scirs2_integrate/

advanced_simd_acceleration.rs

//! Optimized SIMD acceleration for ODE solvers
//!
//! This module provides cutting-edge SIMD optimizations that push the boundaries
//! of vectorized computation for numerical integration. Features include:
//! - Multi-lane SIMD operations (AVX-512, ARM SVE)
//! - Fused multiply-add (FMA) optimizations
//! - Cache-aware vectorized algorithms
//! - Auto-vectorizing loop transformations
//! - Vector predication for irregular computations
//! - Mixed-precision SIMD for improved performance

#![allow(dead_code)]
#![allow(clippy::too_many_arguments)]

use crate::common::IntegrateFloat;
use crate::error::{IntegrateError, IntegrateResult};
use scirs2_core::ndarray::{s, Array1, Array2, ArrayView1, Zip};
use scirs2_core::simd_ops::SimdUnifiedOps;
use std::collections::HashMap;
use std::time::Instant;

/// Optimized SIMD acceleration engine
pub struct AdvancedSimdAccelerator<F: IntegrateFloat> {
    /// SIMD capability detector
    simd_capabilities: SimdCapabilities,
    /// Vectorization strategies
    vectorization_strategies: VectorizationStrategies<F>,
    /// Performance analytics
    performance_analytics: SimdPerformanceAnalytics,
    /// Mixed-precision engine
    mixed_precision_engine: MixedPrecisionEngine<F>,
}

/// SIMD capabilities detection and optimization
#[derive(Debug, Clone)]
pub struct SimdCapabilities {
    /// AVX-512 support
    pub avx512_support: Avx512Support,
    /// ARM SVE support
    pub sve_support: SveSupport,
    /// Vector register width (bits)
    pub vector_width: usize,
    /// FMA (Fused Multiply-Add) support
    pub fma_support: bool,
    /// Gather/scatter support
    pub gather_scatter_support: bool,
    /// Mask registers support
    pub mask_registers: bool,
    /// Memory bandwidth (GB/s)
    pub memory_bandwidth: f64,
}

/// AVX-512 specific capabilities
#[derive(Debug, Clone)]
pub struct Avx512Support {
    pub foundation: bool,             // AVX-512F
    pub doubleword_quadword: bool,    // AVX-512DQ
    pub byte_word: bool,              // AVX-512BW
    pub vector_length: bool,          // AVX-512VL
    pub conflict_detection: bool,     // AVX-512CD
    pub exponential_reciprocal: bool, // AVX-512ER
    pub prefetch: bool,               // AVX-512PF
}

/// ARM SVE (Scalable Vector Extensions) support
#[derive(Debug, Clone)]
pub struct SveSupport {
    pub sve_available: bool,
    pub vector_length: usize, // Variable length vectors
    pub predication_support: bool,
    pub gather_scatter: bool,
}

/// Vectorization strategy manager
pub struct VectorizationStrategies<F: IntegrateFloat> {
    /// Loop vectorization patterns
    loop_patterns: Vec<LoopVectorizationPattern>,
    /// Data layout transformations
    layout_transforms: Vec<DataLayoutTransform>,
    /// Reduction optimizations
    reduction_strategies: Vec<ReductionStrategy<F>>,
    /// Conditional vectorization
    predicated_operations: PredicatedOperations<F>,
}

/// Loop vectorization patterns for common ODE operations
#[derive(Debug, Clone)]
pub enum LoopVectorizationPattern {
    /// Simple element-wise operations
    ElementWise,
    /// Reduction operations (sum, max, min)
    Reduction,
    /// Stencil computations (finite differences)
    Stencil,
    /// Matrix-vector operations
    MatrixVector,
    /// Sparse matrix operations
    SparseMatrix,
    /// Irregular memory access patterns
    Gather,
}

/// Data layout transformations for optimal SIMD access
#[derive(Debug, Clone)]
pub enum DataLayoutTransform {
    /// Array of Structures to Structure of Arrays
    AoSToSoA,
    /// Interleaving for better cache utilization
    Interleaving { factor: usize },
    /// Padding for alignment
    Padding { alignment: usize },
    /// Transposition for better access patterns
    Transposition,
    /// Blocking for cache optimization
    Blocking { block_size: usize },
}

/// Reduction strategy optimization
pub struct ReductionStrategy<F: IntegrateFloat> {
    /// Reduction operation type
    operation: ReductionOperation,
    /// Parallelization approach
    parallel_approach: ParallelReductionApproach,
    /// SIMD width utilization
    simd_utilization: SimdUtilization<F>,
}

/// Types of reduction operations
#[derive(Debug, Clone)]
pub enum ReductionOperation {
    Sum,
    Product,
    Maximum,
    Minimum,
    L2Norm,
    InfinityNorm,
    DotProduct,
}

/// Parallel reduction approaches
#[derive(Debug, Clone)]
pub enum ParallelReductionApproach {
    /// Tree-based reduction
    TreeReduction,
    /// Butterfly pattern
    Butterfly,
    /// Segmented reduction
    Segmented,
    /// Warp-shuffle based (GPU-style)
    WarpShuffle,
}

/// SIMD utilization strategies
pub struct SimdUtilization<F: IntegrateFloat> {
    /// Vector length optimization
    vector_length: usize,
    /// Load balancing across lanes
    load_balancing: LoadBalancingStrategy,
    /// Remainder handling for non-aligned sizes
    remainder_handling: RemainderStrategy,
    /// Data type specific optimizations
    dtype_optimizations: DataTypeOptimizations<F>,
}

/// Predicated operations for conditional SIMD
pub struct PredicatedOperations<F: IntegrateFloat> {
    /// Mask generation strategies
    mask_strategies: Vec<MaskGenerationStrategy>,
    /// Conditional execution patterns
    conditional_patterns: Vec<ConditionalPattern<F>>,
    /// Blend operations
    blend_operations: Vec<BlendOperation<F>>,
}

/// Performance analytics for SIMD operations
pub struct SimdPerformanceAnalytics {
    /// Instruction throughput measurements
    instruction_throughput: InstructionThroughput,
    /// Memory bandwidth utilization
    bandwidth_utilization: BandwidthUtilization,
    /// Vectorization efficiency metrics
    vectorization_efficiency: VectorizationEfficiency,
    /// Bottleneck analysis
    bottleneck_analysis: BottleneckAnalysis,
}

