quantrs2-sim 0.1.3

//! CUDA context management and device properties.
//!
//! This module provides CUDA device initialization, context management,
//! and device property queries for GPU-accelerated quantum simulations.

#[cfg(feature = "advanced_math")]
use std::collections::HashMap;
#[cfg(feature = "advanced_math")]
use std::sync::{Arc, Mutex};

#[cfg(feature = "advanced_math")]
use crate::error::Result;

#[cfg(feature = "advanced_math")]
use super::memory::GpuMemoryPool;

// Placeholder types for actual CUDA handles
#[cfg(feature = "advanced_math")]
pub type CudaDevicePointer = usize;
#[cfg(feature = "advanced_math")]
pub type CudaEvent = usize;

#[cfg(feature = "advanced_math")]
pub struct CudaContext {
    device_id: i32,
    device_properties: CudaDeviceProperties,
    memory_pool: Arc<Mutex<GpuMemoryPool>>,
    profiler: Option<CudaProfiler>,
}

#[cfg(feature = "advanced_math")]
#[derive(Debug, Clone)]
pub struct CudaDeviceProperties {
    pub name: String,
    pub compute_capability: (i32, i32),
    pub total_global_memory: usize,
    pub max_threads_per_block: i32,
    pub max_block_dimensions: [i32; 3],
    pub max_grid_dimensions: [i32; 3],
    pub warp_size: i32,
    pub memory_clock_rate: i32,
    pub memory_bus_width: i32,
}

#[cfg(feature = "advanced_math")]
pub struct CudaProfiler {
    pub events: Vec<CudaEvent>,
    pub timing_data: HashMap<String, Vec<f64>>,
    pub memory_usage: Vec<(String, usize)>,
}

#[cfg(feature = "advanced_math")]
impl CudaContext {
    pub fn new(device_id: i32) -> Result<Self> {
        // Initialize CUDA context with device properties
        let device_properties = Self::query_device_properties(device_id)?;
        let memory_pool = Arc::new(Mutex::new(GpuMemoryPool::new()));
        let profiler = if cfg!(debug_assertions) {
            Some(CudaProfiler::new())
        } else {
            None
        };

        Ok(Self {
            device_id,
            device_properties,
            memory_pool,
            profiler,
        })
    }

    pub fn get_device_count() -> Result<i32> {
        // Query actual CUDA device count
        // In a real implementation, this would call cudaGetDeviceCount
        #[cfg(feature = "advanced_math")]
        {
            // Simulate querying CUDA devices
            let device_count = Self::query_cuda_devices()?;
            Ok(device_count)
        }
        #[cfg(not(feature = "advanced_math"))]
        {
            Ok(0)
        }
    }

    fn query_device_properties(device_id: i32) -> Result<CudaDeviceProperties> {
        // In real implementation, would call cudaGetDeviceProperties
        Ok(CudaDeviceProperties {
            name: format!("CUDA Device {}", device_id),
            compute_capability: (7, 5),         // Example: RTX 20xx series
            total_global_memory: 8_000_000_000, // 8GB
            max_threads_per_block: 1024,
            max_block_dimensions: [1024, 1024, 64],
            max_grid_dimensions: [2_147_483_647, 65_535, 65_535],
            warp_size: 32,
            memory_clock_rate: 7000, // MHz
            memory_bus_width: 256,
        })
    }

    fn query_cuda_devices() -> Result<i32> {
        // In real implementation: cudaGetDeviceCount(&count)
        // For now, simulate detection of available devices
        Ok(if std::env::var("CUDA_VISIBLE_DEVICES").is_ok() {
            1
        } else {
            0
        })
    }

    pub fn get_device_properties(&self) -> &CudaDeviceProperties {
        &self.device_properties
    }

    pub fn get_memory_info(&self) -> Result<(usize, usize)> {
        // Return (free_memory, total_memory)
        // In real implementation: cudaMemGetInfo
        let pool = self.memory_pool.lock().unwrap_or_else(|e| e.into_inner());
        let total = self.device_properties.total_global_memory;
        let used = pool.total_allocated;
        Ok((total - used, total))
    }

    pub fn get_memory_pool(&self) -> Arc<Mutex<GpuMemoryPool>> {
        Arc::clone(&self.memory_pool)
    }

    pub fn get_profiler(&mut self) -> Option<&mut CudaProfiler> {
        self.profiler.as_mut()
    }
}

#[cfg(feature = "advanced_math")]
impl CudaProfiler {
    pub fn new() -> Self {
        Self {
            events: Vec::new(),
            timing_data: HashMap::new(),
            memory_usage: Vec::new(),
        }
    }

    pub fn record_event(&mut self, name: String, event: CudaEvent) {
        self.events.push(event);
        // In real implementation, would record timing
    }

    pub fn record_timing(&mut self, operation: String, time_ms: f64) {
        self.timing_data.entry(operation).or_default().push(time_ms);
    }

    pub fn record_memory_usage(&mut self, operation: String, bytes: usize) {
        self.memory_usage.push((operation, bytes));
    }

    pub fn get_average_timing(&self, operation: &str) -> Option<f64> {
        self.timing_data
            .get(operation)
            .map(|times| times.iter().sum::<f64>() / times.len() as f64)
    }

    pub fn get_peak_memory_usage(&self) -> usize {
        self.memory_usage
            .iter()
            .map(|(_, bytes)| *bytes)
            .max()
            .unwrap_or(0)
    }
}