Struct GpuContext 

pub struct GpuContext { /* private fields */ }

GPU context for managing device operations

Implementations

impl GpuContext

pub fn new(config: GpuConfig) -> Result<Self>

Create a new GPU context

Examples found in repository

examples/advanced_showcase.rs (line 140)

fn demonstrate_advanced_gpu_optimization() -> Result<(), Box<dyn std::error::Error>> {
    println!("\n⚡ Advanced-GPU Optimization Demonstration");
    println!("=====================================");

    // Create GPU context (falls back to CPU if no GPU available)
    println!("🔧 Initializing GPU context...");
    let gpu_config = GpuConfig {
        backend: GpuBackend::Cpu,
        ..Default::default()
    };
    let gpu_context = GpuContext::new(gpu_config)?; // Using CPU backend for demo
    println!("   Backend: {:?}", gpu_context.backend());

    // Create advanced-GPU optimizer
    let optimizer = AdvancedGpuOptimizer::new()
        .with_adaptive_kernels(true)
        .with_memory_prefetch(true)
        .with_multi_gpu(false) // Single GPU for demo
        .with_auto_tuning(true);

    // Generate advanced-optimized matrix
    println!("🔥 Generating advanced-optimized matrix...");
    let start_time = Instant::now();
    let matrix = optimizer.generate_advanced_optimized_matrix(
        &gpu_context,
        500,      // rows
        200,      // cols
        "normal", // distribution
    )?;
    let generation_time = start_time.elapsed();

    println!(
        "   Generated {}x{} matrix in: {:?}",
        matrix.nrows(),
        matrix.ncols(),
        generation_time
    );
    let matrix_mean = matrix.clone().mean();
    let matrix_std = matrix.var(1.0).sqrt();
    println!(
        "   Matrix stats: mean={:.3}, std={:.3}",
        matrix_mean, matrix_std
    );

    // Benchmark performance
    println!("📊 Running performance benchmarks...");
    let datashapes = vec![(100, 50), (500, 200), (1000, 500)];
    let benchmark_results =
        optimizer.benchmark_performance(&gpu_context, "matrix_generation", &datashapes)?;

    println!("   Benchmark Results:");
    println!(
        "     Best Speedup: {:.2}x",
        benchmark_results.best_speedup()
    );
    println!(
        "     Average Speedup: {:.2}x",
        benchmark_results.average_speedup()
    );
    println!(
        "     Total Memory Usage: {:.1} MB",
        benchmark_results.total_memory_usage()
    );

    Ok(())
}

examples/gpu_acceleration.rs (line 172)

fn demonstrate_backend_comparison() -> Result<(), Box<dyn std::error::Error>> {
    println!("⚡ GPU BACKEND COMPARISON");
    println!("{}", "-".repeat(40));

    let testsize = 50_000;
    let features = 20;

    println!("Comparing backends for {testsize} samples with {features} features:");

    // Test different backends
    let backends = vec![
        ("CPU Fallback", GpuBackend::Cpu),
        ("CUDA", GpuBackend::Cuda { device_id: 0 }),
        (
            "OpenCL",
            GpuBackend::OpenCl {
                platform_id: 0,
                device_id: 0,
            },
        ),
    ];

    let mut results: HashMap<String, std::time::Duration> = HashMap::new();

    for (name, backend) in backends {
        println!("\nTesting {name}:");

        let config = GpuConfig {
            backend: backend.clone(),
            threads_per_block: 256,
            enable_double_precision: true,
            ..Default::default()
        };

        match GpuContext::new(config) {
            Ok(context) => {
                if context.is_available() {
                    // Test classification generation
                    let start = Instant::now();
                    let dataset =
                        context.make_classification_gpu(testsize, features, 5, 2, 15, Some(42))?;
                    let duration = start.elapsed();

                    results.insert(name.to_string(), duration);

                    println!(
                        "  ✅ Classification: {} samples in {:.2}ms",
                        dataset.n_samples(),
                        duration.as_millis()
                    );
                    println!(
                        "  📊 Throughput: {:.1} samples/s",
                        dataset.n_samples() as f64 / duration.as_secs_f64()
                    );
                } else {
                    println!("  ❌ Backend not available");
                }
            }
            Err(e) => {
                println!("  ❌ Error: {e}");
            }
        }
    }

