Trait SimdUnifiedOps

Source

pub trait SimdUnifiedOps:
    Sized
    + Copy
    + PartialOrd
    + Zero {
Show 128 methods    // Required methods
    fn simd_add(
        a: &ArrayView1<'_, Self>,
        b: &ArrayView1<'_, Self>,
    ) -> Array1<Self>;
    fn simd_sub(
        a: &ArrayView1<'_, Self>,
        b: &ArrayView1<'_, Self>,
    ) -> Array1<Self>;
    fn simd_mul(
        a: &ArrayView1<'_, Self>,
        b: &ArrayView1<'_, Self>,
    ) -> Array1<Self>;
    fn simd_div(
        a: &ArrayView1<'_, Self>,
        b: &ArrayView1<'_, Self>,
    ) -> Array1<Self>;
    fn simd_dot(a: &ArrayView1<'_, Self>, b: &ArrayView1<'_, Self>) -> Self;
    fn simd_gemv(
        a: &ArrayView2<'_, Self>,
        x: &ArrayView1<'_, Self>,
        beta: Self,
        y: &mut Array1<Self>,
    );
    fn simd_gemm(
        alpha: Self,
        a: &ArrayView2<'_, Self>,
        b: &ArrayView2<'_, Self>,
        beta: Self,
        c: &mut Array2<Self>,
    );
    fn simd_norm(a: &ArrayView1<'_, Self>) -> Self;
    fn simd_max(
        a: &ArrayView1<'_, Self>,
        b: &ArrayView1<'_, Self>,
    ) -> Array1<Self>;
    fn simd_min(
        a: &ArrayView1<'_, Self>,
        b: &ArrayView1<'_, Self>,
    ) -> Array1<Self>;
    fn simd_scalar_mul(a: &ArrayView1<'_, Self>, scalar: Self) -> Array1<Self>;
    fn simd_sum(a: &ArrayView1<'_, Self>) -> Self;
    fn simd_mean(a: &ArrayView1<'_, Self>) -> Self;
    fn simd_max_element(a: &ArrayView1<'_, Self>) -> Self;
    fn simd_min_element(a: &ArrayView1<'_, Self>) -> Self;
    fn simd_fma(
        a: &ArrayView1<'_, Self>,
        b: &ArrayView1<'_, Self>,
        c: &ArrayView1<'_, Self>,
    ) -> Array1<Self>;
    fn simd_add_cache_optimized(
        a: &ArrayView1<'_, Self>,
        b: &ArrayView1<'_, Self>,
    ) -> Array1<Self>;
    fn simd_fma_advanced_optimized(
        a: &ArrayView1<'_, Self>,
        b: &ArrayView1<'_, Self>,
        c: &ArrayView1<'_, Self>,
    ) -> Array1<Self>;
    fn simd_add_adaptive(
        a: &ArrayView1<'_, Self>,
        b: &ArrayView1<'_, Self>,
    ) -> Array1<Self>;
    fn simd_transpose(a: &ArrayView2<'_, Self>) -> Array2<Self>;
    fn simd_transpose_blocked(a: &ArrayView2<'_, Self>) -> Array2<Self>;
    fn simd_abs(a: &ArrayView1<'_, Self>) -> Array1<Self>;
    fn simd_sqrt(a: &ArrayView1<'_, Self>) -> Array1<Self>;
    fn simd_exp(a: &ArrayView1<'_, Self>) -> Array1<Self>;
    fn simd_ln(a: &ArrayView1<'_, Self>) -> Array1<Self>;
    fn simd_sin(a: &ArrayView1<'_, Self>) -> Array1<Self>;
    fn simd_cos(a: &ArrayView1<'_, Self>) -> Array1<Self>;
    fn simd_tan(a: &ArrayView1<'_, Self>) -> Array1<Self>;
    fn simd_sinh(a: &ArrayView1<'_, Self>) -> Array1<Self>;
    fn simd_cosh(a: &ArrayView1<'_, Self>) -> Array1<Self>;
    fn simd_tanh(a: &ArrayView1<'_, Self>) -> Array1<Self>;
    fn simd_floor(a: &ArrayView1<'_, Self>) -> Array1<Self>;
    fn simd_ceil(a: &ArrayView1<'_, Self>) -> Array1<Self>;
    fn simd_round(a: &ArrayView1<'_, Self>) -> Array1<Self>;
    fn simd_atan(a: &ArrayView1<'_, Self>) -> Array1<Self>;
    fn simd_asin(a: &ArrayView1<'_, Self>) -> Array1<Self>;
    fn simd_acos(a: &ArrayView1<'_, Self>) -> Array1<Self>;
    fn simd_atan2(
        y: &ArrayView1<'_, Self>,
        x: &ArrayView1<'_, Self>,
    ) -> Array1<Self>;
    fn simd_log10(a: &ArrayView1<'_, Self>) -> Array1<Self>;
    fn simd_log2(a: &ArrayView1<'_, Self>) -> Array1<Self>;
    fn simd_clamp(
        a: &ArrayView1<'_, Self>,
        min: Self,
        max: Self,
    ) -> Array1<Self>;
    fn simd_fract(a: &ArrayView1<'_, Self>) -> Array1<Self>;
    fn simd_trunc(a: &ArrayView1<'_, Self>) -> Array1<Self>;
    fn simd_recip(a: &ArrayView1<'_, Self>) -> Array1<Self>;
    fn simd_powf(base: &ArrayView1<'_, Self>, exp: Self) -> Array1<Self>;
    fn simd_pow(
        base: &ArrayView1<'_, Self>,
        exp: &ArrayView1<'_, Self>,
    ) -> Array1<Self>;
    fn simd_powi(base: &ArrayView1<'_, Self>, n: i32) -> Array1<Self>;
    fn simd_gamma(x: &ArrayView1<'_, Self>) -> Array1<Self>;
    fn simd_exp2(a: &ArrayView1<'_, Self>) -> Array1<Self>;
    fn simd_cbrt(a: &ArrayView1<'_, Self>) -> Array1<Self>;
    fn simd_ln_1p(a: &ArrayView1<'_, Self>) -> Array1<Self>;
    fn simd_exp_m1(a: &ArrayView1<'_, Self>) -> Array1<Self>;
    fn simd_to_radians(a: &ArrayView1<'_, Self>) -> Array1<Self>;
    fn simd_to_degrees(a: &ArrayView1<'_, Self>) -> Array1<Self>;
    fn simd_digamma(a: &ArrayView1<'_, Self>) -> Array1<Self>;
    fn simd_trigamma(a: &ArrayView1<'_, Self>) -> Array1<Self>;
    fn simd_ln_gamma(a: &ArrayView1<'_, Self>) -> Array1<Self>;
    fn simd_erf(a: &ArrayView1<'_, Self>) -> Array1<Self>;
    fn simd_erfc(a: &ArrayView1<'_, Self>) -> Array1<Self>;
    fn simd_erfinv(a: &ArrayView1<'_, Self>) -> Array1<Self>;
    fn simd_erfcinv(a: &ArrayView1<'_, Self>) -> Array1<Self>;
    fn simd_sigmoid(a: &ArrayView1<'_, Self>) -> Array1<Self>;
    fn simd_gelu(a: &ArrayView1<'_, Self>) -> Array1<Self>;
    fn simd_swish(a: &ArrayView1<'_, Self>) -> Array1<Self>;
    fn simd_softplus(a: &ArrayView1<'_, Self>) -> Array1<Self>;
    fn simd_mish(a: &ArrayView1<'_, Self>) -> Array1<Self>;
    fn simd_elu(a: &ArrayView1<'_, Self>, alpha: Self) -> Array1<Self>;
    fn simd_selu(a: &ArrayView1<'_, Self>) -> Array1<Self>;
