2_core::random::random::<u64>(),
            size: buffer_size,
            buffer_type: GpuBufferType::InputOutput,
            handle: GpuBufferHandle::Vulkan(0x88888888), // Placeholder
        };
        Ok(buffer)
    }

    fn transfer_to_gpu<T: Float>(&mut self, _data: &[T], _buffer: &GpuBuffer) -> Result<()> {
        Ok(())
    }

    fn transfer_from_gpu<T: Float>(&mut self, _buffer: &GpuBuffer, _data: &mut [T]) -> Result<()> {
        Ok(())
    }

    fn launch_kernel(
        &mut self,
        _kernel_name: &str,
        _grid_size: (u32, u32, u32),
        _block_size: (u32, u32, u32),
        _args: &[GpuKernelArg],
    ) -> Result<()> {
        Ok(())
    }

    fn synchronize(&mut self) -> Result<()> {
        Ok(())
    }

    fn deallocate(&mut self, _buffer: &GpuBuffer) -> Result<()> {
        Ok(())
    }

    fn memory_stats(&self) -> GpuMemoryStats {
        self.memory_stats.clone()
    }

    fn performance_stats(&self) -> GpuPerformanceStats {
        self.performance_stats.clone()
    }
}

impl GpuRuntime for OpenClRuntime {
    fn initialize(&mut self) -> Result<()> {
        // Initialize OpenCL context and command queue
        self.context = Some(0xAAAAAAAA); // Placeholder
        self.command_queue = Some(0xBBBBBBBB); // Placeholder
        Ok(())
    }

    fn is_available(&self) -> bool {
        // Check OpenCL availability
        true // Placeholder
    }

    fn device_info(&self) -> HashMap<String, String> {
        let mut info = HashMap::new();
        info.insert("backend".to_string(), "OpenCL".to_string());
        info.insert("platform_id".to_string(), self.platform_id.to_string());
        info.insert("device_id".to_string(), self.device_id.to_string());
        info
    }

    fn allocate<T: Float>(&mut self, size: usize) -> Result<GpuBuffer> {
        let buffer_size = size * std::mem::size_of::<T>();
        let buffer = GpuBuffer {
            id: scirs2_core::random::random::<u64>(),
            size: buffer_size,
            buffer_type: GpuBufferType::InputOutput,
            handle: GpuBufferHandle::OpenCL(0xCCCCCCCC), // Placeholder
        };
        Ok(buffer)
    }

    fn transfer_to_gpu<T: Float>(&mut self, _data: &[T], _buffer: &GpuBuffer) -> Result<()> {
        Ok(())
    }

    fn transfer_from_gpu<T: Float>(&mut self, _buffer: &GpuBuffer, _data: &mut [T]) -> Result<()> {
        Ok(())
    }

    fn launch_kernel(
        &mut self,
        _kernel_name: &str,
        _grid_size: (u32, u32, u32),
        _block_size: (u32, u32, u32),
        _args: &[GpuKernelArg],
    ) -> Result<()> {
        Ok(())
    }

    fn synchronize(&mut self) -> Result<()> {
        Ok(())
    }

    fn deallocate(&mut self, _buffer: &GpuBuffer) -> Result<()> {
        Ok(())
    }

    fn memory_stats(&self) -> GpuMemoryStats {
        self.memory_stats.clone()
    }

    fn performance_stats(&self) -> GpuPerformanceStats {
        self.performance_stats.clone()
    }
}

impl Default for GpuMemoryStats {
    fn default() -> Self {
        Self {
            total_memory: 8 * 1024 * 1024 * 1024, // 8GB placeholder
            free_memory: 8 * 1024 * 1024 * 1024,
            used_memory: 0,
            allocation_count: 0,
        }
    }
}

impl Default for GpuPerformanceStats {
    fn default() -> Self {
        Self {
            total_kernel_time: Duration::new(0, 0),
            memory_transfer_time: Duration::new(0, 0),
            kernel_launches: 0,
            gpu_utilization: 0.0,
            memory_bandwidth_utilization: 0.0,
        }
    }
}

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

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