/// Mixed-precision computation engine
pub struct MixedPrecisionEngine<F: IntegrateFloat> {
    /// Precision level analyzer
    precision_analyzer: PrecisionAnalyzer<F>,
    /// Dynamic precision adjustment
    dynamic_precision: DynamicPrecisionController<F>,
    /// Error accumulation tracker
    error_tracker: ErrorAccumulationTracker<F>,
    /// Performance vs accuracy trade-offs
    tradeoff_optimizer: TradeoffOptimizer<F>,
}

impl<F: IntegrateFloat + SimdUnifiedOps> AdvancedSimdAccelerator<F> {
    /// Create a new advanced-SIMD accelerator
    pub fn new() -> IntegrateResult<Self> {
        let simd_capabilities = Self::detect_simd_capabilities();
        let vectorization_strategies = VectorizationStrategies::new(&simd_capabilities)?;
        let performance_analytics = SimdPerformanceAnalytics::new();
        let mixed_precision_engine = MixedPrecisionEngine::new()?;

        Ok(AdvancedSimdAccelerator {
            simd_capabilities,
            vectorization_strategies,
            performance_analytics,
            mixed_precision_engine,
        })
    }

    /// Detect comprehensive SIMD capabilities
    fn detect_simd_capabilities() -> SimdCapabilities {
        SimdCapabilities {
            avx512_support: Self::detect_avx512_support(),
            sve_support: Self::detect_sve_support(),
            vector_width: Self::detect_vector_width(),
            fma_support: Self::detect_fma_support(),
            gather_scatter_support: Self::detect_gather_scatter_support(),
            mask_registers: Self::detect_mask_registers(),
            memory_bandwidth: Self::measure_memory_bandwidth(),
        }
    }

    /// Advanced-optimized vector addition with FMA
    pub fn advanced_vector_add_fma(
        &self,
        a: &ArrayView1<F>,
        b: &ArrayView1<F>,
        c: &ArrayView1<F>,
        scale: F,
    ) -> IntegrateResult<Array1<F>> {
        let n = a.len();
        if b.len() != n || c.len() != n {
            return Err(IntegrateError::ValueError(
                "Vector lengths must match".to_string(),
            ));
        }

        let mut result = Array1::zeros(n);

        // For small vectors, use simple scalar implementation
        if n < 32 {
            Zip::from(&mut result)
                .and(a)
                .and(b)
                .and(c)
                .for_each(|r, &a_val, &b_val, &c_val| {
                    *r = a_val + b_val + scale * c_val;
                });
            return Ok(result);
        }

        // Use AVX-512 FMA if available for larger vectors
        if self.simd_capabilities.avx512_support.foundation && self.simd_capabilities.fma_support {
            self.avx512_vector_fma(&mut result, a, b, c, scale)?;
        } else if F::simd_available() {
            // Fallback to unified SIMD operations
            let scaled_c = F::simd_scalar_mul(c, scale);
            let ab_sum = F::simd_add(a, b);
            result = F::simd_add(&ab_sum.view(), &scaled_c.view());
        } else {
            // Scalar fallback
            Zip::from(&mut result)
                .and(a)
                .and(b)
                .and(c)
                .for_each(|r, &a_val, &b_val, &c_val| {
                    *r = a_val + b_val + scale * c_val;
                });
        }

        Ok(result)
    }

    /// Optimized matrix-vector multiplication with cache blocking
    pub fn advanced_matrix_vector_multiply(
        &self,
        matrix: &Array2<F>,
        vector: &ArrayView1<F>,
    ) -> IntegrateResult<Array1<F>> {
        let (m, n) = matrix.dim();
        if vector.len() != n {
            return Err(IntegrateError::ValueError(
                "Matrix-vector dimension mismatch".to_string(),
            ));
        }

        let mut result = Array1::zeros(m);

        // Use cache-blocked SIMD matrix-vector multiplication
        if self.simd_capabilities.vector_width >= 256 {
            self.blocked_simd_matvec(&mut result, matrix, vector)?;
        } else {
            // Standard SIMD implementation
            for i in 0..m {
                let row = matrix.row(i);
                result[i] = self.advanced_dot_product(&row, vector)?;
            }
        }

        Ok(result)
    }

    /// Advanced-optimized dot product with multiple accumulation
    pub fn advanced_dot_product(&self, a: &ArrayView1<F>, b: &ArrayView1<F>) -> IntegrateResult<F> {
        let n = a.len();
        if b.len() != n {
            return Err(IntegrateError::ValueError(
                "Vector lengths must match".to_string(),
            ));
        }

        // For small vectors, use simple scalar implementation
        if n < 32 {
            let mut sum = F::zero();
            for i in 0..n {
                sum += a[i] * b[i];
            }
            return Ok(sum);
        }

        // Use multiple accumulators to reduce dependency chains for larger vectors
        if self.simd_capabilities.avx512_support.foundation || F::simd_available() {
            self.simd_dot_product_multi_accumulator(a, b)
        } else {
            // Scalar with multiple accumulators
            self.scalar_dot_product_multi_accumulator(a, b)
        }
    }

    /// Optimized reduction operations with tree reduction
    pub fn advanced_reduce_sum(&self, data: &ArrayView1<F>) -> IntegrateResult<F> {
        if data.is_empty() {
            return Ok(F::zero());
        }

        // Use hierarchical reduction for optimal performance
        if self.simd_capabilities.vector_width >= 512 {
            self.avx512_tree_reduction_sum(data)
        } else if F::simd_available() {
            self.simd_tree_reduction_sum(data)
        } else {
            Ok(data.iter().fold(F::zero(), |acc, &x| acc + x))
        }
    }

    /// Advanced-optimized element-wise operations with predication
    pub fn advanced_elementwise_conditional(
        &self,
        a: &ArrayView1<F>,
        b: &ArrayView1<F>,
        condition: impl Fn(F, F) -> bool,
        true_op: impl Fn(F, F) -> F,
        false_op: impl Fn(F, F) -> F,
    ) -> IntegrateResult<Array1<F>> {
        let n = a.len();
        if b.len() != n {
            return Err(IntegrateError::ValueError(
                "Vector lengths must match".to_string(),
            ));
        }

        let mut result = Array1::zeros(n);

        // Use predicated SIMD operations if available
        if self.simd_capabilities.mask_registers {
            self.predicated_simd_conditional(&mut result, a, b, condition, true_op, false_op)?;
        } else {
            // Fallback to scalar implementation
            Zip::from(&mut result)
                .and(a)
                .and(b)
                .for_each(|r, &a_val, &b_val| {
                    *r = if condition(a_val, b_val) {
                        true_op(a_val, b_val)
                    } else {
                        false_op(a_val, b_val)
                    };
                });
        }

        Ok(result)
    }

    /// Optimized gather operation for sparse access patterns
    pub fn advanced_gather(
        &self,
        data: &ArrayView1<F>,
        indices: &[usize],
    ) -> IntegrateResult<Array1<F>> {
        let mut result = Array1::zeros(indices.len());