    fn simd_hardsigmoid(a: &ArrayView1<'_, Self>) -> Array1<Self>;
    fn simd_hardswish(a: &ArrayView1<'_, Self>) -> Array1<Self>;
    fn simd_sinc(a: &ArrayView1<'_, Self>) -> Array1<Self>;
    fn simd_log_softmax(a: &ArrayView1<'_, Self>) -> Array1<Self>;
    fn simd_asinh(a: &ArrayView1<'_, Self>) -> Array1<Self>;
    fn simd_acosh(a: &ArrayView1<'_, Self>) -> Array1<Self>;
    fn simd_atanh(a: &ArrayView1<'_, Self>) -> Array1<Self>;
    fn simd_beta(
        a: &ArrayView1<'_, Self>,
        b: &ArrayView1<'_, Self>,
    ) -> Array1<Self>;
    fn simd_ln_beta(
        a: &ArrayView1<'_, Self>,
        b: &ArrayView1<'_, Self>,
    ) -> Array1<Self>;
    fn simd_lerp(
        a: &ArrayView1<'_, Self>,
        b: &ArrayView1<'_, Self>,
        t: Self,
    ) -> Array1<Self>;
    fn simd_smoothstep(
        edge0: Self,
        edge1: Self,
        x: &ArrayView1<'_, Self>,
    ) -> Array1<Self>;
    fn simd_hypot(
        x: &ArrayView1<'_, Self>,
        y: &ArrayView1<'_, Self>,
    ) -> Array1<Self>;
    fn simd_copysign(
        x: &ArrayView1<'_, Self>,
        y: &ArrayView1<'_, Self>,
    ) -> Array1<Self>;
    fn simd_smootherstep(
        edge0: Self,
        edge1: Self,
        x: &ArrayView1<'_, Self>,
    ) -> Array1<Self>;
    fn simd_logaddexp(
        a: &ArrayView1<'_, Self>,
        b: &ArrayView1<'_, Self>,
    ) -> Array1<Self>;
    fn simd_logit(a: &ArrayView1<'_, Self>) -> Array1<Self>;
    fn simd_square(a: &ArrayView1<'_, Self>) -> Array1<Self>;
    fn simd_rsqrt(a: &ArrayView1<'_, Self>) -> Array1<Self>;
    fn simd_sincos(a: &ArrayView1<'_, Self>) -> (Array1<Self>, Array1<Self>);
    fn simd_expm1(a: &ArrayView1<'_, Self>) -> Array1<Self>;
    fn simd_log1p(a: &ArrayView1<'_, Self>) -> Array1<Self>;
    fn simd_sum_squares(a: &ArrayView1<'_, Self>) -> Self;
    fn simd_multiply(
        a: &ArrayView1<'_, Self>,
        b: &ArrayView1<'_, Self>,
    ) -> Array1<Self>;
    fn simd_available() -> bool;
    fn simd_sub_f32_ultra(
        a: &ArrayView1<'_, Self>,
        b: &ArrayView1<'_, Self>,
        result: &mut ArrayViewMut1<'_, Self>,
    );
    fn simd_mul_f32_ultra(
        a: &ArrayView1<'_, Self>,
        b: &ArrayView1<'_, Self>,
        result: &mut ArrayViewMut1<'_, Self>,
    );
    fn simd_sum_cubes(a: &ArrayView1<'_, Self>) -> Self;
    fn simd_div_f32_ultra(
        a: &ArrayView1<'_, Self>,
        b: &ArrayView1<'_, Self>,
        result: &mut ArrayViewMut1<'_, Self>,
    );
    fn simd_sin_f32_ultra(
        a: &ArrayView1<'_, Self>,
        result: &mut ArrayViewMut1<'_, Self>,
    );
    fn simd_add_f32_ultra(
        a: &ArrayView1<'_, Self>,
        b: &ArrayView1<'_, Self>,
        result: &mut ArrayViewMut1<'_, Self>,
    );
    fn simd_fma_f32_ultra(
        a: &ArrayView1<'_, Self>,
        b: &ArrayView1<'_, Self>,
        c: &ArrayView1<'_, Self>,
        result: &mut ArrayViewMut1<'_, Self>,
    );
    fn simd_pow_f32_ultra(
        a: &ArrayView1<'_, Self>,
        b: &ArrayView1<'_, Self>,
        result: &mut ArrayViewMut1<'_, Self>,
    );
    fn simd_exp_f32_ultra(
        a: &ArrayView1<'_, Self>,
        result: &mut ArrayViewMut1<'_, Self>,
    );
    fn simd_cos_f32_ultra(
        a: &ArrayView1<'_, Self>,
        result: &mut ArrayViewMut1<'_, Self>,
    );
    fn simd_dot_f32_ultra(
        a: &ArrayView1<'_, Self>,
        b: &ArrayView1<'_, Self>,
    ) -> Self;
    fn simd_variance(a: &ArrayView1<'_, Self>) -> Self;
    fn simd_std(a: &ArrayView1<'_, Self>) -> Self;
    fn simd_norm_l1(a: &ArrayView1<'_, Self>) -> Self;
    fn simd_norm_linf(a: &ArrayView1<'_, Self>) -> Self;
    fn simd_cosine_similarity(
        a: &ArrayView1<'_, Self>,
        b: &ArrayView1<'_, Self>,
    ) -> Self;
    fn simd_distance_euclidean(
        a: &ArrayView1<'_, Self>,
        b: &ArrayView1<'_, Self>,
    ) -> Self;
    fn simd_distance_manhattan(
        a: &ArrayView1<'_, Self>,
        b: &ArrayView1<'_, Self>,
    ) -> Self;
    fn simd_distance_chebyshev(
        a: &ArrayView1<'_, Self>,
        b: &ArrayView1<'_, Self>,
    ) -> Self;
    fn simd_distance_cosine(
        a: &ArrayView1<'_, Self>,
        b: &ArrayView1<'_, Self>,
    ) -> Self;
    fn simd_weighted_sum(
        values: &ArrayView1<'_, Self>,
        weights: &ArrayView1<'_, Self>,
    ) -> Self;
    fn simd_weighted_mean(
        values: &ArrayView1<'_, Self>,
        weights: &ArrayView1<'_, Self>,
    ) -> Self;
    fn simd_argmin(a: &ArrayView1<'_, Self>) -> Option<usize>;
    fn simd_argmax(a: &ArrayView1<'_, Self>) -> Option<usize>;
    fn simd_clip(
        a: &ArrayView1<'_, Self>,
        min_val: Self,
        max_val: Self,
    ) -> Array1<Self>;
    fn simd_log_sum_exp(a: &ArrayView1<'_, Self>) -> Self;
    fn simd_softmax(a: &ArrayView1<'_, Self>) -> Array1<Self>;
    fn simd_cumsum(a: &ArrayView1<'_, Self>) -> Array1<Self>;
    fn simd_cumprod(a: &ArrayView1<'_, Self>) -> Array1<Self>;
    fn simd_diff(a: &ArrayView1<'_, Self>) -> Array1<Self>;
    fn simd_sign(a: &ArrayView1<'_, Self>) -> Array1<Self>;
    fn simd_relu(a: &ArrayView1<'_, Self>) -> Array1<Self>;
    fn simd_leaky_relu(a: &ArrayView1<'_, Self>, alpha: Self) -> Array1<Self>;
    fn simd_normalize(a: &ArrayView1<'_, Self>) -> Array1<Self>;
    fn simd_standardize(a: &ArrayView1<'_, Self>) -> Array1<Self>;

