torsh_sparse/performance_tools/optimization/

hardware.rs

//! Hardware benchmarking and system analysis

use crate::TorshResult;
use std::collections::HashMap;
use std::time::Instant;

/// Hardware-specific performance benchmark and system analysis
#[derive(Debug)]
pub struct HardwareBenchmark {
    /// System information
    system_info: SystemInfo,
    /// Benchmark results cache
    benchmark_cache: HashMap<String, f64>,
    /// Hardware feature detection results
    feature_support: HashMap<String, bool>,
}

/// Comprehensive system information
#[derive(Debug, Clone)]
pub struct SystemInfo {
    /// CPU information
    pub cpu_info: CpuInfo,
    /// Memory information
    pub memory_info: MemoryInfo,
    /// Cache hierarchy information
    pub cache_info: CacheInfo,
    /// Detected hardware features
    pub hardware_features: Vec<String>,
    /// Operating system information
    pub os_info: String,
}

/// CPU-specific information
#[derive(Debug, Clone)]
pub struct CpuInfo {
    /// Number of physical cores
    pub physical_cores: usize,
    /// Number of logical cores (with hyperthreading)
    pub logical_cores: usize,
    /// CPU frequency in MHz
    pub base_frequency_mhz: f64,
    /// CPU architecture
    pub architecture: String,
    /// Supported instruction sets
    pub instruction_sets: Vec<String>,
}

/// Memory hierarchy information
#[derive(Debug, Clone)]
pub struct MemoryInfo {
    /// Total system memory in bytes
    pub total_memory: usize,
    /// Available memory in bytes
    pub available_memory: usize,
    /// Memory bandwidth in GB/s (estimated)
    pub memory_bandwidth_gbps: f64,
}

/// Cache hierarchy information
#[derive(Debug, Clone)]
pub struct CacheInfo {
    /// L1 cache size per core in bytes
    pub l1_cache_size: usize,
    /// L2 cache size per core in bytes
    pub l2_cache_size: usize,
    /// L3 cache size total in bytes
    pub l3_cache_size: usize,
    /// Cache line size in bytes
    pub cache_line_size: usize,
}

/// System capability analysis report
#[derive(Debug, Clone)]
pub struct SystemCapabilityReport {
    /// System information summary
    pub system_info: SystemInfo,
    /// Capability scores for different aspects
    pub capability_scores: HashMap<String, f64>,
    /// Hardware-specific optimization recommendations
    pub recommendations: Vec<String>,
    /// When the benchmark was performed
    pub benchmark_timestamp: std::time::SystemTime,
}

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

impl HardwareBenchmark {
    /// Create a new hardware benchmark analyzer
    pub fn new() -> Self {
        let system_info = SystemInfo::detect();
        Self {
            system_info,
            benchmark_cache: HashMap::new(),
            feature_support: HashMap::new(),
        }
    }

    /// Analyze system capabilities for sparse tensor operations
    pub fn analyze_system_capabilities(&mut self) -> TorshResult<SystemCapabilityReport> {
        let mut capabilities = HashMap::new();

        // Benchmark core computational capabilities
        capabilities.insert(
            "cpu_compute_score".to_string(),
            self.benchmark_cpu_compute()?,
        );
        capabilities.insert(
            "memory_bandwidth_score".to_string(),
            self.benchmark_memory_bandwidth()?,
        );
        capabilities.insert(
            "cache_efficiency_score".to_string(),
            self.benchmark_cache_efficiency()?,
        );

        // Detect and benchmark hardware-specific features
        if self.detect_simd_support() {
            capabilities.insert(
                "simd_acceleration_score".to_string(),
                self.benchmark_simd_performance()?,
            );
        }

        if self.detect_numa_support() {
            capabilities.insert(
                "numa_efficiency_score".to_string(),
                self.benchmark_numa_performance()?,
            );
        }

        // Generate optimization recommendations
        let recommendations = self.generate_hardware_recommendations(&capabilities);

        Ok(SystemCapabilityReport {
            system_info: self.system_info.clone(),
            capability_scores: capabilities,
            recommendations,
            benchmark_timestamp: std::time::SystemTime::now(),
        })
    }

    /// Benchmark CPU computational performance
    pub fn benchmark_cpu_compute(&mut self) -> TorshResult<f64> {
        let cache_key = "cpu_compute".to_string();
        if let Some(&cached_score) = self.benchmark_cache.get(&cache_key) {
            return Ok(cached_score);
        }

        let start = Instant::now();
        let iterations = 1_000_000;