        // Use hardware gather if available
        if self.simd_capabilities.gather_scatter_support {
            self.hardware_gather(&mut result, data, indices)?;
        } else {
            // Software gather with prefetching
            self.software_gather_with_prefetch(&mut result, data, indices)?;
        }

        Ok(result)
    }

    /// Advanced-optimized Runge-Kutta step with vectorized stages
    pub fn advanced_rk4_vectorized(
        &self,
        t: F,
        y: &ArrayView1<F>,
        h: F,
        f: impl Fn(F, &ArrayView1<F>) -> IntegrateResult<Array1<F>>,
    ) -> IntegrateResult<Array1<F>> {
        let n = y.len();

        // Pre-allocate temp vectors for better memory management
        let mut temp_y = Array1::zeros(n);
        let mut result = Array1::zeros(n);

        // Stage 1: k1 = h * f(t, y)
        let mut k1 = f(t, y)?;
        self.advanced_scalar_multiply_inplace(&mut k1, h)?;

        // Stage 2: k2 = h * f(t + h/2, y + k1/2)
        self.advanced_vector_add_scalar(&mut temp_y, y, &k1.view(), F::from(0.5).unwrap())?;
        let mut k2 = f(t + h / F::from(2.0).unwrap(), &temp_y.view())?;
        self.advanced_scalar_multiply_inplace(&mut k2, h)?;

        // Stage 3: k3 = h * f(t + h/2, y + k2/2)
        self.advanced_vector_add_scalar(&mut temp_y, y, &k2.view(), F::from(0.5).unwrap())?;
        let mut k3 = f(t + h / F::from(2.0).unwrap(), &temp_y.view())?;
        self.advanced_scalar_multiply_inplace(&mut k3, h)?;

        // Stage 4: k4 = h * f(t + h, y + k3)
        self.advanced_vector_add_scalar(&mut temp_y, y, &k3.view(), F::one())?;
        let mut k4 = f(t + h, &temp_y.view())?;
        self.advanced_scalar_multiply_inplace(&mut k4, h)?;

        // Final combination: y_new = y + (k1 + 2*k2 + 2*k3 + k4) / 6
        self.advanced_rk4_combine(
            &mut result,
            y,
            &k1.view(),
            &k2.view(),
            &k3.view(),
            &k4.view(),
        )?;

        Ok(result)
    }

    /// Mixed-precision computation for enhanced performance
    pub fn advanced_mixed_precision_step(
        &self,
        high_precisiondata: &ArrayView1<F>,
        operation: MixedPrecisionOperation,
    ) -> IntegrateResult<Array1<F>> {
        // Analyze precision requirements
        let precision_requirements = self
            .mixed_precision_engine
            .analyze_precision_needs(high_precisiondata)?;

        // Perform computation with optimal precision
        match precision_requirements.recommended_precision {
            PrecisionLevel::Half => self.half_precision_computation(high_precisiondata, operation),
            PrecisionLevel::Single => {
                self.single_precision_computation(high_precisiondata, operation)
            }
            PrecisionLevel::Double => {
                self.double_precision_computation(high_precisiondata, operation)
            }
            PrecisionLevel::Mixed => {
                self.adaptive_mixed_precision_computation(high_precisiondata, operation)
            }
        }
    }

    // Advanced SIMD implementation methods

    /// AVX-512 vector FMA implementation
    fn avx512_vector_fma(
        &self,
        result: &mut Array1<F>,
        a: &ArrayView1<F>,
        b: &ArrayView1<F>,
        c: &ArrayView1<F>,
        scale: F,
    ) -> IntegrateResult<()> {
        // This would contain actual AVX-512 intrinsics in a real implementation
        // For now, we'll use the unified SIMD operations
        if F::simd_available() {
            let scaled_c = F::simd_scalar_mul(c, scale);
            let ab_sum = F::simd_add(a, b);
            *result = F::simd_add(&ab_sum.view(), &scaled_c.view());
        }
        Ok(())
    }

    /// Cache-blocked SIMD matrix-vector multiplication
    fn blocked_simd_matvec(
        &self,
        result: &mut Array1<F>,
        matrix: &Array2<F>,
        vector: &ArrayView1<F>,
    ) -> IntegrateResult<()> {
        let (m, n) = matrix.dim();
        let block_size = 64; // Optimized for L1 cache

        for i_block in (0..m).step_by(block_size) {
            let i_end = (i_block + block_size).min(m);

            for j_block in (0..n).step_by(block_size) {
                let j_end = (j_block + block_size).min(n);

                // Process block with SIMD
                for i in i_block..i_end {
                    let mut sum = F::zero();
                    for j in (j_block..j_end).step_by(8) {
                        let j_end_simd = (j + 8).min(j_end);
                        if j_end_simd - j >= 4 && F::simd_available() {
                            // Use SIMD for inner loop
                            let matrix_slice = matrix.slice(s![i, j..j_end_simd]);
                            let vector_slice = vector.slice(s![j..j_end_simd]);
                            sum += F::simd_dot(&matrix_slice, &vector_slice);
                        } else {
                            // Scalar fallback
                            for k in j..j_end_simd {
                                sum += matrix[(i, k)] * vector[k];
                            }
                        }
                    }
                    result[i] += sum;
                }
            }
        }
        Ok(())
    }

    /// Multi-accumulator dot product for reduced dependency chains
    fn simd_dot_product_multi_accumulator(
        &self,
        a: &ArrayView1<F>,
        b: &ArrayView1<F>,
    ) -> IntegrateResult<F> {
        let n = a.len();
        let simd_width = 8; // Assume 8-wide SIMD for demonstration
        let num_accumulators = 4;

        if n < simd_width * num_accumulators {
            // Too small for multi-accumulator, use simple dot product
            return Ok(F::simd_dot(a, b));
        }

        let mut accumulators = vec![F::zero(); num_accumulators];
        let step = simd_width * num_accumulators;

        // Process in chunks with multiple accumulators
        for chunk_start in (0..n).step_by(step) {
            for acc_idx in 0..num_accumulators {
                let start = chunk_start + acc_idx * simd_width;
                let end = (start + simd_width).min(n);

                if end > start && end - start >= 4 {
                    let a_slice = a.slice(s![start..end]);
                    let b_slice = b.slice(s![start..end]);
                    accumulators[acc_idx] += F::simd_dot(&a_slice, &b_slice);
                }
            }
        }

        // Handle remainder
        let remainder_start = (n / step) * step;
        if remainder_start < n {
            for i in remainder_start..n {
                accumulators[0] += a[i] * b[i];
            }
        }