    // Provided method
    fn simd_sum_f32_ultra(a: &ArrayView1<'_, Self>) -> Self { ... }
}

Expand description

Unified SIMD operations trait

Required Methods§

Source

fn simd_add(a: &ArrayView1<'_, Self>, b: &ArrayView1<'_, Self>) -> Array1<Self>

Element-wise addition

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fn simd_sub(a: &ArrayView1<'_, Self>, b: &ArrayView1<'_, Self>) -> Array1<Self>

Element-wise subtraction

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fn simd_mul(a: &ArrayView1<'_, Self>, b: &ArrayView1<'_, Self>) -> Array1<Self>

Element-wise multiplication

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fn simd_div(a: &ArrayView1<'_, Self>, b: &ArrayView1<'_, Self>) -> Array1<Self>

Element-wise division

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fn simd_dot(a: &ArrayView1<'_, Self>, b: &ArrayView1<'_, Self>) -> Self

Dot product

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fn simd_gemv( a: &ArrayView2<'_, Self>, x: &ArrayView1<'_, Self>, beta: Self, y: &mut Array1<Self>, )

Matrix-vector multiplication (GEMV)

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fn simd_gemm( alpha: Self, a: &ArrayView2<'_, Self>, b: &ArrayView2<'_, Self>, beta: Self, c: &mut Array2<Self>, )

Matrix-matrix multiplication (GEMM)

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fn simd_norm(a: &ArrayView1<'_, Self>) -> Self

Vector norm (L2)