    // Calculate speedups
    if let Some(cpu_time) = results.get("CPU Fallback") {
        println!("\nSpeedup Analysis:");
        for (backend, gpu_time) in &results {
            if backend != "CPU Fallback" {
                let speedup = cpu_time.as_secs_f64() / gpu_time.as_secs_f64();
                println!("  {backend}: {speedup:.1}x faster than CPU");
            }
        }
    }

    println!();
    Ok(())
}

#[allow(dead_code)]
fn demonstrate_performance_benchmarks() -> Result<(), Box<dyn std::error::Error>> {
    println!("📊 PERFORMANCE BENCHMARKS");
    println!("{}", "-".repeat(40));

    let config = get_optimal_gpu_config();
    let benchmark = GpuBenchmark::new(config)?;

    println!("Running data generation benchmarks...");
    let data_results = benchmark.benchmark_data_generation()?;
    data_results.print_results();

    println!("\nRunning matrix operation benchmarks...");
    let matrix_results = benchmark.benchmark_matrix_operations()?;
    matrix_results.print_results();

    // Compare with CPU baseline
    println!("\nCPU vs GPU Comparison:");
    demonstrate_cpu_gpu_comparison()?;

    println!();
    Ok(())
}

#[allow(dead_code)]
fn demonstrate_cpu_gpu_comparison() -> Result<(), Box<dyn std::error::Error>> {
    let dataset_sizes = vec![10_000, 50_000, 100_000];

    println!(
        "{:<12} {:<15} {:<15} {:<10}",
        "Size", "CPU Time", "GPU Time", "Speedup"
    );
    println!("{}", "-".repeat(55));

    for &size in &dataset_sizes {
        // CPU benchmark
        let cpu_start = Instant::now();
        let _cpudataset = make_classification(size, 20, 5, 2, 15, Some(42))?;
        let cpu_time = cpu_start.elapsed();

        // GPU benchmark
        let gpu_start = Instant::now();
        let _gpudataset = make_classification_auto_gpu(size, 20, 5, 2, 15, Some(42))?;
        let gpu_time = gpu_start.elapsed();

        let speedup = cpu_time.as_secs_f64() / gpu_time.as_secs_f64();

        println!(
            "{:<12} {:<15} {:<15} {:<10.1}x",
            size,
            format!("{:.1}ms", cpu_time.as_millis()),
            format!("{:.1}ms", gpu_time.as_millis()),
            speedup
        );
    }

    Ok(())
}

#[allow(dead_code)]
fn demonstrate_memory_management() -> Result<(), Box<dyn std::error::Error>> {
    println!("💾 GPU MEMORY MANAGEMENT");
    println!("{}", "-".repeat(40));

    // Configure memory-constrained GPU context
    let memory_config = GpuMemoryConfig {
        max_memory_mb: Some(512),  // Limit to 512MB
        pool_size_mb: 256,         // 256MB pool
        enable_coalescing: true,   // Enable memory coalescing
        use_unified_memory: false, // Don't use unified memory
    };

    let gpu_config = GpuConfig {
        backend: get_optimal_gpu_config().backend,
        memory: memory_config,
        threads_per_block: 256,
        ..Default::default()
    };

    println!("Memory Configuration:");
    println!(
        "  Max Memory: {} MB",
        gpu_config.memory.max_memory_mb.unwrap_or(0)
    );
    println!("  Pool Size: {} MB", gpu_config.memory.pool_size_mb);
    println!("  Coalescing: {}", gpu_config.memory.enable_coalescing);
    println!("  Unified Memory: {}", gpu_config.memory.use_unified_memory);

    let context = GpuContext::new(gpu_config)?;
    let device_info = context.device_info();

    println!("\nDevice Memory Info:");
    println!("  Total: {} MB", device_info.total_memory_mb);
    println!("  Available: {} MB", device_info.available_memory_mb);
    println!(
        "  Utilization: {:.1}%",
        (device_info.total_memory_mb - device_info.available_memory_mb) as f64
            / device_info.total_memory_mb as f64
            * 100.0
    );