        // Perform computational work representative of sparse operations
        let mut sum = 0.0;
        for i in 0..iterations {
            sum += (i as f64).sqrt().sin().cos();
        }

        let duration = start.elapsed();
        let score = (iterations as f64 / duration.as_secs_f64()) / 1_000_000.0; // Normalize to millions of ops per second

        // Prevent optimization
        std::hint::black_box(sum);

        self.benchmark_cache.insert(cache_key, score);
        Ok(score)
    }

    /// Benchmark memory bandwidth
    pub fn benchmark_memory_bandwidth(&mut self) -> TorshResult<f64> {
        let cache_key = "memory_bandwidth".to_string();
        if let Some(&cached_score) = self.benchmark_cache.get(&cache_key) {
            return Ok(cached_score);
        }

        let size = 10_000_000; // 10M elements
        let data: Vec<f64> = (0..size).map(|i| i as f64).collect();

        let start = Instant::now();
        let sum: f64 = data.iter().sum();
        let duration = start.elapsed();

        let bytes_processed = size * std::mem::size_of::<f64>();
        let bandwidth_gbps =
            (bytes_processed as f64 / duration.as_secs_f64()) / (1024.0 * 1024.0 * 1024.0);

        // Prevent optimization
        std::hint::black_box(sum);

        self.benchmark_cache.insert(cache_key, bandwidth_gbps);
        Ok(bandwidth_gbps)
    }

    /// Benchmark cache efficiency
    pub fn benchmark_cache_efficiency(&mut self) -> TorshResult<f64> {
        let cache_key = "cache_efficiency".to_string();
        if let Some(&cached_score) = self.benchmark_cache.get(&cache_key) {
            return Ok(cached_score);
        }

        let size = 1_000_000;
        let data: Vec<f64> = vec![1.0; size];

        // Sequential access (cache-friendly)
        let start_sequential = Instant::now();
        let mut sum_sequential = 0.0;
        for &value in &data {
            sum_sequential += value;
        }
        let sequential_time = start_sequential.elapsed();

        // Random access (cache-unfriendly)
        let start_random = Instant::now();
        let mut sum_random = 0.0;
        for i in 0..size {
            let index = (i * 7) % size; // Simple pseudo-random pattern
            sum_random += data[index];
        }
        let random_time = start_random.elapsed();

        // Cache efficiency score: ratio of random to sequential performance
        let efficiency_score = sequential_time.as_secs_f64() / random_time.as_secs_f64();

        // Prevent optimization
        std::hint::black_box((sum_sequential, sum_random));

        self.benchmark_cache.insert(cache_key, efficiency_score);
        Ok(efficiency_score)
    }

    /// Detect SIMD support
    fn detect_simd_support(&mut self) -> bool {
        let feature = "simd_support".to_string();
        if let Some(&cached) = self.feature_support.get(&feature) {
            return cached;
        }

        // In a real implementation, this would check CPU features
        let supported = cfg!(target_feature = "sse2")
            || cfg!(target_feature = "avx")
            || cfg!(target_feature = "neon");
        self.feature_support.insert(feature, supported);
        supported
    }

    /// Detect NUMA support
    fn detect_numa_support(&mut self) -> bool {
        let feature = "numa_support".to_string();
        if let Some(&cached) = self.feature_support.get(&feature) {
            return cached;
        }

        // Simplified NUMA detection - would use system calls in practice
        let supported = self.system_info.cpu_info.physical_cores > 4;
        self.feature_support.insert(feature, supported);
        supported
    }

    /// Benchmark SIMD performance
    fn benchmark_simd_performance(&mut self) -> TorshResult<f64> {
        // Simplified SIMD benchmark - would use actual SIMD intrinsics
        let cache_key = "simd_performance".to_string();
        if let Some(&cached_score) = self.benchmark_cache.get(&cache_key) {
            return Ok(cached_score);
        }

        let score = if self.detect_simd_support() {
            0.8 // Assume good SIMD performance
        } else {
            0.2 // Poor SIMD performance
        };

        self.benchmark_cache.insert(cache_key, score);
        Ok(score)
    }

    /// Benchmark NUMA performance
    fn benchmark_numa_performance(&mut self) -> TorshResult<f64> {
        // Simplified NUMA benchmark
        let cache_key = "numa_performance".to_string();
        if let Some(&cached_score) = self.benchmark_cache.get(&cache_key) {
            return Ok(cached_score);
        }

        let score = if self.detect_numa_support() {
            0.7 // Assume reasonable NUMA performance
        } else {
            0.9 // Single NUMA node, no penalty
        };

        self.benchmark_cache.insert(cache_key, score);
        Ok(score)
    }

    /// Generate hardware-specific optimization recommendations
    fn generate_hardware_recommendations(
        &self,
        capabilities: &HashMap<String, f64>,
    ) -> Vec<String> {
        let mut recommendations = Vec::new();