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fn simd_max(a: &ArrayView1<'_, Self>, b: &ArrayView1<'_, Self>) -> Array1<Self>

Element-wise maximum

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fn simd_min(a: &ArrayView1<'_, Self>, b: &ArrayView1<'_, Self>) -> Array1<Self>

Element-wise minimum

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fn simd_scalar_mul(a: &ArrayView1<'_, Self>, scalar: Self) -> Array1<Self>

Scalar multiplication

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fn simd_sum(a: &ArrayView1<'_, Self>) -> Self

Sum reduction

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fn simd_mean(a: &ArrayView1<'_, Self>) -> Self

Mean reduction

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fn simd_max_element(a: &ArrayView1<'_, Self>) -> Self

Find maximum element

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fn simd_min_element(a: &ArrayView1<'_, Self>) -> Self

Find minimum element

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fn simd_fma( a: &ArrayView1<'_, Self>, b: &ArrayView1<'_, Self>, c: &ArrayView1<'_, Self>, ) -> Array1<Self>

Fused multiply-add: a * b + c

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fn simd_add_cache_optimized( a: &ArrayView1<'_, Self>, b: &ArrayView1<'_, Self>, ) -> Array1<Self>

Enhanced cache-optimized addition for large arrays

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fn simd_fma_advanced_optimized( a: &ArrayView1<'_, Self>, b: &ArrayView1<'_, Self>, c: &ArrayView1<'_, Self>, ) -> Array1<Self>

Advanced-optimized fused multiply-add for maximum performance

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fn simd_add_adaptive( a: &ArrayView1<'_, Self>, b: &ArrayView1<'_, Self>, ) -> Array1<Self>

Adaptive SIMD operation that selects optimal implementation

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fn simd_transpose(a: &ArrayView2<'_, Self>) -> Array2<Self>

Matrix transpose

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fn simd_transpose_blocked(a: &ArrayView2<'_, Self>) -> Array2<Self>

Cache-optimized blocked matrix transpose Uses L1 cache-friendly block sizes for improved memory access patterns. Expected 3-5x speedup for large matrices (>512x512).

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fn simd_abs(a: &ArrayView1<'_, Self>) -> Array1<Self>

Element-wise absolute value

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fn simd_sqrt(a: &ArrayView1<'_, Self>) -> Array1<Self>

Element-wise square root

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fn simd_exp(a: &ArrayView1<'_, Self>) -> Array1<Self>

Element-wise exponential (e^x)

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fn simd_ln(a: &ArrayView1<'_, Self>) -> Array1<Self>

Element-wise natural logarithm (ln(x))

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fn simd_sin(a: &ArrayView1<'_, Self>) -> Array1<Self>

Element-wise sine (sin(x))

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fn simd_cos(a: &ArrayView1<'_, Self>) -> Array1<Self>

Element-wise cosine (cos(x))

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fn simd_tan(a: &ArrayView1<'_, Self>) -> Array1<Self>

Element-wise tangent (tan(x))

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fn simd_sinh(a: &ArrayView1<'_, Self>) -> Array1<Self>

Element-wise hyperbolic sine (sinh(x))

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fn simd_cosh(a: &ArrayView1<'_, Self>) -> Array1<Self>

Element-wise hyperbolic cosine (cosh(x))

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fn simd_tanh(a: &ArrayView1<'_, Self>) -> Array1<Self>

Element-wise hyperbolic tangent (tanh(x))

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fn simd_floor(a: &ArrayView1<'_, Self>) -> Array1<Self>

Element-wise floor (largest integer <= x)

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fn simd_ceil(a: &ArrayView1<'_, Self>) -> Array1<Self>

Element-wise ceiling (smallest integer >= x)

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fn simd_round(a: &ArrayView1<'_, Self>) -> Array1<Self>

Element-wise rounding to nearest integer

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fn simd_atan(a: &ArrayView1<'_, Self>) -> Array1<Self>

Element-wise arctangent (atan(x))

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fn simd_asin(a: &ArrayView1<'_, Self>) -> Array1<Self>

Element-wise arcsine (asin(x))

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fn simd_acos(a: &ArrayView1<'_, Self>) -> Array1<Self>

Element-wise arccosine (acos(x))

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fn simd_atan2( y: &ArrayView1<'_, Self>, x: &ArrayView1<'_, Self>, ) -> Array1<Self>

Element-wise two-argument arctangent (atan2(y, x))

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fn simd_log10(a: &ArrayView1<'_, Self>) -> Array1<Self>

Element-wise base-10 logarithm (log10(x))

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fn simd_log2(a: &ArrayView1<'_, Self>) -> Array1<Self>

Element-wise base-2 logarithm (log2(x))

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fn simd_clamp(a: &ArrayView1<'_, Self>, min: Self, max: Self) -> Array1<Self>

Element-wise clamp (constrain values to [min, max])

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fn simd_fract(a: &ArrayView1<'_, Self>) -> Array1<Self>

Element-wise fractional part (x - trunc(x))

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fn simd_trunc(a: &ArrayView1<'_, Self>) -> Array1<Self>

Element-wise trunc (round toward zero)

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fn simd_recip(a: &ArrayView1<'_, Self>) -> Array1<Self>

Element-wise reciprocal (1/x)

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fn simd_powf(base: &ArrayView1<'_, Self>, exp: Self) -> Array1<Self>

Element-wise power with scalar exponent (base^exp)

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fn simd_pow( base: &ArrayView1<'_, Self>, exp: &ArrayView1<'_, Self>, ) -> Array1<Self>

Element-wise power with array exponent (base[i]^exp[i])