    // Test memory-efficient generation
    println!("\nTesting memory-efficient dataset generation...");

    let sizes = vec![10_000, 25_000, 50_000];
    for &size in &sizes {
        let start = Instant::now();

        match context.make_regression_gpu(size, 50, 30, 0.1, Some(42)) {
            Ok(dataset) => {
                let duration = start.elapsed();
                let memory_estimate = dataset.n_samples() * dataset.n_features() * 8; // 8 bytes per f64

                println!(
                    "  {} samples: {:.1}ms (~{:.1} MB)",
                    size,
                    duration.as_millis(),
                    memory_estimate as f64 / (1024.0 * 1024.0)
                );
            }
            Err(e) => {
                println!("  {size} samples: Failed - {e}");
            }
        }
    }

    println!();
    Ok(())
}

pub fn device_info(&self) -> &GpuDeviceInfo

Get device information

Examples found in repository

examples/gpu_acceleration.rs (line 306)

fn demonstrate_memory_management() -> Result<(), Box<dyn std::error::Error>> {
    println!("💾 GPU MEMORY MANAGEMENT");
    println!("{}", "-".repeat(40));

    // Configure memory-constrained GPU context
    let memory_config = GpuMemoryConfig {
        max_memory_mb: Some(512),  // Limit to 512MB
        pool_size_mb: 256,         // 256MB pool
        enable_coalescing: true,   // Enable memory coalescing
        use_unified_memory: false, // Don't use unified memory
    };

    let gpu_config = GpuConfig {
        backend: get_optimal_gpu_config().backend,
        memory: memory_config,
        threads_per_block: 256,
        ..Default::default()
    };

    println!("Memory Configuration:");
    println!(
        "  Max Memory: {} MB",
        gpu_config.memory.max_memory_mb.unwrap_or(0)
    );
    println!("  Pool Size: {} MB", gpu_config.memory.pool_size_mb);
    println!("  Coalescing: {}", gpu_config.memory.enable_coalescing);
    println!("  Unified Memory: {}", gpu_config.memory.use_unified_memory);

    let context = GpuContext::new(gpu_config)?;
    let device_info = context.device_info();

    println!("\nDevice Memory Info:");
    println!("  Total: {} MB", device_info.total_memory_mb);
    println!("  Available: {} MB", device_info.available_memory_mb);
    println!(
        "  Utilization: {:.1}%",
        (device_info.total_memory_mb - device_info.available_memory_mb) as f64
            / device_info.total_memory_mb as f64
            * 100.0
    );

    // Test memory-efficient generation
    println!("\nTesting memory-efficient dataset generation...");

    let sizes = vec![10_000, 25_000, 50_000];
    for &size in &sizes {
        let start = Instant::now();

        match context.make_regression_gpu(size, 50, 30, 0.1, Some(42)) {
            Ok(dataset) => {
                let duration = start.elapsed();
                let memory_estimate = dataset.n_samples() * dataset.n_features() * 8; // 8 bytes per f64

                println!(
                    "  {} samples: {:.1}ms (~{:.1} MB)",
                    size,
                    duration.as_millis(),
                    memory_estimate as f64 / (1024.0 * 1024.0)
                );
            }
            Err(e) => {
                println!("  {size} samples: Failed - {e}");
            }
        }
    }

    println!();
    Ok(())
}

pub fn backend(&self) -> &GpuBackend

Get the backend type

Examples found in repository

examples/advanced_showcase.rs (line 141)

fn demonstrate_advanced_gpu_optimization() -> Result<(), Box<dyn std::error::Error>> {
    println!("\n⚡ Advanced-GPU Optimization Demonstration");
    println!("=====================================");

    // Create GPU context (falls back to CPU if no GPU available)
    println!("🔧 Initializing GPU context...");
    let gpu_config = GpuConfig {
        backend: GpuBackend::Cpu,
        ..Default::default()
    };
    let gpu_context = GpuContext::new(gpu_config)?; // Using CPU backend for demo
    println!("   Backend: {:?}", gpu_context.backend());

    // Create advanced-GPU optimizer
    let optimizer = AdvancedGpuOptimizer::new()
        .with_adaptive_kernels(true)
        .with_memory_prefetch(true)
        .with_multi_gpu(false) // Single GPU for demo
        .with_auto_tuning(true);

    // Generate advanced-optimized matrix
    println!("🔥 Generating advanced-optimized matrix...");
    let start_time = Instant::now();
    let matrix = optimizer.generate_advanced_optimized_matrix(
        &gpu_context,
        500,      // rows
        200,      // cols
        "normal", // distribution
    )?;
    let generation_time = start_time.elapsed();

    println!(
        "   Generated {}x{} matrix in: {:?}",
        matrix.nrows(),
        matrix.ncols(),
        generation_time
    );
    let matrix_mean = matrix.clone().mean();
    let matrix_std = matrix.var(1.0).sqrt();
    println!(
        "   Matrix stats: mean={:.3}, std={:.3}",
        matrix_mean, matrix_std
    );

