rustorch 0.6.29

//! Performance benchmark suite implementation
//! パフォーマンスベンチマークスイート実装

use super::{BenchmarkConfig, BenchmarkResult};
use crate::error::{RusTorchError, RusTorchResult};
use std::collections::HashMap;
use std::time::Instant;

/// Performance benchmark suite for GPU operations
/// GPU演算用パフォーマンスベンチマークスイート
pub struct PerformanceBenchmark {
    config: BenchmarkConfig,
    results: Vec<BenchmarkResult>,
}

impl PerformanceBenchmark {
    /// Create a new performance benchmark
    /// 新しいパフォーマンスベンチマークを作成
    pub fn new(config: BenchmarkConfig) -> Self {
        Self {
            config,
            results: Vec::new(),
        }
    }

    /// Run comprehensive benchmark suite
    /// 包括的ベンチマークスイートを実行
    pub fn run_comprehensive_benchmarks(&mut self) -> RusTorchResult<()> {
        println!("🚀 Starting Comprehensive GPU Performance Benchmarks");
        println!("=====================================================");

        // Element-wise operation benchmarks
        self.benchmark_elementwise_operations()?;

        // Matrix multiplication benchmarks
        self.benchmark_matrix_operations()?;

        // Reduction operation benchmarks
        self.benchmark_reduction_operations()?;

        // Neural network operation benchmarks
        self.benchmark_neural_network_operations()?;

        // Convolution operation benchmarks
        self.benchmark_convolution_operations()?;

        // Memory transfer benchmarks
        self.benchmark_memory_operations()?;

        self.print_comprehensive_report();
        Ok(())
    }

    /// Benchmark element-wise operations
    /// 要素ごと演算のベンチマーク
    fn benchmark_elementwise_operations(&mut self) -> RusTorchResult<()> {
        println!("\n📊 Benchmarking Element-wise Operations");
        println!("---------------------------------------");

        let sizes = vec![
            1024,     // 1K elements
            65536,    // 64K elements
            1048576,  // 1M elements
            16777216, // 16M elements
            67108864, // 64M elements
        ];

        for &size in &sizes {
            // Element-wise addition
            self.benchmark_elementwise_add(size)?;

            // Element-wise multiplication
            self.benchmark_elementwise_mul(size)?;

            // Element-wise division
            self.benchmark_elementwise_div(size)?;
        }

        Ok(())
    }

    /// Benchmark matrix operations
    /// 行列演算のベンチマーク
    fn benchmark_matrix_operations(&mut self) -> RusTorchResult<()> {
        println!("\n📊 Benchmarking Matrix Operations");
        println!("----------------------------------");

        let matrix_sizes = vec![
            (64, 64, 64),       // Small matrices
            (128, 128, 128),    // Medium matrices
            (256, 256, 256),    // Large matrices
            (512, 512, 512),    // Very large matrices
            (1024, 1024, 1024), // Huge matrices
            (2048, 2048, 2048), // Massive matrices
        ];

        for &(m, n, k) in &matrix_sizes {
            self.benchmark_matrix_multiplication(m, n, k)?;
        }

        // Specialized matrix operations
        self.benchmark_transpose_operations()?;

        Ok(())
    }

    /// Benchmark reduction operations
    /// リダクション演算のベンチマーク
    fn benchmark_reduction_operations(&mut self) -> RusTorchResult<()> {
        println!("\n📊 Benchmarking Reduction Operations");
        println!("------------------------------------");

        let sizes = vec![1024, 65536, 1048576, 16777216, 67108864];

        for &size in &sizes {
            self.benchmark_reduce_sum(size)?;
            self.benchmark_reduce_mean(size)?;
            self.benchmark_reduce_max(size)?;
        }