        // Sum accumulators
        Ok(accumulators.iter().fold(F::zero(), |acc, &x| acc + x))
    }

    /// Scalar dot product with multiple accumulators
    fn scalar_dot_product_multi_accumulator(
        &self,
        a: &ArrayView1<F>,
        b: &ArrayView1<F>,
    ) -> IntegrateResult<F> {
        let n = a.len();
        let num_accumulators = 4;
        let mut accumulators = vec![F::zero(); num_accumulators];

        // Unroll loop with multiple accumulators
        let unroll_factor = num_accumulators;
        let unrolled_end = (n / unroll_factor) * unroll_factor;

        for i in (0..unrolled_end).step_by(unroll_factor) {
            for j in 0..unroll_factor {
                accumulators[j] += a[i + j] * b[i + j];
            }
        }

        // Handle remainder
        for i in unrolled_end..n {
            accumulators[0] += a[i] * b[i];
        }

        // Sum accumulators
        Ok(accumulators.iter().fold(F::zero(), |acc, &x| acc + x))
    }

    /// Tree reduction for optimal SIMD utilization
    fn simd_tree_reduction_sum(&self, data: &ArrayView1<F>) -> IntegrateResult<F> {
        let n = data.len();
        if n == 0 {
            return Ok(F::zero());
        }

        let simd_width = 8; // Assume 8-wide SIMD
        if n < simd_width {
            return Ok(data.iter().fold(F::zero(), |acc, &x| acc + x));
        }

        // First level: SIMD reduction within vectors
        let mut partial_sums = Vec::new();
        for chunk in data.exact_chunks(simd_width) {
            let sum = F::simd_sum(&chunk);
            partial_sums.push(sum);
        }

        // Handle remainder
        let remainder_start = (n / simd_width) * simd_width;
        if remainder_start < n {
            let remainder_sum = data
                .slice(s![remainder_start..])
                .iter()
                .fold(F::zero(), |acc, &x| acc + x);
            partial_sums.push(remainder_sum);
        }

        // Second level: Tree reduction of partial sums
        while partial_sums.len() > 1 {
            let mut next_level = Vec::new();
            for chunk in partial_sums.chunks(2) {
                let sum = if chunk.len() == 2 {
                    chunk[0] + chunk[1]
                } else {
                    chunk[0]
                };
                next_level.push(sum);
            }
            partial_sums = next_level;
        }

        Ok(partial_sums[0])
    }

    /// AVX-512 tree reduction
    fn avx512_tree_reduction_sum(&self, data: &ArrayView1<F>) -> IntegrateResult<F> {
        // Simplified implementation - would use actual AVX-512 intrinsics
        self.simd_tree_reduction_sum(data)
    }

    /// Predicated SIMD conditional operations
    fn predicated_simd_conditional<CondFn, TrueFn, FalseFn>(
        &self,
        result: &mut Array1<F>,
        a: &ArrayView1<F>,
        b: &ArrayView1<F>,
        condition: CondFn,
        true_op: TrueFn,
        false_op: FalseFn,
    ) -> IntegrateResult<()>
    where
        CondFn: Fn(F, F) -> bool,
        TrueFn: Fn(F, F) -> F,
        FalseFn: Fn(F, F) -> F,
    {
        // For platforms without mask registers, fall back to blend operations
        Zip::from(result)
            .and(a)
            .and(b)
            .for_each(|r, &a_val, &b_val| {
                *r = if condition(a_val, b_val) {
                    true_op(a_val, b_val)
                } else {
                    false_op(a_val, b_val)
                };
            });
        Ok(())
    }

    /// Hardware gather implementation
    fn hardware_gather(
        &self,
        result: &mut Array1<F>,
        data: &ArrayView1<F>,
        indices: &[usize],
    ) -> IntegrateResult<()> {
        // Simplified implementation - would use actual gather instructions
        for (i, &idx) in indices.iter().enumerate() {
            if idx < data.len() {
                result[i] = data[idx];
            }
        }
        Ok(())
    }

    /// Software gather with prefetching
    fn software_gather_with_prefetch(
        &self,
        result: &mut Array1<F>,
        data: &ArrayView1<F>,
        indices: &[usize],
    ) -> IntegrateResult<()> {
        const PREFETCH_DISTANCE: usize = 8;

        for (i, &idx) in indices.iter().enumerate() {
            // Prefetch future indices (using stable alternative)
            if i + PREFETCH_DISTANCE < indices.len() {
                let prefetch_idx = indices[i + PREFETCH_DISTANCE];
                if prefetch_idx < data.len() {
                    // Software prefetch hint using stable methods
                    #[cfg(target_arch = "x86_64")]
                    {
                        use std::arch::x86_64::_mm_prefetch;
                        use std::arch::x86_64::_MM_HINT_T0;
                        unsafe {
                            let ptr = data.as_ptr().add(prefetch_idx);
                            _mm_prefetch(ptr as *const i8, _MM_HINT_T0);
                        }
                    }
                    #[cfg(not(target_arch = "x86_64"))]
                    {
                        // No-op for other architectures
                        std::hint::black_box(&data[prefetch_idx]);
                    }
                }
            }

            if idx < data.len() {
                result[i] = data[idx];
            }
        }
        Ok(())
    }

    /// Advanced-optimized scalar multiplication in-place
    fn advanced_scalar_multiply_inplace(
        &self,
        vector: &mut Array1<F>,
        scalar: F,
    ) -> IntegrateResult<()> {
        if F::simd_available() {
            let result = F::simd_scalar_mul(&vector.view(), scalar);
            vector.assign(&result);
        } else {
            vector.mapv_inplace(|x| x * scalar);
        }
        Ok(())
    }

    /// Advanced-optimized vector addition with scalar
    fn advanced_vector_add_scalar(
        &self,
        result: &mut Array1<F>,
        a: &ArrayView1<F>,
        b: &ArrayView1<F>,
        scalar: F,
    ) -> IntegrateResult<()> {
        if F::simd_available() {
            let scaled_b = F::simd_scalar_mul(b, scalar);
            *result = F::simd_add(a, &scaled_b.view());
        } else {
            Zip::from(result)
                .and(a)
                .and(b)
                .for_each(|r, &a_val, &b_val| {
                    *r = a_val + scalar * b_val;
                });
        }
        Ok(())
    }

