Module optimization 

Optimization and performance enhancements for metrics computation

This module provides optimized implementations of metrics calculations for improved performance, memory efficiency, and numerical stability.

The optimization module contains four main components:

1. parallel - Tools for parallel computation of metrics
2. memory - Tools for memory-efficient metrics computation
3. numeric - Tools for numerically stable metrics computation
4. quantum_acceleration - Quantum-inspired algorithms for exponential speedups

Examples

Using StableMetrics for numerical stability

use scirs2_metrics::optimization::numeric::StableMetrics;
use scirs2_metrics::error::Result;

fn compute_kl_divergence(p: &[f64], q: &[f64]) -> Result<f64> {
    let stable = StableMetrics::<f64>::new()
        .with_epsilon(1e-10)
        .with_clip_values(true);
    stable.kl_divergence(p, q)
}

Using parallel computation for batch metrics

use scirs2_metrics::optimization::parallel::{ParallelConfig, compute_metrics_batch};
use scirs2_metrics::error::Result;
use scirs2_core::ndarray::Array1;

fn compute_multiple_metrics(y_true: &Array1<f64>, y_pred: &Array1<f64>) -> Result<Vec<f64>> {
    let config = ParallelConfig {
        parallel_enabled: true,
        min_chunk_size: 1000,
        num_threads: None,
    };

    let metrics: Vec<Box<dyn Fn(&Array1<f64>, &Array1<f64>) -> Result<f64> + Send + Sync>> = vec![
        // Define your metric functions here
    ];

    compute_metrics_batch(y_true, y_pred, &metrics, &config)
}

Using memory-efficient metrics for large datasets

use scirs2_metrics::optimization::memory::{StreamingMetric, ChunkedMetrics};
use scirs2_metrics::error::Result;

struct StreamingMeanAbsoluteError;

impl StreamingMetric<f64> for StreamingMeanAbsoluteError {
    type State = (f64, usize); // Running sum and count
     
    fn init_state(&self) -> Self::State {
        (0.0, 0)
    }
     
    fn update_state(&self, state: &mut Self::State, batch_true: &[f64], batch_pred: &[f64]) -> Result<()> {
        for (y_t, y_p) in batch_true.iter().zip(batch_pred.iter()) {
            state.0 += (y_t - y_p).abs();
            state.1 += 1;
        }
        Ok(())
    }
     
    fn finalize(&self, state: &Self::State) -> Result<f64> {
        if state.1 == 0 {
            return Err(scirs2_metrics::error::MetricsError::InvalidInput(
                "No data processed".to_string()
            ));
        }
        Ok(state.0 / state.1 as f64)
    }
}

Using quantum-inspired acceleration for large-scale computations

use scirs2_metrics::optimization::quantum_acceleration::{QuantumMetricsComputer, QuantumConfig};
use scirs2_metrics::error::Result;
use scirs2_core::ndarray::Array1;

fn compute_quantum_correlation(x: &Array1<f64>, y: &Array1<f64>) -> Result<f64> {
    let config = QuantumConfig::default();
    let mut quantum_computer = QuantumMetricsComputer::new(config)?;
    quantum_computer.quantum_correlation(&x.view(), &y.view())
}

Re-exports

pub use advanced_memory_optimization::AdvancedMemoryPool;

pub use advanced_memory_optimization::AllocationStrategy;

pub use advanced_memory_optimization::BlockType;

pub use advanced_memory_optimization::MemoryBlock;

pub use advanced_memory_optimization::MemoryPoolConfig;

pub use advanced_memory_optimization::MemoryStats;

pub use advanced_memory_optimization::StrategyBenchmark;

pub use distributed::DistributedConfig;

pub use distributed::DistributedMetricsBuilder;

pub use distributed::DistributedMetricsCoordinator;

pub use distributed_advanced::AdvancedClusterConfig;

pub use distributed_advanced::AdvancedDistributedCoordinator;

pub use distributed_advanced::AutoScalingConfig;

pub use distributed_advanced::ClusterState;

pub use distributed_advanced::ConsensusAlgorithm;

pub use distributed_advanced::ConsensusConfig;

pub use distributed_advanced::DistributedTask;

pub use distributed_advanced::FaultToleranceConfig;

pub use distributed_advanced::LocalityConfig;

pub use distributed_advanced::NodeInfo;

pub use distributed_advanced::NodeRole;

pub use distributed_advanced::NodeStatus;

pub use distributed_advanced::OptimizationConfig;

pub use distributed_advanced::ResourceRequirements;

pub use distributed_advanced::ShardingConfig;

pub use distributed_advanced::ShardingStrategy;

pub use distributed_advanced::TaskPriority;

pub use distributed_advanced::TaskType;

pub use gpu_acceleration::BenchmarkResults;

pub use gpu_acceleration::GpuAccelConfig;

pub use gpu_acceleration::GpuInfo;

pub use gpu_acceleration::GpuMetricsComputer;

pub use gpu_kernels::AdvancedGpuComputer;

pub use gpu_kernels::BatchSettings;

pub use gpu_kernels::CudaContext;

pub use gpu_kernels::ErrorHandling;

pub use gpu_kernels::GpuApi;

pub use gpu_kernels::GpuComputeConfig;

pub use gpu_kernels::GpuComputeResults;

pub use gpu_kernels::GpuPerformanceStats;

pub use gpu_kernels::KernelMetrics;

pub use gpu_kernels::KernelOptimization;

pub use gpu_kernels::MemoryStrategy;

pub use gpu_kernels::OpenClContext;

pub use gpu_kernels::TransferMetrics;

pub use gpu_kernels::VectorizationLevel;

pub use hardware::HardwareAccelConfig;

pub use hardware::HardwareAcceleratedMatrix;

pub use hardware::HardwareCapabilities;

pub use hardware::SimdDistanceMetrics;

pub use hardware::SimdStatistics;

pub use hardware::VectorWidth;

pub use memory::ChunkedMetrics;

pub use memory::StreamingMetric;

pub use numeric::StableMetric;

pub use numeric::StableMetrics;

pub use parallel::ParallelConfig;

pub use quantum_acceleration::InterferencePatterns;

pub use quantum_acceleration::QuantumBenchmarkResults;

pub use quantum_acceleration::QuantumConfig;

pub use quantum_acceleration::QuantumMetricsComputer;

pub use quantum_acceleration::QuantumProcessor;

pub use quantum_acceleration::SuperpositionManager;

pub use quantum_acceleration::VqeParameters;

pub use simd_gpu::SimdMetrics;

Modules

advanced_memory_optimization

Advanced memory optimization for GPU acceleration

distributed

Advanced distributed computing with modular architecture

distributed_advanced

Advanced distributed optimization with consensus algorithms and fault recovery

enhanced_gpu_kernels

Enhanced GPU acceleration with real compute shaders and kernel optimization

gpu_acceleration

GPU acceleration for metrics computation

gpu_kernels

Advanced GPU kernels for high-performance metrics computation

hardware

Hardware acceleration utilities for metrics computation

memory

Memory-efficient metrics computation

numeric

Numerically stable implementations of metrics

parallel

Parallel computation utilities for metrics

quantum_acceleration

Quantum-inspired acceleration for metrics computation

simd_gpu

SIMD optimizations and GPU acceleration support for metrics computation