        Ok(())
    }

    /// Benchmark neural network operations
    /// ニューラルネットワーク演算のベンチマーク
    fn benchmark_neural_network_operations(&mut self) -> RusTorchResult<()> {
        println!("\n📊 Benchmarking Neural Network Operations");
        println!("----------------------------------------");

        let sizes = vec![1024, 65536, 1048576, 4194304];

        for &size in &sizes {
            self.benchmark_relu_activation(size)?;
            self.benchmark_gelu_activation(size)?;
            self.benchmark_softmax(size)?;
            self.benchmark_batch_normalization(size)?;
        }

        Ok(())
    }

    /// Benchmark convolution operations
    /// 畳み込み演算のベンチマーク
    fn benchmark_convolution_operations(&mut self) -> RusTorchResult<()> {
        println!("\n📊 Benchmarking Convolution Operations");
        println!("--------------------------------------");

        let conv_configs = vec![
            (32, 32, 3, 3),   // Small feature maps
            (64, 64, 3, 3),   // Medium feature maps
            (128, 128, 3, 3), // Large feature maps
            (256, 256, 5, 5), // Large kernels
            (512, 512, 7, 7), // Very large
        ];

        for &(input_h, input_w, kernel_h, kernel_w) in &conv_configs {
            self.benchmark_conv2d(input_h, input_w, kernel_h, kernel_w)?;
            self.benchmark_max_pool2d(input_h, input_w)?;
        }

        Ok(())
    }

    /// Benchmark memory operations
    /// メモリ演算のベンチマーク
    fn benchmark_memory_operations(&mut self) -> RusTorchResult<()> {
        println!("\n📊 Benchmarking Memory Operations");
        println!("----------------------------------");

        let sizes_mb = vec![1, 4, 16, 64, 256, 1024]; // Memory sizes in MB

        for &size_mb in &sizes_mb {
            let size_bytes = size_mb * 1024 * 1024;
            let size_elements = size_bytes / 4; // f32 elements

            self.benchmark_host_to_device_transfer(size_elements)?;
            self.benchmark_device_to_host_transfer(size_elements)?;
            self.benchmark_device_to_device_copy(size_elements)?;
        }

        Ok(())
    }

    // Individual benchmark implementations
    fn benchmark_elementwise_add(&mut self, size: usize) -> RusTorchResult<()> {
        let problem_size = format!("{} elements", size);
        let mut result = BenchmarkResult::new(
            "Element-wise Add".to_string(),
            "GPU".to_string(),
            problem_size,
        )
        .with_flops(size as u64)
        .with_memory_bytes((size * 3 * 4) as u64); // 3 f32 arrays

        let a = vec![1.5f32; size];
        let b = vec![2.5f32; size];

        // CPU benchmark
        let cpu_time = self.benchmark_cpu_operation(|| {
            let mut c = vec![0.0f32; size];
            for i in 0..size {
                c[i] = a[i] + b[i];
            }
            c
        })?;

        result = result.with_cpu_timing(cpu_time, self.config.measurement_iterations);

        // GPU benchmark (if available)
        #[cfg(feature = "cuda")]
        {
            if let Ok(gpu_time) = self.benchmark_gpu_elementwise_add(&a, &b, size) {
                result = result.with_gpu_timing(gpu_time, self.config.measurement_iterations);
            }
        }

        self.results.push(result);
        Ok(())
    }

    fn benchmark_cpu_operation<F, R>(&self, op: F) -> RusTorchResult<f64>
    where
        F: Fn() -> R,
    {
        // Warmup
        for _ in 0..self.config.warmup_iterations {
            let _ = op();
        }

        let start = Instant::now();
        for _ in 0..self.config.measurement_iterations {
            let _ = op();
        }
        let elapsed = start.elapsed();

        Ok(elapsed.as_millis() as f64)
    }

    #[cfg(feature = "cuda")]
    fn benchmark_gpu_elementwise_add(
        &self,
        a: &[f32],
        b: &[f32],
        size: usize,
    ) -> RusTorchResult<f64> {
        use crate::gpu::cuda_kernels::CudaKernelExecutor;

        let executor = CudaKernelExecutor::new(0)?;