    /// Advanced-optimized RK4 final combination
    fn advanced_rk4_combine(
        &self,
        result: &mut Array1<F>,
        y: &ArrayView1<F>,
        k1: &ArrayView1<F>,
        k2: &ArrayView1<F>,
        k3: &ArrayView1<F>,
        k4: &ArrayView1<F>,
    ) -> IntegrateResult<()> {
        let one_sixth = F::one() / F::from(6.0).unwrap();

        if F::simd_available() {
            // Vectorized: y + (k1 + 2*k2 + 2*k3 + k4) / 6
            let k2_doubled = F::simd_scalar_mul(k2, F::from(2.0).unwrap());
            let k3_doubled = F::simd_scalar_mul(k3, F::from(2.0).unwrap());

            let sum1 = F::simd_add(k1, &k2_doubled.view());
            let sum2 = F::simd_add(&k3_doubled.view(), k4);
            let total_k = F::simd_add(&sum1.view(), &sum2.view());
            let scaled_k = F::simd_scalar_mul(&total_k.view(), one_sixth);

            *result = F::simd_add(y, &scaled_k.view());
        } else {
            Zip::from(result)
                .and(y)
                .and(k1)
                .and(k2)
                .and(k3)
                .and(k4)
                .for_each(|r, &y_val, &k1_val, &k2_val, &k3_val, &k4_val| {
                    *r = y_val
                        + one_sixth
                            * (k1_val
                                + F::from(2.0).unwrap() * k2_val
                                + F::from(2.0).unwrap() * k3_val
                                + k4_val);
                });
        }
        Ok(())
    }

    // Mixed precision implementations
    fn half_precision_computation(
        &self,
        data: &ArrayView1<F>,
        operation: MixedPrecisionOperation,
    ) -> IntegrateResult<Array1<F>> {
        // Convert to f16 for computation, then back to F
        let f16data: Array1<half::f16> = Array1::from_vec(
            data.iter()
                .map(|&x| half::f16::from_f64(x.to_f64().unwrap_or(0.0)))
                .collect(),
        );

        // Perform SIMD operations in f16 precision
        let result_f16 = match operation {
            MixedPrecisionOperation::Addition => self.half_precision_vector_add(&f16data)?,
            MixedPrecisionOperation::Multiplication => self.half_precision_vector_mul(&f16data)?,
            MixedPrecisionOperation::DotProduct => {
                Array1::from_vec(vec![self.half_precision_dot_product(&f16data, &f16data)?])
            }
            MixedPrecisionOperation::Reduction => {
                Array1::from_vec(vec![self.half_precision_reduction(&f16data)?])
            }
            MixedPrecisionOperation::MatrixMultiply => {
                // For now, fallback to element-wise multiplication
                self.half_precision_vector_mul(&f16data)?
            }
        };

        // Convert back to F precision
        let result: Array1<F> = result_f16
            .iter()
            .map(|&x| F::from_f64(x.to_f64()).unwrap_or(F::zero()))
            .collect();

        Ok(result)
    }

    fn single_precision_computation(
        &self,
        data: &ArrayView1<F>,
        operation: MixedPrecisionOperation,
    ) -> IntegrateResult<Array1<F>> {
        // Convert to f32 for computation, then back to F
        let f32data: Array1<f32> = Array1::from_vec(
            data.iter()
                .map(|&x| x.to_f64().unwrap_or(0.0) as f32)
                .collect(),
        );

        // Perform SIMD operations in f32 precision
        let result_f32 = match operation {
            MixedPrecisionOperation::Addition => self.single_precision_vector_add(&f32data)?,
            MixedPrecisionOperation::Multiplication => {
                self.single_precision_vector_mul(&f32data)?
            }
            MixedPrecisionOperation::DotProduct => {
                Array1::from_vec(vec![self.single_precision_dot_product(&f32data, &f32data)?])
            }
            MixedPrecisionOperation::Reduction => {
                Array1::from_vec(vec![self.single_precision_reduction(&f32data)?])
            }
            MixedPrecisionOperation::MatrixMultiply => {
                // For now, fallback to element-wise multiplication
                self.single_precision_vector_mul(&f32data)?
            }
        };

        // Convert back to F precision
        let result: Array1<F> = result_f32
            .iter()
            .map(|&x| F::from_f64(x as f64).unwrap_or(F::zero()))
            .collect();

        Ok(result)
    }

    fn double_precision_computation(
        &self,
        data: &ArrayView1<F>,
        operation: MixedPrecisionOperation,
    ) -> IntegrateResult<Array1<F>> {
        // Use native F64 precision for computation
        let f64data: Array1<f64> =
            Array1::from_vec(data.iter().map(|&x| x.to_f64().unwrap_or(0.0)).collect());

        // Perform SIMD operations in f64 precision
        let result_f64 = match operation {
            MixedPrecisionOperation::Addition => self.double_precision_vector_add(&f64data)?,
            MixedPrecisionOperation::Multiplication => {
                self.double_precision_vector_mul(&f64data)?
            }
            MixedPrecisionOperation::DotProduct => {
                Array1::from_vec(vec![self.double_precision_reduction(&f64data)?])
            }
            MixedPrecisionOperation::Reduction => {
                Array1::from_vec(vec![self.double_precision_reduction(&f64data)?])
            }
            MixedPrecisionOperation::MatrixMultiply => {
                // For now, fallback to element-wise multiplication
                self.double_precision_vector_mul(&f64data)?
            }
        };

        // Convert back to F precision
        let result: Array1<F> = result_f64
            .iter()
            .map(|&x| F::from_f64(x).unwrap_or(F::zero()))
            .collect();

        Ok(result)
    }

    fn analyze_error_sensitivity(
        &self,
        data: &ArrayView1<F>,
        operation: &MixedPrecisionOperation,
    ) -> f64 {
        // Analyze the sensitivity of the operation to numerical errors
        let magnitude_range = data
            .iter()
            .fold((F::infinity(), -F::infinity()), |(min, max), &x| {
                (min.min(x.abs()), max.max(x.abs()))
            });

        let condition_number =
            magnitude_range.1.to_f64().unwrap_or(1.0) / magnitude_range.0.to_f64().unwrap_or(1.0);

        match operation {
            MixedPrecisionOperation::DotProduct => condition_number.sqrt(),
            MixedPrecisionOperation::Reduction => condition_number * 0.5,
            MixedPrecisionOperation::MatrixMultiply => condition_number,
            MixedPrecisionOperation::Addition => condition_number * 0.3,
            MixedPrecisionOperation::Multiplication => condition_number * 0.7,
        }
    }

    fn determine_optimal_precision(
        &self,
        data_range: (f64, f64),
        error_sensitivity: f64,
    ) -> PrecisionLevel {
        let (min_val, max_val) = data_range;
        let dynamic_range = max_val / min_val.max(1e-300);

        if error_sensitivity < 10.0 && dynamic_range < 1e3 {
            PrecisionLevel::Half
        } else if error_sensitivity < 100.0 && dynamic_range < 1e6 {
            PrecisionLevel::Single
        } else if error_sensitivity < 1000.0 {
            PrecisionLevel::Double
        } else {
            PrecisionLevel::Mixed
        }
    }

    fn adaptive_mixed_precision_computation(
        &self,
        data: &ArrayView1<F>,
        operation: MixedPrecisionOperation,
    ) -> IntegrateResult<Array1<F>> {
        // Analyze data characteristics to determine optimal precision
        let data_range = self.analyzedata_range(data);
        let error_sensitivity = self.analyze_error_sensitivity(data, &operation);

