quantrs2_anneal/scientific_performance_optimization/

profiling.rs

//! Profiling and GPU acceleration types for scientific performance optimization.
//!
//! This module contains performance profiling, CPU profiling, memory profiling,
//! I/O profiling, and GPU acceleration.

use std::collections::{HashMap, VecDeque};
use std::time::{Duration, Instant};

use super::config::{GPUAccelerationConfig, ProfilingConfig};

/// Performance profiler for system monitoring
pub struct PerformanceProfiler {
    /// Configuration
    pub config: ProfilingConfig,
    /// CPU profiler
    pub cpu_profiler: CPUProfiler,
    /// Memory profiler
    pub memory_profiler: MemoryProfiler,
    /// I/O profiler
    pub io_profiler: IOProfiler,
    /// Performance metrics
    pub metrics: PerformanceMetrics,
}

impl PerformanceProfiler {
    /// Create a new performance profiler
    #[must_use]
    pub fn new(config: ProfilingConfig) -> Self {
        Self {
            config,
            cpu_profiler: CPUProfiler::new(),
            memory_profiler: MemoryProfiler::new(),
            io_profiler: IOProfiler::new(),
            metrics: PerformanceMetrics::default(),
        }
    }

    /// Start profiling
    pub fn start(&mut self) {
        self.cpu_profiler.start();
        self.memory_profiler.start();
        self.io_profiler.start();
    }

    /// Stop profiling and collect metrics
    pub fn stop(&mut self) -> PerformanceMetrics {
        self.cpu_profiler.stop();
        self.memory_profiler.stop();
        self.io_profiler.stop();
        self.metrics.clone()
    }

    /// Take a snapshot of current metrics
    pub fn snapshot(&mut self) {
        self.cpu_profiler.sample();
        self.memory_profiler.sample();
        self.io_profiler.sample();
    }
}

/// CPU performance profiler
#[derive(Debug)]
pub struct CPUProfiler {
    /// CPU usage samples
    pub cpu_samples: VecDeque<CPUSample>,
    /// Function call statistics
    pub function_stats: HashMap<String, FunctionStatistics>,
    /// Profiling configuration
    pub config: CPUProfilingConfig,
    /// Is profiling active
    pub is_active: bool,
}

impl CPUProfiler {
    /// Create a new CPU profiler
    #[must_use]
    pub fn new() -> Self {
        Self {
            cpu_samples: VecDeque::new(),
            function_stats: HashMap::new(),
            config: CPUProfilingConfig::default(),
            is_active: false,
        }
    }

    /// Start CPU profiling
    pub fn start(&mut self) {
        self.is_active = true;
        self.cpu_samples.clear();
    }

    /// Stop CPU profiling
    pub fn stop(&mut self) {
        self.is_active = false;
    }

    /// Take a CPU sample
    pub fn sample(&mut self) {
        if !self.is_active {
            return;
        }

        let sample = CPUSample {
            timestamp: Instant::now(),
            usage_percent: 0.0, // Would need system call to get real value
            active_threads: std::thread::available_parallelism()
                .map(|p| p.get())
                .unwrap_or(1),
            context_switches: 0,
        };

        self.cpu_samples.push_back(sample);

        // Keep only recent samples
        while self.cpu_samples.len() > self.config.max_samples {
            self.cpu_samples.pop_front();
        }
    }

    /// Record function call
    pub fn record_function_call(&mut self, function_name: &str, duration: Duration) {
        let stats = self
            .function_stats
            .entry(function_name.to_string())
            .or_insert_with(|| FunctionStatistics::new(function_name));

        stats.call_count += 1;
        stats.total_time += duration;
        stats.average_time = stats.total_time / stats.call_count as u32;

        if duration > stats.max_time {
            stats.max_time = duration;
        }
        if stats.min_time == Duration::ZERO || duration < stats.min_time {
            stats.min_time = duration;
        }
    }

    /// Get average CPU usage
    #[must_use]
    pub fn average_usage(&self) -> f64 {
        if self.cpu_samples.is_empty() {
            return 0.0;
        }
        let sum: f64 = self.cpu_samples.iter().map(|s| s.usage_percent).sum();
        sum / self.cpu_samples.len() as f64
    }
}

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

/// CPU usage sample
#[derive(Debug, Clone)]
pub struct CPUSample {
    /// Timestamp
    pub timestamp: Instant,
    /// CPU usage percentage
    pub usage_percent: f64,
    /// Active threads
    pub active_threads: usize,
    /// Context switches
    pub context_switches: u64,
}