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fn simd_powi(base: &ArrayView1<'_, Self>, n: i32) -> Array1<Self>

Element-wise power with integer exponent (base^n)

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fn simd_gamma(x: &ArrayView1<'_, Self>) -> Array1<Self>

Element-wise gamma function Γ(x)

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fn simd_exp2(a: &ArrayView1<'_, Self>) -> Array1<Self>

Element-wise 2^x (base-2 exponential)

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fn simd_cbrt(a: &ArrayView1<'_, Self>) -> Array1<Self>

Element-wise cube root (cbrt(x) = x^(1/3))

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fn simd_ln_1p(a: &ArrayView1<'_, Self>) -> Array1<Self>

Element-wise ln(1+x) (numerically stable for small x)

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fn simd_exp_m1(a: &ArrayView1<'_, Self>) -> Array1<Self>

Element-wise exp(x)-1 (numerically stable for small x)

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fn simd_to_radians(a: &ArrayView1<'_, Self>) -> Array1<Self>

Element-wise conversion from degrees to radians (x * π / 180)

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fn simd_to_degrees(a: &ArrayView1<'_, Self>) -> Array1<Self>

Element-wise conversion from radians to degrees (x * 180 / π)

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fn simd_digamma(a: &ArrayView1<'_, Self>) -> Array1<Self>

Element-wise digamma function ψ(x) = d/dx ln(Γ(x))

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fn simd_trigamma(a: &ArrayView1<'_, Self>) -> Array1<Self>

Element-wise trigamma function ψ’(x) = d²/dx² ln(Γ(x)) The second derivative of log-gamma, critical for Fisher information in Bayesian inference.

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fn simd_ln_gamma(a: &ArrayView1<'_, Self>) -> Array1<Self>

Element-wise log-gamma function ln(Γ(x)) More numerically stable than computing gamma(x).ln() - used extensively in statistical distributions.

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fn simd_erf(a: &ArrayView1<'_, Self>) -> Array1<Self>

Element-wise error function erf(x) = (2/√π) ∫₀ˣ e^(-t²) dt Critical for normal distribution CDF: Φ(x) = 0.5 * (1 + erf(x/√2)) Properties: erf(0)=0, erf(∞)=1, erf(-x)=-erf(x)

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fn simd_erfc(a: &ArrayView1<'_, Self>) -> Array1<Self>

Element-wise complementary error function erfc(x) = 1 - erf(x) More numerically stable than 1 - erf(x) for large x

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fn simd_erfinv(a: &ArrayView1<'_, Self>) -> Array1<Self>

Element-wise inverse error function erfinv(y) such that erf(erfinv(y)) = y Critical for inverse normal CDF (probit function): Φ⁻¹(p) = √2 * erfinv(2p - 1) Domain: (-1, 1), Range: (-∞, ∞) Properties: erfinv(0)=0, erfinv(-y)=-erfinv(y) (odd function)

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fn simd_erfcinv(a: &ArrayView1<'_, Self>) -> Array1<Self>

Element-wise inverse complementary error function erfcinv(y) such that erfc(erfcinv(y)) = y More numerically stable than erfinv(1-y) for y close to 0 Domain: (0, 2), Range: (-∞, ∞)

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fn simd_sigmoid(a: &ArrayView1<'_, Self>) -> Array1<Self>

Element-wise sigmoid (logistic) function: σ(x) = 1 / (1 + exp(-x)) Critical for neural networks, logistic regression, and probability modeling Range: (0, 1), σ(0) = 0.5, σ(-∞) = 0, σ(+∞) = 1 Properties: σ(-x) = 1 - σ(x), derivative σ’(x) = σ(x)(1 - σ(x))

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fn simd_gelu(a: &ArrayView1<'_, Self>) -> Array1<Self>

Element-wise GELU (Gaussian Error Linear Unit) activation function GELU(x) = x * Φ(x) = x * 0.5 * (1 + erf(x / √2)) Where Φ(x) is the standard normal CDF Critical for Transformer models (BERT, GPT, etc.) Properties: GELU(0) = 0, smooth approximation of ReLU

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fn simd_swish(a: &ArrayView1<'_, Self>) -> Array1<Self>

Element-wise Swish (SiLU - Sigmoid Linear Unit) activation function Swish(x) = x * sigmoid(x) = x / (1 + exp(-x)) Self-gated activation discovered via neural architecture search Used in EfficientNet, GPT-NeoX, and many modern architectures Properties: smooth, non-monotonic, self-gating, unbounded above

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fn simd_softplus(a: &ArrayView1<'_, Self>) -> Array1<Self>

Element-wise Softplus activation function Softplus(x) = ln(1 + exp(x)) Smooth approximation of ReLU Used in probabilistic models, Bayesian deep learning, smooth counting Properties: softplus(0) = ln(2) ≈ 0.693, always positive, derivative = sigmoid(x)

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fn simd_mish(a: &ArrayView1<'_, Self>) -> Array1<Self>

Element-wise Mish activation function Mish(x) = x * tanh(softplus(x)) = x * tanh(ln(1 + exp(x))) Self-regularized non-monotonic activation function Used in YOLOv4, modern object detection, and neural architectures Properties: smooth, non-monotonic, Mish(0) = 0, unbounded above

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fn simd_elu(a: &ArrayView1<'_, Self>, alpha: Self) -> Array1<Self>

Element-wise ELU (Exponential Linear Unit) activation function ELU(x, α) = x if x >= 0, α * (exp(x) - 1) if x < 0 Helps with vanishing gradients and faster learning Used in deep neural networks for smoother outputs Properties: smooth, continuous derivative, bounded below by -α