        // Warmup
        for _ in 0..self.config.warmup_iterations {
            let mut c = vec![0.0f32; size];
            let _ = executor.elementwise_add_f32(a, b, &mut c)?;
        }

        let start = Instant::now();
        for _ in 0..self.config.measurement_iterations {
            let mut c = vec![0.0f32; size];
            let _ = executor.elementwise_add_f32(a, b, &mut c)?;
        }
        let elapsed = start.elapsed();

        Ok(elapsed.as_millis() as f64)
    }

    fn benchmark_elementwise_mul(&mut self, size: usize) -> RusTorchResult<()> {
        // Similar implementation for multiplication
        Ok(())
    }

    fn benchmark_elementwise_div(&mut self, size: usize) -> RusTorchResult<()> {
        // Similar implementation for division
        Ok(())
    }

    fn benchmark_matrix_multiplication(
        &mut self,
        m: usize,
        n: usize,
        k: usize,
    ) -> RusTorchResult<()> {
        // Matrix multiplication benchmark implementation
        Ok(())
    }

    fn benchmark_transpose_operations(&mut self) -> RusTorchResult<()> {
        // Transpose operations benchmark
        Ok(())
    }

    fn benchmark_reduce_sum(&mut self, size: usize) -> RusTorchResult<()> {
        // Reduction sum benchmark
        Ok(())
    }

    fn benchmark_reduce_mean(&mut self, size: usize) -> RusTorchResult<()> {
        // Reduction mean benchmark
        Ok(())
    }

    fn benchmark_reduce_max(&mut self, size: usize) -> RusTorchResult<()> {
        // Reduction max benchmark
        Ok(())
    }

    fn benchmark_relu_activation(&mut self, size: usize) -> RusTorchResult<()> {
        // ReLU activation benchmark
        Ok(())
    }

    fn benchmark_gelu_activation(&mut self, size: usize) -> RusTorchResult<()> {
        // GELU activation benchmark
        Ok(())
    }

    fn benchmark_softmax(&mut self, size: usize) -> RusTorchResult<()> {
        // Softmax benchmark
        Ok(())
    }

    fn benchmark_batch_normalization(&mut self, size: usize) -> RusTorchResult<()> {
        // Batch normalization benchmark
        Ok(())
    }

    fn benchmark_conv2d(
        &mut self,
        input_h: usize,
        input_w: usize,
        kernel_h: usize,
        kernel_w: usize,
    ) -> RusTorchResult<()> {
        // 2D convolution benchmark
        Ok(())
    }

    fn benchmark_max_pool2d(&mut self, input_h: usize, input_w: usize) -> RusTorchResult<()> {
        // Max pooling benchmark
        Ok(())
    }

    fn benchmark_host_to_device_transfer(&mut self, size: usize) -> RusTorchResult<()> {
        // Host to device transfer benchmark
        Ok(())
    }

    fn benchmark_device_to_host_transfer(&mut self, size: usize) -> RusTorchResult<()> {
        // Device to host transfer benchmark
        Ok(())
    }

    fn benchmark_device_to_device_copy(&mut self, size: usize) -> RusTorchResult<()> {
        // Device to device copy benchmark
        Ok(())
    }

    /// Print comprehensive benchmark report
    /// 包括的ベンチマークレポートを出力
    fn print_comprehensive_report(&self) {
        println!("\n📊 Comprehensive Performance Report");
        println!("====================================");

        for result in &self.results {
            println!(
                "{}: {} on {}",
                result.operation_name, result.problem_size, result.device_name
            );

            if let Some(speedup) = result.speedup {
                println!("  Speedup: {:.2}x", speedup);
            }

            if let Some(throughput) = result.gpu_throughput_gops {
                println!("  Throughput: {:.2} GOPS", throughput);
            }
        }
    }

    /// Get benchmark results
    /// ベンチマーク結果を取得
    pub fn results(&self) -> &[BenchmarkResult] {
        &self.results
    }

    /// Clear all results
    /// すべての結果をクリア
    pub fn clear_results(&mut self) {
        self.results.clear();
    }
}

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