/// Function call statistics
#[derive(Debug, Clone)]
pub struct FunctionStatistics {
    /// Function name
    pub function_name: String,
    /// Total call count
    pub call_count: u64,
    /// Total execution time
    pub total_time: Duration,
    /// Average execution time
    pub average_time: Duration,
    /// Maximum execution time
    pub max_time: Duration,
    /// Minimum execution time
    pub min_time: Duration,
}

impl FunctionStatistics {
    /// Create new function statistics
    #[must_use]
    pub fn new(function_name: &str) -> Self {
        Self {
            function_name: function_name.to_string(),
            call_count: 0,
            total_time: Duration::ZERO,
            average_time: Duration::ZERO,
            max_time: Duration::ZERO,
            min_time: Duration::ZERO,
        }
    }
}

/// CPU profiling configuration
#[derive(Debug, Clone)]
pub struct CPUProfilingConfig {
    /// Maximum samples to keep
    pub max_samples: usize,
    /// Sampling interval
    pub sampling_interval: Duration,
    /// Enable function-level profiling
    pub enable_function_profiling: bool,
}

impl Default for CPUProfilingConfig {
    fn default() -> Self {
        Self {
            max_samples: 1000,
            sampling_interval: Duration::from_millis(100),
            enable_function_profiling: true,
        }
    }
}

/// Memory profiler
#[derive(Debug, Clone, Default)]
pub struct MemoryProfiler {
    /// Memory samples
    pub samples: VecDeque<MemorySample>,
    /// Is active
    pub is_active: bool,
}

impl MemoryProfiler {
    /// Create a new memory profiler
    #[must_use]
    pub const fn new() -> Self {
        Self {
            samples: VecDeque::new(),
            is_active: false,
        }
    }

    /// Start memory profiling
    pub fn start(&mut self) {
        self.is_active = true;
        self.samples.clear();
    }

    /// Stop memory profiling
    pub fn stop(&mut self) {
        self.is_active = false;
    }

    /// Take a memory sample
    pub fn sample(&mut self) {
        if !self.is_active {
            return;
        }

        let sample = MemorySample {
            timestamp: Instant::now(),
            heap_usage: 0,
            stack_usage: 0,
            total_allocated: 0,
        };

        self.samples.push_back(sample);
    }
}

/// Memory sample
#[derive(Debug, Clone)]
pub struct MemorySample {
    /// Timestamp
    pub timestamp: Instant,
    /// Heap usage
    pub heap_usage: usize,
    /// Stack usage
    pub stack_usage: usize,
    /// Total allocated
    pub total_allocated: usize,
}

/// I/O profiler
#[derive(Debug, Clone, Default)]
pub struct IOProfiler {
    /// I/O samples
    pub samples: VecDeque<IOSample>,
    /// Is active
    pub is_active: bool,
}

impl IOProfiler {
    /// Create a new I/O profiler
    #[must_use]
    pub const fn new() -> Self {
        Self {
            samples: VecDeque::new(),
            is_active: false,
        }
    }

    /// Start I/O profiling
    pub fn start(&mut self) {
        self.is_active = true;
        self.samples.clear();
    }

    /// Stop I/O profiling
    pub fn stop(&mut self) {
        self.is_active = false;
    }

    /// Take an I/O sample
    pub fn sample(&mut self) {
        if !self.is_active {
            return;
        }

        let sample = IOSample {
            timestamp: Instant::now(),
            bytes_read: 0,
            bytes_written: 0,
            io_operations: 0,
        };

        self.samples.push_back(sample);
    }
}

/// I/O sample
#[derive(Debug, Clone)]
pub struct IOSample {
    /// Timestamp
    pub timestamp: Instant,
    /// Bytes read
    pub bytes_read: u64,
    /// Bytes written
    pub bytes_written: u64,
    /// I/O operations count
    pub io_operations: u64,
}

/// Performance metrics
#[derive(Debug, Clone, Default)]
pub struct PerformanceMetrics {
    /// Overall performance score
    pub performance_score: f64,
    /// CPU utilization
    pub cpu_utilization: f64,
    /// Memory utilization
    pub memory_utilization: f64,
    /// I/O throughput
    pub io_throughput: f64,
}

/// GPU accelerator for compute-intensive tasks
pub struct GPUAccelerator {
    /// Configuration
    pub config: GPUAccelerationConfig,
    /// Available GPU devices
    pub devices: Vec<GPUDevice>,
    /// GPU memory manager
    pub memory_manager: GPUMemoryManager,
    /// Kernel registry
    pub kernel_registry: KernelRegistry,
}

impl GPUAccelerator {
    /// Create a new GPU accelerator
    #[must_use]
    pub fn new(config: GPUAccelerationConfig) -> Self {
        Self {
            config,
            devices: Vec::new(),
            memory_manager: GPUMemoryManager::new(),
            kernel_registry: KernelRegistry::new(),
        }
    }