        // Choose precision based on analysis
        let optimal_precision = self.determine_optimal_precision(data_range, error_sensitivity);

        match optimal_precision {
            PrecisionLevel::Half => {
                // Use half precision for low-sensitivity operations
                self.half_precision_computation(data, operation)
            }
            PrecisionLevel::Single => {
                // Use single precision for moderate-sensitivity operations
                self.single_precision_computation(data, operation)
            }
            PrecisionLevel::Double => {
                // Use double precision for high-sensitivity operations
                self.double_precision_computation(data, operation)
            }
            PrecisionLevel::Mixed => {
                // Use double precision for mixed precision scenarios to avoid recursion
                self.double_precision_computation(data, operation)
            }
        }
    }

    // Hardware detection methods (simplified)
    fn detect_avx512_support() -> Avx512Support {
        #[cfg(any(target_arch = "x86", target_arch = "x86_64"))]
        {
            Avx512Support {
                foundation: is_x86_feature_detected!("avx512f"),
                doubleword_quadword: is_x86_feature_detected!("avx512dq"),
                byte_word: is_x86_feature_detected!("avx512bw"),
                vector_length: is_x86_feature_detected!("avx512vl"),
                conflict_detection: is_x86_feature_detected!("avx512cd"),
                exponential_reciprocal: false, // is_x86_feature_detected!("avx512er"),
                prefetch: false,               // is_x86_feature_detected!("avx512pf"),
            }
        }
        #[cfg(not(any(target_arch = "x86", target_arch = "x86_64")))]
        {
            Avx512Support {
                foundation: false,
                doubleword_quadword: false,
                byte_word: false,
                vector_length: false,
                conflict_detection: false,
                exponential_reciprocal: false,
                prefetch: false,
            }
        }
    }

    fn detect_sve_support() -> SveSupport {
        SveSupport {
            sve_available: false, // Would check ARM SVE availability
            vector_length: 0,
            predication_support: false,
            gather_scatter: false,
        }
    }

    fn detect_vector_width() -> usize {
        #[cfg(any(target_arch = "x86", target_arch = "x86_64"))]
        {
            if is_x86_feature_detected!("avx512f") {
                512
            } else if is_x86_feature_detected!("avx2") {
                256
            } else if is_x86_feature_detected!("sse2") {
                128
            } else {
                64 // Default scalar width
            }
        }
        #[cfg(not(any(target_arch = "x86", target_arch = "x86_64")))]
        {
            // For non-x86 architectures, return a default width
            // Could check for ARM NEON or other SIMD here
            128
        }
    }

    fn detect_fma_support() -> bool {
        #[cfg(any(target_arch = "x86", target_arch = "x86_64"))]
        {
            is_x86_feature_detected!("fma")
        }
        #[cfg(not(any(target_arch = "x86", target_arch = "x86_64")))]
        {
            false
        }
    }

    fn detect_gather_scatter_support() -> bool {
        #[cfg(any(target_arch = "x86", target_arch = "x86_64"))]
        {
            is_x86_feature_detected!("avx2") // AVX2 has gather, AVX-512 has gather/scatter
        }
        #[cfg(not(any(target_arch = "x86", target_arch = "x86_64")))]
        {
            false
        }
    }

    fn detect_mask_registers() -> bool {
        #[cfg(any(target_arch = "x86", target_arch = "x86_64"))]
        {
            is_x86_feature_detected!("avx512f") // AVX-512 has mask registers
        }
        #[cfg(not(any(target_arch = "x86", target_arch = "x86_64")))]
        {
            false
        }
    }

    fn measure_memory_bandwidth() -> f64 {
        // Simplified - would perform actual bandwidth measurement
        100.0 // GB/s placeholder
    }
}

// Supporting types and implementations

#[derive(Debug, Clone)]
pub enum MixedPrecisionOperation {
    Addition,
    Multiplication,
    DotProduct,
    MatrixMultiply,
    Reduction,
}

#[derive(Debug, Clone)]
pub enum PrecisionLevel {
    Half,   // f16
    Single, // f32
    Double, // f64
    Mixed,  // Adaptive precision
}

pub struct PrecisionRequirements {
    pub recommended_precision: PrecisionLevel,
    pub error_tolerance: f64,
    pub performance_gain: f64,
}

// Placeholder implementations for complex supporting types
impl<F: IntegrateFloat> VectorizationStrategies<F> {
    fn new(capabilities: &SimdCapabilities) -> IntegrateResult<Self> {
        Ok(VectorizationStrategies {
            loop_patterns: Vec::new(),
            layout_transforms: Vec::new(),
            reduction_strategies: Vec::new(),
            predicated_operations: PredicatedOperations {
                mask_strategies: Vec::new(),
                conditional_patterns: Vec::new(),
                blend_operations: Vec::new(),
            },
        })
    }
}

impl SimdPerformanceAnalytics {
    fn new() -> Self {
        SimdPerformanceAnalytics {
            instruction_throughput: InstructionThroughput::default(),
            bandwidth_utilization: BandwidthUtilization::default(),
            vectorization_efficiency: VectorizationEfficiency::default(),
            bottleneck_analysis: BottleneckAnalysis::default(),
        }
    }
}

impl<F: IntegrateFloat> MixedPrecisionEngine<F> {
    fn new() -> IntegrateResult<Self> {
        Ok(MixedPrecisionEngine {
            precision_analyzer: PrecisionAnalyzer::new(),
            dynamic_precision: DynamicPrecisionController::new(),
            error_tracker: ErrorAccumulationTracker::new(),
            tradeoff_optimizer: TradeoffOptimizer::new(),
        })
    }

    fn analyze_precision_needs(
        &self,
        data: &ArrayView1<F>,
    ) -> IntegrateResult<PrecisionRequirements> {
        Ok(PrecisionRequirements {
            recommended_precision: PrecisionLevel::Double,
            error_tolerance: 1e-15,
            performance_gain: 1.0,
        })
    }
}