    // Benchmark performance
    println!("📊 Running performance benchmarks...");
    let datashapes = vec![(100, 50), (500, 200), (1000, 500)];
    let benchmark_results =
        optimizer.benchmark_performance(&gpu_context, "matrix_generation", &datashapes)?;

    println!("   Benchmark Results:");
    println!(
        "     Best Speedup: {:.2}x",
        benchmark_results.best_speedup()
    );
    println!(
        "     Average Speedup: {:.2}x",
        benchmark_results.average_speedup()
    );
    println!(
        "     Total Memory Usage: {:.1} MB",
        benchmark_results.total_memory_usage()
    );

    Ok(())
}

pub fn is_available(&self) -> bool

Check if GPU is available and functional

Examples found in repository

examples/gpu_acceleration.rs (line 174)

fn demonstrate_backend_comparison() -> Result<(), Box<dyn std::error::Error>> {
    println!("⚡ GPU BACKEND COMPARISON");
    println!("{}", "-".repeat(40));

    let testsize = 50_000;
    let features = 20;

    println!("Comparing backends for {testsize} samples with {features} features:");

    // Test different backends
    let backends = vec![
        ("CPU Fallback", GpuBackend::Cpu),
        ("CUDA", GpuBackend::Cuda { device_id: 0 }),
        (
            "OpenCL",
            GpuBackend::OpenCl {
                platform_id: 0,
                device_id: 0,
            },
        ),
    ];

    let mut results: HashMap<String, std::time::Duration> = HashMap::new();

    for (name, backend) in backends {
        println!("\nTesting {name}:");

        let config = GpuConfig {
            backend: backend.clone(),
            threads_per_block: 256,
            enable_double_precision: true,
            ..Default::default()
        };

        match GpuContext::new(config) {
            Ok(context) => {
                if context.is_available() {
                    // Test classification generation
                    let start = Instant::now();
                    let dataset =
                        context.make_classification_gpu(testsize, features, 5, 2, 15, Some(42))?;
                    let duration = start.elapsed();

                    results.insert(name.to_string(), duration);

                    println!(
                        "  ✅ Classification: {} samples in {:.2}ms",
                        dataset.n_samples(),
                        duration.as_millis()
                    );
                    println!(
                        "  📊 Throughput: {:.1} samples/s",
                        dataset.n_samples() as f64 / duration.as_secs_f64()
                    );
                } else {
                    println!("  ❌ Backend not available");
                }
            }
            Err(e) => {
                println!("  ❌ Error: {e}");
            }
        }
    }

    // Calculate speedups
    if let Some(cpu_time) = results.get("CPU Fallback") {
        println!("\nSpeedup Analysis:");
        for (backend, gpu_time) in &results {
            if backend != "CPU Fallback" {
                let speedup = cpu_time.as_secs_f64() / gpu_time.as_secs_f64();
                println!("  {backend}: {speedup:.1}x faster than CPU");
            }
        }
    }

    println!();
    Ok(())
}

pub fn make_classification_gpu( &self, n_samples: usize, n_features: usize, n_classes: usize, n_clusters_per_class: usize, n_informative: usize, random_state: Option<u64>, ) -> Result<Dataset>

Generate classification dataset on GPU

Examples found in repository

examples/gpu_acceleration.rs (line 178)

fn demonstrate_backend_comparison() -> Result<(), Box<dyn std::error::Error>> {
    println!("⚡ GPU BACKEND COMPARISON");
    println!("{}", "-".repeat(40));

    let testsize = 50_000;
    let features = 20;

    println!("Comparing backends for {testsize} samples with {features} features:");

    // Test different backends
    let backends = vec![
        ("CPU Fallback", GpuBackend::Cpu),
        ("CUDA", GpuBackend::Cuda { device_id: 0 }),
        (
            "OpenCL",
            GpuBackend::OpenCl {
                platform_id: 0,
                device_id: 0,
            },
        ),
    ];

    let mut results: HashMap<String, std::time::Duration> = HashMap::new();

    for (name, backend) in backends {
        println!("\nTesting {name}:");

        let config = GpuConfig {
            backend: backend.clone(),
            threads_per_block: 256,
            enable_double_precision: true,
            ..Default::default()
        };