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fn simd_selu(a: &ArrayView1<'_, Self>) -> Array1<Self>

SELU activation function (Scaled Exponential Linear Unit)

SELU(x) = λ * (x if x > 0, α * (exp(x) - 1) if x <= 0) where λ ≈ 1.0507 and α ≈ 1.6733 (fixed constants) Self-normalizing: preserves mean=0, variance=1 through layers Used in Self-Normalizing Neural Networks (SNNs) Eliminates need for BatchNorm when using LeCun Normal initialization

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fn simd_hardsigmoid(a: &ArrayView1<'_, Self>) -> Array1<Self>

Hardsigmoid activation function

Hardsigmoid(x) = clip((x + 3) / 6, 0, 1) Piecewise linear approximation of sigmoid Used in MobileNetV3 for efficient inference Avoids expensive exp() computation

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fn simd_hardswish(a: &ArrayView1<'_, Self>) -> Array1<Self>

Hardswish activation function

Hardswish(x) = x * hardsigmoid(x) = x * clip((x + 3) / 6, 0, 1) Piecewise linear approximation of Swish Used in MobileNetV3 for efficient inference Avoids expensive exp() computation while maintaining self-gating

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fn simd_sinc(a: &ArrayView1<'_, Self>) -> Array1<Self>

Sinc function (normalized)

sinc(x) = sin(πx) / (πx) for x ≠ 0, sinc(0) = 1 Critical for signal processing, windowing, interpolation Properties: sinc(n) = 0 for all non-zero integers n

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fn simd_log_softmax(a: &ArrayView1<'_, Self>) -> Array1<Self>

Log-softmax function for numerically stable probability computation

log_softmax(x_i) = x_i - log(Σ_j exp(x_j)) Critical for neural networks, especially cross-entropy loss More numerically stable than computing log(softmax(x)) Used in Transformers, LLMs, and classification networks

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fn simd_asinh(a: &ArrayView1<'_, Self>) -> Array1<Self>

Inverse hyperbolic sine: asinh(x) = ln(x + √(x² + 1))

Domain: (-∞, +∞), Range: (-∞, +∞) Used in: hyperbolic geometry, conformal mapping, special relativity (rapidity)

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fn simd_acosh(a: &ArrayView1<'_, Self>) -> Array1<Self>

Inverse hyperbolic cosine: acosh(x) = ln(x + √(x² - 1))

Domain: [1, +∞), Range: [0, +∞) Returns NaN for x < 1 Used in: hyperbolic geometry, distance calculations, special relativity

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fn simd_atanh(a: &ArrayView1<'_, Self>) -> Array1<Self>

Inverse hyperbolic tangent: atanh(x) = 0.5 * ln((1+x)/(1-x))

Domain: (-1, 1), Range: (-∞, +∞) Returns ±∞ at x = ±1, NaN for |x| > 1 Used in: statistical transformations (Fisher’s z), probability

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fn simd_beta(a: &ArrayView1<'_, Self>, b: &ArrayView1<'_, Self>) -> Array1<Self>

Beta function: B(a, b) = Γ(a)Γ(b)/Γ(a+b)

The beta function is fundamental for:

Beta distribution (Bayesian priors)
Binomial coefficients: C(n,k) = 1/(n+1)/B(n-k+1, k+1)
Statistical hypothesis testing
Incomplete beta function (regularized)

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fn simd_ln_beta( a: &ArrayView1<'_, Self>, b: &ArrayView1<'_, Self>, ) -> Array1<Self>

Log-Beta function: ln(B(a, b)) = ln(Γ(a)) + ln(Γ(b)) - ln(Γ(a+b))

More numerically stable than computing B(a,b) for large arguments. Returns ln(B(a,b)) for each pair of inputs.

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fn simd_lerp( a: &ArrayView1<'_, Self>, b: &ArrayView1<'_, Self>, t: Self, ) -> Array1<Self>

Linear interpolation: lerp(a, b, t) = a + t * (b - a) = a * (1 - t) + b * t

Computes element-wise linear interpolation between arrays a and b using interpolation parameter t. When t=0, returns a; when t=1, returns b.

Critical for:

Animation blending (skeletal animation, morph targets)
Quaternion SLERP approximation (for small angles)
Gradient computation in neural networks
Smooth parameter transitions
Color blending and image processing

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fn simd_smoothstep( edge0: Self, edge1: Self, x: &ArrayView1<'_, Self>, ) -> Array1<Self>

Smoothstep interpolation: smoothstep(edge0, edge1, x)

Returns smooth Hermite interpolation between 0 and 1 when edge0 < x < edge1.

Returns 0 if x <= edge0
Returns 1 if x >= edge1
Returns smooth curve: 3t² - 2t³ where t = (x - edge0) / (edge1 - edge0)

Critical for:

Shader programming (lighting, transitions)
Activation function variants
Smooth threshold functions
Anti-aliasing and blending

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fn simd_hypot( x: &ArrayView1<'_, Self>, y: &ArrayView1<'_, Self>, ) -> Array1<Self>

Hypotenuse: hypot(x, y) = sqrt(x² + y²)

Computes element-wise hypotenuse without overflow/underflow issues. Uses the standard library implementation which handles extreme values.