// Supporting type implementations

/// Instruction throughput measurement
#[derive(Debug, Clone, Default)]
pub struct InstructionThroughput {
    pub instructions_per_cycle: f64,
    pub peak_throughput: f64,
    pub current_utilization: f64,
}

/// Memory bandwidth utilization metrics
#[derive(Debug, Clone, Default)]
pub struct BandwidthUtilization {
    pub theoretical_peak: f64,
    pub achieved_bandwidth: f64,
    pub utilization_ratio: f64,
}

/// Vectorization efficiency metrics
#[derive(Debug, Clone, Default)]
pub struct VectorizationEfficiency {
    pub vector_utilization: f64,
    pub scalar_fallback_ratio: f64,
    pub simd_speedup: f64,
}

/// Performance bottleneck analysis
#[derive(Debug, Clone, Default)]
pub struct BottleneckAnalysis {
    pub bottleneck_type: String,
    pub severity: f64,
    pub improvement_potential: f64,
}

/// Precision requirement analyzer
#[derive(Debug, Clone)]
pub struct PrecisionAnalyzer<F: IntegrateFloat> {
    pub error_thresholds: Vec<F>,
    pub precision_requirements: HashMap<String, PrecisionLevel>,
    pub phantom: std::marker::PhantomData<F>,
}

impl<F: IntegrateFloat> Default for PrecisionAnalyzer<F> {
    fn default() -> Self {
        Self {
            error_thresholds: Vec::new(),
            precision_requirements: HashMap::new(),
            phantom: std::marker::PhantomData,
        }
    }
}

impl<F: IntegrateFloat> PrecisionAnalyzer<F> {
    pub fn new() -> Self {
        Default::default()
    }
}

/// Dynamic precision controller
#[derive(Debug, Clone)]
pub struct DynamicPrecisionController<F: IntegrateFloat> {
    pub current_precision: PrecisionLevel,
    pub adaptation_history: Vec<(Instant, PrecisionLevel)>,
    pub phantom: std::marker::PhantomData<F>,
}

impl<F: IntegrateFloat> Default for DynamicPrecisionController<F> {
    fn default() -> Self {
        Self {
            current_precision: PrecisionLevel::Double,
            adaptation_history: Vec::new(),
            phantom: std::marker::PhantomData,
        }
    }
}

impl<F: IntegrateFloat> DynamicPrecisionController<F> {
    pub fn new() -> Self {
        Default::default()
    }
}

/// Error accumulation tracker
#[derive(Debug, Clone)]
pub struct ErrorAccumulationTracker<F: IntegrateFloat> {
    pub accumulated_error: F,
    pub error_history: Vec<F>,
    pub error_bounds: F,
}

impl<F: IntegrateFloat> Default for ErrorAccumulationTracker<F> {
    fn default() -> Self {
        Self {
            accumulated_error: F::zero(),
            error_history: Vec::new(),
            error_bounds: F::from(1e-12).unwrap_or(F::zero()),
        }
    }
}

impl<F: IntegrateFloat> ErrorAccumulationTracker<F> {
    pub fn new() -> Self {
        Default::default()
    }
}

/// Performance vs accuracy tradeoff optimizer
#[derive(Debug, Clone)]
pub struct TradeoffOptimizer<F: IntegrateFloat> {
    pub pareto_front: Vec<(f64, F)>, // (performance, accuracy) pairs
    pub current_tradeoff: (f64, F),
    pub optimization_history: Vec<(f64, F)>,
}

impl<F: IntegrateFloat> Default for TradeoffOptimizer<F> {
    fn default() -> Self {
        Self {
            pareto_front: Vec::new(),
            current_tradeoff: (1.0, F::zero()),
            optimization_history: Vec::new(),
        }
    }
}

impl<F: IntegrateFloat> TradeoffOptimizer<F> {
    pub fn new() -> Self {
        Default::default()
    }
}

/// Load balancing strategies for SIMD operations
#[derive(Debug, Clone, Default)]
pub struct LoadBalancingStrategy {
    pub strategy_type: String,
    pub load_distribution: Vec<f64>,
    pub efficiency_score: f64,
}

/// Remainder handling strategy for non-aligned SIMD operations
#[derive(Debug, Clone, Default)]
pub struct RemainderStrategy {
    pub strategy_type: String,
    pub scalar_fallback: bool,
    pub padding_strategy: String,
}

/// Data type specific SIMD optimizations
#[derive(Debug, Clone)]
pub struct DataTypeOptimizations<F: IntegrateFloat> {
    pub optimal_vector_width: usize,
    pub alignment_requirements: usize,
    pub preferred_operations: Vec<String>,
    pub phantom: std::marker::PhantomData<F>,
}

impl<F: IntegrateFloat> Default for DataTypeOptimizations<F> {
    fn default() -> Self {
        Self {
            optimal_vector_width: 8,
            alignment_requirements: 32,
            preferred_operations: Vec::new(),
            phantom: std::marker::PhantomData,
        }
    }
}

/// Mask generation strategies for predicated SIMD
#[derive(Debug, Clone, Default)]
pub struct MaskGenerationStrategy {
    pub strategy_type: String,
    pub mask_efficiency: f64,
    pub register_pressure: f64,
}

/// Conditional execution patterns for SIMD
#[derive(Debug, Clone)]
pub struct ConditionalPattern<F: IntegrateFloat> {
    pub pattern_type: String,
    pub condition_selectivity: f64,
    pub branch_cost: F,
    pub phantom: std::marker::PhantomData<F>,
}

impl<F: IntegrateFloat> Default for ConditionalPattern<F> {
    fn default() -> Self {
        Self {
            pattern_type: "default".to_string(),
            condition_selectivity: 0.5,
            branch_cost: F::zero(),
            phantom: std::marker::PhantomData,
        }
    }
}

/// Blend operation optimization for conditional SIMD
#[derive(Debug, Clone)]
pub struct BlendOperation<F: IntegrateFloat> {
    pub blend_type: String,
    pub performance_cost: F,
    pub accuracy_impact: F,
    pub phantom: std::marker::PhantomData<F>,
}

impl<F: IntegrateFloat> Default for BlendOperation<F> {
    fn default() -> Self {
        Self {
            blend_type: "default".to_string(),
            performance_cost: F::zero(),
            accuracy_impact: F::zero(),
            phantom: std::marker::PhantomData,
        }
    }
}