    /// Check if GPU is available
    #[must_use]
    pub fn is_available(&self) -> bool {
        self.config.enable_gpu && !self.devices.is_empty()
    }

    /// Get available GPU count
    #[must_use]
    pub fn device_count(&self) -> usize {
        self.devices.len()
    }

    /// Get device by ID
    #[must_use]
    pub fn get_device(&self, device_id: usize) -> Option<&GPUDevice> {
        self.devices.iter().find(|d| d.device_id == device_id)
    }

    /// Detect available GPU devices
    pub fn detect_devices(&mut self) {
        // In a real implementation, this would use CUDA/OpenCL to detect devices
        // For now, this is a placeholder
        self.devices.clear();
    }
}

/// GPU device representation
#[derive(Debug)]
pub struct GPUDevice {
    /// Device identifier
    pub device_id: usize,
    /// Device name
    pub device_name: String,
    /// Compute capability
    pub compute_capability: (u32, u32),
    /// Total memory
    pub total_memory: usize,
    /// Available memory
    pub available_memory: usize,
    /// Device status
    pub status: GPUDeviceStatus,
}

impl GPUDevice {
    /// Create a new GPU device
    #[must_use]
    pub fn new(device_id: usize, device_name: String) -> Self {
        Self {
            device_id,
            device_name,
            compute_capability: (0, 0),
            total_memory: 0,
            available_memory: 0,
            status: GPUDeviceStatus::Available,
        }
    }

    /// Check if device is available
    #[must_use]
    pub fn is_available(&self) -> bool {
        self.status == GPUDeviceStatus::Available
    }

    /// Get memory utilization
    #[must_use]
    pub fn memory_utilization(&self) -> f64 {
        if self.total_memory == 0 {
            return 0.0;
        }
        (self.total_memory - self.available_memory) as f64 / self.total_memory as f64
    }
}

/// GPU device status
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum GPUDeviceStatus {
    /// Device is available
    Available,
    /// Device is busy
    Busy,
    /// Device has an error
    Error,
    /// Device is not supported
    Unsupported,
}

/// GPU memory manager
#[derive(Debug, Clone, Default)]
pub struct GPUMemoryManager {
    /// Allocated buffers
    pub allocated_buffers: HashMap<String, GPUBuffer>,
    /// Total allocated
    pub total_allocated: usize,
}

impl GPUMemoryManager {
    /// Create a new GPU memory manager
    #[must_use]
    pub fn new() -> Self {
        Self {
            allocated_buffers: HashMap::new(),
            total_allocated: 0,
        }
    }

    /// Allocate a buffer
    pub fn allocate(&mut self, name: &str, size: usize) -> Result<(), String> {
        let buffer = GPUBuffer {
            name: name.to_string(),
            size,
            device_ptr: 0, // Placeholder
        };

        self.allocated_buffers.insert(name.to_string(), buffer);
        self.total_allocated += size;
        Ok(())
    }

    /// Free a buffer
    pub fn free(&mut self, name: &str) -> Result<(), String> {
        if let Some(buffer) = self.allocated_buffers.remove(name) {
            self.total_allocated = self.total_allocated.saturating_sub(buffer.size);
            Ok(())
        } else {
            Err(format!("Buffer {name} not found"))
        }
    }
}

/// GPU buffer
#[derive(Debug, Clone)]
pub struct GPUBuffer {
    /// Buffer name
    pub name: String,
    /// Buffer size
    pub size: usize,
    /// Device pointer
    pub device_ptr: usize,
}

/// Kernel registry for GPU compute kernels
#[derive(Debug, Clone, Default)]
pub struct KernelRegistry {
    /// Registered kernels
    pub kernels: HashMap<String, GPUKernel>,
}

impl KernelRegistry {
    /// Create a new kernel registry
    #[must_use]
    pub fn new() -> Self {
        Self {
            kernels: HashMap::new(),
        }
    }

    /// Register a kernel
    pub fn register(&mut self, name: &str, kernel: GPUKernel) {
        self.kernels.insert(name.to_string(), kernel);
    }

    /// Get a kernel
    #[must_use]
    pub fn get(&self, name: &str) -> Option<&GPUKernel> {
        self.kernels.get(name)
    }
}

/// GPU compute kernel
#[derive(Debug, Clone)]
pub struct GPUKernel {
    /// Kernel name
    pub name: String,
    /// Block size
    pub block_size: usize,
    /// Grid size
    pub grid_size: usize,
    /// Shared memory size
    pub shared_memory: usize,
}

impl GPUKernel {
    /// Create a new GPU kernel
    #[must_use]
    pub fn new(name: &str) -> Self {
        Self {
            name: name.to_string(),
            block_size: 256,
            grid_size: 1024,
            shared_memory: 0,
        }
    }
}