Critical for:

Distance calculations in 2D/3D
Computing vector magnitudes
Graphics and physics simulations
Complex number modulus: |a+bi| = hypot(a, b)

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fn simd_copysign( x: &ArrayView1<'_, Self>, y: &ArrayView1<'_, Self>, ) -> Array1<Self>

Copysign: copysign(x, y) returns x with the sign of y

For each element, returns the magnitude of x with the sign of y.

copysign(1.0, -2.0) = -1.0
copysign(-3.0, 4.0) = 3.0

Critical for:

Sign manipulation in numerical algorithms
Implementing special functions (e.g., reflection formula)
Gradient sign propagation

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fn simd_smootherstep( edge0: Self, edge1: Self, x: &ArrayView1<'_, Self>, ) -> Array1<Self>

Smootherstep (Ken Perlin’s improved smoothstep): 6t⁵ - 15t⁴ + 10t³

An improved version of smoothstep with second-order continuous derivatives. The first AND second derivatives are zero at the boundaries.

Critical for:

Perlin noise and procedural generation
High-quality animation easing
Shader programming (better lighting transitions)
Gradient-based optimization (smoother loss landscapes)

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fn simd_logaddexp( a: &ArrayView1<'_, Self>, b: &ArrayView1<'_, Self>, ) -> Array1<Self>

Logaddexp: log(exp(a) + exp(b)) computed in a numerically stable way

Uses the identity: log(exp(a) + exp(b)) = max(a,b) + log(1 + exp(-|a-b|)) This avoids overflow/underflow for large positive or negative values.

Critical for:

Log-probability computations (Bayesian inference)
Log-likelihood calculations in ML
Hidden Markov Model forward/backward algorithms
Neural network loss functions (cross-entropy)

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fn simd_logit(a: &ArrayView1<'_, Self>) -> Array1<Self>

Logit function: log(p / (1-p)) - inverse of sigmoid

Maps probabilities in (0, 1) to log-odds in (-∞, +∞). The logit function is the inverse of the sigmoid (logistic) function.

Critical for:

Logistic regression (log-odds interpretation)
Probability calibration
Converting probabilities to unbounded space for optimization
Statistical modeling (link functions)

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fn simd_square(a: &ArrayView1<'_, Self>) -> Array1<Self>

Element-wise square: x²

More efficient than simd_pow(x, 2) or simd_mul(x, x) as it’s a single multiplication.

Critical for:

Variance computation: E[X²] - E[X]²
Distance calculations: ||a - b||² = (a - b)²
Neural network loss functions (MSE)
Physics simulations (kinetic energy: ½mv²)

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fn simd_rsqrt(a: &ArrayView1<'_, Self>) -> Array1<Self>

Inverse square root: 1/sqrt(x)

More efficient than simd_div(1, simd_sqrt(x)) for normalization operations.

Critical for:

Vector normalization: v * rsqrt(dot(v,v))
Graphics (lighting, physics simulations)
Layer normalization in neural networks
Quaternion normalization

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fn simd_sincos(a: &ArrayView1<'_, Self>) -> (Array1<Self>, Array1<Self>)

Simultaneous sin and cos: returns (sin(x), cos(x))

More efficient than calling sin and cos separately when both are needed. Returns a tuple of two arrays.

Critical for:

Rotation matrices (2D and 3D)
Fourier transforms
Wave simulations
Animation and physics

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fn simd_expm1(a: &ArrayView1<'_, Self>) -> Array1<Self>

Numerically stable exp(x) - 1

Returns exp(x) - 1 accurately for small x values where exp(x) ≈ 1. For small x, the direct calculation exp(x) - 1 suffers from catastrophic cancellation.

Critical for:

Financial calculations (compound interest for small rates)
Numerical integration of differential equations
Statistical distributions (Poisson, exponential)
Machine learning (softplus, log-sum-exp)

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fn simd_log1p(a: &ArrayView1<'_, Self>) -> Array1<Self>

Numerically stable ln(1 + x)

Returns ln(1 + x) accurately for small x values where 1 + x ≈ 1. For small x, the direct calculation ln(1 + x) suffers from catastrophic cancellation.

Critical for:

Log-probability calculations (log(1 - p) for small p)
Numerical integration
Statistical distributions
Machine learning (binary cross-entropy loss)

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fn simd_sum_squares(a: &ArrayView1<'_, Self>) -> Self

Sum of squares

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fn simd_multiply( a: &ArrayView1<'_, Self>, b: &ArrayView1<'_, Self>, ) -> Array1<Self>

Element-wise multiplication (alias for simd_mul)

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fn simd_available() -> bool

Check if SIMD is available for this type

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fn simd_sub_f32_ultra( a: &ArrayView1<'_, Self>, b: &ArrayView1<'_, Self>, result: &mut ArrayViewMut1<'_, Self>, )

Ultra-optimized subtraction (alias for simd_sub for compatibility)

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fn simd_mul_f32_ultra( a: &ArrayView1<'_, Self>, b: &ArrayView1<'_, Self>, result: &mut ArrayViewMut1<'_, Self>, )

Ultra-optimized multiplication (alias for simd_mul for compatibility)

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fn simd_sum_cubes(a: &ArrayView1<'_, Self>) -> Self

Ultra-optimized cubes sum (power 3 sum)

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fn simd_div_f32_ultra( a: &ArrayView1<'_, Self>, b: &ArrayView1<'_, Self>, result: &mut ArrayViewMut1<'_, Self>, )

Ultra-optimized division (alias for simd_div for compatibility)

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fn simd_sin_f32_ultra( a: &ArrayView1<'_, Self>, result: &mut ArrayViewMut1<'_, Self>, )

Ultra-optimized sine function

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fn simd_add_f32_ultra( a: &ArrayView1<'_, Self>, b: &ArrayView1<'_, Self>, result: &mut ArrayViewMut1<'_, Self>, )

Ultra-optimized addition (alias for simd_add for compatibility)

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fn simd_fma_f32_ultra( a: &ArrayView1<'_, Self>, b: &ArrayView1<'_, Self>, c: &ArrayView1<'_, Self>, result: &mut ArrayViewMut1<'_, Self>, )