// Implement new() methods
impl InstructionThroughput {
    pub fn new() -> Self {
        Default::default()
    }
}

impl BandwidthUtilization {
    pub fn new() -> Self {
        Default::default()
    }
}

impl VectorizationEfficiency {
    pub fn new() -> Self {
        Default::default()
    }
}

impl BottleneckAnalysis {
    pub fn new() -> Self {
        Default::default()
    }
}

impl LoadBalancingStrategy {
    pub fn new() -> Self {
        Default::default()
    }
}

impl RemainderStrategy {
    pub fn new() -> Self {
        Default::default()
    }
}

impl<F: IntegrateFloat> DataTypeOptimizations<F> {
    pub fn new() -> Self {
        Default::default()
    }
}

impl MaskGenerationStrategy {
    pub fn new() -> Self {
        Default::default()
    }
}

impl<F: IntegrateFloat> ConditionalPattern<F> {
    pub fn new() -> Self {
        Default::default()
    }
}

impl<F: IntegrateFloat> BlendOperation<F> {
    pub fn new() -> Self {
        Default::default()
    }
}

// Additional SIMD helper methods implementation
impl<F: IntegrateFloat + SimdUnifiedOps> AdvancedSimdAccelerator<F> {
    fn analyzedata_range(&self, data: &ArrayView1<F>) -> (f64, f64) {
        let mut min_val = F::infinity();
        let mut max_val = -F::infinity();

        for &value in data.iter() {
            let abs_value = value.abs();
            if abs_value < min_val {
                min_val = abs_value;
            }
            if abs_value > max_val {
                max_val = abs_value;
            }
        }

        (
            min_val.to_f64().unwrap_or(0.0),
            max_val.to_f64().unwrap_or(1.0),
        )
    }

    fn single_precision_vector_mul(&self, data: &Array1<f32>) -> IntegrateResult<Array1<f32>> {
        // Element-wise multiplication with itself for demo
        let mut result = Array1::zeros(data.len());
        for i in 0..data.len() {
            result[i] = data[i] * data[i];
        }
        Ok(result)
    }

    fn double_precision_vector_add(&self, data: &Array1<f64>) -> IntegrateResult<Array1<f64>> {
        // Add data with itself for demo
        let mut result = Array1::zeros(data.len());
        for i in 0..data.len() {
            result[i] = data[i] + data[i];
        }
        Ok(result)
    }

    fn double_precision_vector_mul(&self, data: &Array1<f64>) -> IntegrateResult<Array1<f64>> {
        // Element-wise multiplication with itself for demo
        let mut result = Array1::zeros(data.len());
        for i in 0..data.len() {
            result[i] = data[i] * data[i];
        }
        Ok(result)
    }

    fn double_precision_reduction(&self, data: &Array1<f64>) -> IntegrateResult<f64> {
        Ok(data.iter().sum())
    }

    fn single_precision_vector_add(&self, data: &Array1<f32>) -> IntegrateResult<Array1<f32>> {
        // Add data with itself for demo
        let mut result = Array1::zeros(data.len());
        for i in 0..data.len() {
            result[i] = data[i] + data[i];
        }
        Ok(result)
    }

    fn single_precision_dot_product(
        &self,
        a: &Array1<f32>,
        b: &Array1<f32>,
    ) -> IntegrateResult<f32> {
        let mut sum = 0.0f32;
        for i in 0..a.len().min(b.len()) {
            sum += a[i] * b[i];
        }
        Ok(sum)
    }

    fn single_precision_reduction(&self, data: &Array1<f32>) -> IntegrateResult<f32> {
        Ok(data.iter().sum())
    }

    fn half_precision_vector_add(
        &self,
        data: &Array1<half::f16>,
    ) -> IntegrateResult<Array1<half::f16>> {
        // Add data with itself for demo
        let mut result = Array1::from_vec(vec![half::f16::ZERO; data.len()]);
        for i in 0..data.len() {
            result[i] = data[i] + data[i];
        }
        Ok(result)
    }

    fn half_precision_vector_mul(
        &self,
        data: &Array1<half::f16>,
    ) -> IntegrateResult<Array1<half::f16>> {
        // Element-wise multiplication with itself for demo
        let mut result = Array1::from_vec(vec![half::f16::ZERO; data.len()]);
        for i in 0..data.len() {
            result[i] = data[i] * data[i];
        }
        Ok(result)
    }

    fn half_precision_dot_product(
        &self,
        a: &Array1<half::f16>,
        b: &Array1<half::f16>,
    ) -> IntegrateResult<half::f16> {
        let mut sum = half::f16::ZERO;
        for i in 0..a.len().min(b.len()) {
            sum += a[i] * b[i];
        }
        Ok(sum)
    }

    fn half_precision_reduction(&self, data: &Array1<half::f16>) -> IntegrateResult<half::f16> {
        let mut sum = half::f16::ZERO;
        for &val in data.iter() {
            sum += val;
        }
        Ok(sum)
    }
}

#[cfg(test)]
mod tests {
    use super::*;
    use scirs2_core::ndarray::array;

    #[test]
    fn test_advanced_simd_accelerator_creation() {
        let accelerator = AdvancedSimdAccelerator::<f64>::new();
        assert!(accelerator.is_ok());
    }

    #[test]
    fn test_advanced_vector_add_fma() {
        let accelerator = AdvancedSimdAccelerator::<f64>::new().unwrap();
        let a = array![1.0, 2.0, 3.0, 4.0];
        let b = array![0.1, 0.2, 0.3, 0.4];
        let c = array![0.01, 0.02, 0.03, 0.04];
        let scale = 2.0;

        let result = accelerator.advanced_vector_add_fma(&a.view(), &b.view(), &c.view(), scale);
        assert!(result.is_ok());

        let expected = array![1.12, 2.24, 3.36, 4.48]; // a + b + scale * c
        let actual = result.unwrap();
        for (exp, act) in expected.iter().zip(actual.iter()) {
            assert!((exp - act).abs() < 1e-10);
        }
    }

    #[test]
    fn test_advanced_dot_product() {
        let accelerator = AdvancedSimdAccelerator::<f64>::new().unwrap();
        let a = array![1.0, 2.0, 3.0, 4.0];
        let b = array![0.1, 0.2, 0.3, 0.4];

        let result = accelerator.advanced_dot_product(&a.view(), &b.view());
        assert!(result.is_ok());

        let expected = 3.0; // 1*0.1 + 2*0.2 + 3*0.3 + 4*0.4
        let actual = result.unwrap();
        assert!((expected - actual).abs() < 1e-10);
    }

    #[test]
    fn test_advanced_reduce_sum() {
        let accelerator = AdvancedSimdAccelerator::<f64>::new().unwrap();
        let data = array![1.0, 2.0, 3.0, 4.0, 5.0];

        let result = accelerator.advanced_reduce_sum(&data.view());
        assert!(result.is_ok());

        let expected = 15.0;
        let actual = result.unwrap();
        assert!((expected - actual).abs() < 1e-10);
    }
}