        // CPU recommendations
        if let Some(&cpu_score) = capabilities.get("cpu_compute_score") {
            if cpu_score > 2.0 {
                recommendations.push(
                    "High CPU performance detected - consider CPU-intensive algorithms".to_string(),
                );
            } else if cpu_score < 0.5 {
                recommendations.push(
                    "Limited CPU performance - prefer memory-efficient algorithms".to_string(),
                );
            }
        }

        // Memory recommendations
        if let Some(&memory_score) = capabilities.get("memory_bandwidth_score") {
            if memory_score > 10.0 {
                recommendations.push(
                    "High memory bandwidth available - streaming algorithms recommended"
                        .to_string(),
                );
            } else if memory_score < 2.0 {
                recommendations
                    .push("Limited memory bandwidth - minimize memory access patterns".to_string());
            }
        }

        // Cache recommendations
        if let Some(&cache_score) = capabilities.get("cache_efficiency_score") {
            if cache_score > 0.8 {
                recommendations
                    .push("Excellent cache performance - leverage block algorithms".to_string());
            } else if cache_score < 0.3 {
                recommendations.push(
                    "Poor cache performance - consider cache-oblivious algorithms".to_string(),
                );
            }
        }

        // SIMD recommendations
        if capabilities.contains_key("simd_acceleration_score") {
            recommendations
                .push("SIMD support detected - enable vectorized operations".to_string());
        }

        // NUMA recommendations
        if capabilities.contains_key("numa_efficiency_score") {
            recommendations
                .push("NUMA system detected - consider thread affinity optimization".to_string());
        }

        recommendations
    }
}

impl SystemInfo {
    /// Detect system information
    pub fn detect() -> Self {
        Self {
            cpu_info: CpuInfo::detect(),
            memory_info: MemoryInfo::detect(),
            cache_info: CacheInfo::detect(),
            hardware_features: Self::detect_hardware_features(),
            os_info: Self::detect_os_info(),
        }
    }

    /// Detect hardware features
    fn detect_hardware_features() -> Vec<String> {
        #[allow(unused_mut)]
        let mut features = Vec::new();

        // In a real implementation, this would check actual CPU features
        #[cfg(target_feature = "sse2")]
        features.push("SSE2".to_string());

        #[cfg(target_feature = "avx")]
        features.push("AVX".to_string());

        #[cfg(target_feature = "avx2")]
        features.push("AVX2".to_string());

        #[cfg(target_feature = "fma")]
        features.push("FMA".to_string());

        features
    }

    /// Detect operating system information
    fn detect_os_info() -> String {
        format!("{}", std::env::consts::OS)
    }
}

impl CpuInfo {
    /// Detect CPU information
    pub fn detect() -> Self {
        // In a real implementation, this would query actual system information
        Self {
            physical_cores: 4,          // Placeholder - would use actual detection
            logical_cores: 8,           // Placeholder - would use actual detection
            base_frequency_mhz: 2400.0, // Placeholder
            architecture: std::env::consts::ARCH.to_string(),
            instruction_sets: vec!["x86_64".to_string()], // Placeholder
        }
    }
}

impl MemoryInfo {
    /// Detect memory information
    pub fn detect() -> Self {
        // Simplified memory detection - would use system calls in practice
        Self {
            total_memory: 16 * 1024 * 1024 * 1024,    // 16GB placeholder
            available_memory: 8 * 1024 * 1024 * 1024, // 8GB placeholder
            memory_bandwidth_gbps: 25.6,              // Placeholder
        }
    }
}

impl CacheInfo {
    /// Detect cache information
    pub fn detect() -> Self {
        // Simplified cache detection - would use CPU identification in practice
        Self {
            l1_cache_size: 32 * 1024,       // 32KB L1
            l2_cache_size: 256 * 1024,      // 256KB L2
            l3_cache_size: 8 * 1024 * 1024, // 8MB L3
            cache_line_size: 64,            // 64 bytes
        }
    }
}