        match GpuContext::new(config) {
            Ok(context) => {
                if context.is_available() {
                    // Test classification generation
                    let start = Instant::now();
                    let dataset =
                        context.make_classification_gpu(testsize, features, 5, 2, 15, Some(42))?;
                    let duration = start.elapsed();

                    results.insert(name.to_string(), duration);

                    println!(
                        "  ✅ Classification: {} samples in {:.2}ms",
                        dataset.n_samples(),
                        duration.as_millis()
                    );
                    println!(
                        "  📊 Throughput: {:.1} samples/s",
                        dataset.n_samples() as f64 / duration.as_secs_f64()
                    );
                } else {
                    println!("  ❌ Backend not available");
                }
            }
            Err(e) => {
                println!("  ❌ Error: {e}");
            }
        }
    }

    // Calculate speedups
    if let Some(cpu_time) = results.get("CPU Fallback") {
        println!("\nSpeedup Analysis:");
        for (backend, gpu_time) in &results {
            if backend != "CPU Fallback" {
                let speedup = cpu_time.as_secs_f64() / gpu_time.as_secs_f64();
                println!("  {backend}: {speedup:.1}x faster than CPU");
            }
        }
    }

    println!();
    Ok(())
}

pub fn make_regression_gpu( &self, n_samples: usize, n_features: usize, n_informative: usize, noise: f64, random_state: Option<u64>, ) -> Result<Dataset>

Generate regression dataset on GPU

Examples found in repository

examples/gpu_acceleration.rs (line 325)

fn demonstrate_memory_management() -> Result<(), Box<dyn std::error::Error>> {
    println!("💾 GPU MEMORY MANAGEMENT");
    println!("{}", "-".repeat(40));

    // Configure memory-constrained GPU context
    let memory_config = GpuMemoryConfig {
        max_memory_mb: Some(512),  // Limit to 512MB
        pool_size_mb: 256,         // 256MB pool
        enable_coalescing: true,   // Enable memory coalescing
        use_unified_memory: false, // Don't use unified memory
    };

    let gpu_config = GpuConfig {
        backend: get_optimal_gpu_config().backend,
        memory: memory_config,
        threads_per_block: 256,
        ..Default::default()
    };

    println!("Memory Configuration:");
    println!(
        "  Max Memory: {} MB",
        gpu_config.memory.max_memory_mb.unwrap_or(0)
    );
    println!("  Pool Size: {} MB", gpu_config.memory.pool_size_mb);
    println!("  Coalescing: {}", gpu_config.memory.enable_coalescing);
    println!("  Unified Memory: {}", gpu_config.memory.use_unified_memory);

    let context = GpuContext::new(gpu_config)?;
    let device_info = context.device_info();

    println!("\nDevice Memory Info:");
    println!("  Total: {} MB", device_info.total_memory_mb);
    println!("  Available: {} MB", device_info.available_memory_mb);
    println!(
        "  Utilization: {:.1}%",
        (device_info.total_memory_mb - device_info.available_memory_mb) as f64
            / device_info.total_memory_mb as f64
            * 100.0
    );

    // Test memory-efficient generation
    println!("\nTesting memory-efficient dataset generation...");

    let sizes = vec![10_000, 25_000, 50_000];
    for &size in &sizes {
        let start = Instant::now();

        match context.make_regression_gpu(size, 50, 30, 0.1, Some(42)) {
            Ok(dataset) => {
                let duration = start.elapsed();
                let memory_estimate = dataset.n_samples() * dataset.n_features() * 8; // 8 bytes per f64

                println!(
                    "  {} samples: {:.1}ms (~{:.1} MB)",
                    size,
                    duration.as_millis(),
                    memory_estimate as f64 / (1024.0 * 1024.0)
                );
            }
            Err(e) => {
                println!("  {size} samples: Failed - {e}");
            }
        }
    }

    println!();
    Ok(())
}

pub fn make_blobs_gpu( &self, n_samples: usize, n_features: usize, n_centers: usize, cluster_std: f64, random_state: Option<u64>, ) -> Result<Dataset>

Generate clustering dataset (blobs) on GPU

pub fn gpu_matrix_multiply( &self, a: &Array2<f64>, b: &Array2<f64>, ) -> Result<Array2<f64>>

Perform matrix operations on GPU

pub fn gpu_elementwise_op<F>( &self, data: &Array2<f64>, op: F, ) -> Result<Array2<f64>>

where F: Fn(f64) -> f64 + Send + Sync,

Apply element-wise operations on GPU