Ultra-optimized fused multiply-add

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fn simd_pow_f32_ultra( a: &ArrayView1<'_, Self>, b: &ArrayView1<'_, Self>, result: &mut ArrayViewMut1<'_, Self>, )

Ultra-optimized power function

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fn simd_exp_f32_ultra( a: &ArrayView1<'_, Self>, result: &mut ArrayViewMut1<'_, Self>, )

Ultra-optimized exponential function

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fn simd_cos_f32_ultra( a: &ArrayView1<'_, Self>, result: &mut ArrayViewMut1<'_, Self>, )

Ultra-optimized cosine function

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fn simd_dot_f32_ultra( a: &ArrayView1<'_, Self>, b: &ArrayView1<'_, Self>, ) -> Self

Ultra-optimized dot product

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fn simd_variance(a: &ArrayView1<'_, Self>) -> Self

Variance (population variance)

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fn simd_std(a: &ArrayView1<'_, Self>) -> Self

Standard deviation

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fn simd_norm_l1(a: &ArrayView1<'_, Self>) -> Self

L1 norm (Manhattan norm)

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fn simd_norm_linf(a: &ArrayView1<'_, Self>) -> Self

L∞ norm (Chebyshev norm / max absolute)

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fn simd_cosine_similarity( a: &ArrayView1<'_, Self>, b: &ArrayView1<'_, Self>, ) -> Self

Cosine similarity between two vectors

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fn simd_distance_euclidean( a: &ArrayView1<'_, Self>, b: &ArrayView1<'_, Self>, ) -> Self

Euclidean distance between two vectors

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fn simd_distance_manhattan( a: &ArrayView1<'_, Self>, b: &ArrayView1<'_, Self>, ) -> Self

Manhattan distance between two vectors

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fn simd_distance_chebyshev( a: &ArrayView1<'_, Self>, b: &ArrayView1<'_, Self>, ) -> Self

Chebyshev distance between two vectors

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fn simd_distance_cosine( a: &ArrayView1<'_, Self>, b: &ArrayView1<'_, Self>, ) -> Self

Cosine distance (1 - cosine_similarity)

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fn simd_weighted_sum( values: &ArrayView1<'_, Self>, weights: &ArrayView1<'_, Self>, ) -> Self

Weighted sum

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fn simd_weighted_mean( values: &ArrayView1<'_, Self>, weights: &ArrayView1<'_, Self>, ) -> Self

Weighted mean

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fn simd_argmin(a: &ArrayView1<'_, Self>) -> Option<usize>

Find index of minimum element (argmin)

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fn simd_argmax(a: &ArrayView1<'_, Self>) -> Option<usize>

Find index of maximum element (argmax)

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fn simd_clip( a: &ArrayView1<'_, Self>, min_val: Self, max_val: Self, ) -> Array1<Self>

Clip values to [min_val, max_val] range

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fn simd_log_sum_exp(a: &ArrayView1<'_, Self>) -> Self

Log-sum-exp for numerically stable softmax computation

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fn simd_softmax(a: &ArrayView1<'_, Self>) -> Array1<Self>

Softmax for probability distribution (softmax = exp(x - log_sum_exp(x)))

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fn simd_cumsum(a: &ArrayView1<'_, Self>) -> Array1<Self>

Cumulative sum

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fn simd_cumprod(a: &ArrayView1<'_, Self>) -> Array1<Self>

Cumulative product

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fn simd_diff(a: &ArrayView1<'_, Self>) -> Array1<Self>

First-order difference (a[i+1] - a[i])

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fn simd_sign(a: &ArrayView1<'_, Self>) -> Array1<Self>

Sign function: returns -1 for negative, 0 for zero, +1 for positive

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fn simd_relu(a: &ArrayView1<'_, Self>) -> Array1<Self>

ReLU activation: max(0, x)

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fn simd_leaky_relu(a: &ArrayView1<'_, Self>, alpha: Self) -> Array1<Self>

Leaky ReLU: x if x > 0 else alpha * x

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fn simd_normalize(a: &ArrayView1<'_, Self>) -> Array1<Self>

L2 normalization (unit vector)

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fn simd_standardize(a: &ArrayView1<'_, Self>) -> Array1<Self>

Standardization: (x - mean) / std

Provided Methods§

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fn simd_sum_f32_ultra(a: &ArrayView1<'_, Self>) -> Self

Ultra-optimized sum reduction (alias for simd_sum for compatibility)

Dyn Compatibility§

This trait is not dyn compatible.

In older versions of Rust, dyn compatibility was called "object safety", so this trait is not object safe.

Trait SimdUnifiedOps Copy item path

Required Methods§

fn simd_add(a: &ArrayView1<'_, Self>, b: &ArrayView1<'_, Self>) -> Array1<Self>

fn simd_sub(a: &ArrayView1<'_, Self>, b: &ArrayView1<'_, Self>) -> Array1<Self>

fn simd_mul(a: &ArrayView1<'_, Self>, b: &ArrayView1<'_, Self>) -> Array1<Self>

fn simd_div(a: &ArrayView1<'_, Self>, b: &ArrayView1<'_, Self>) -> Array1<Self>

fn simd_dot(a: &ArrayView1<'_, Self>, b: &ArrayView1<'_, Self>) -> Self

fn simd_gemv( a: &ArrayView2<'_, Self>, x: &ArrayView1<'_, Self>, beta: Self, y: &mut Array1<Self>, )

fn simd_gemm( alpha: Self, a: &ArrayView2<'_, Self>, b: &ArrayView2<'_, Self>, beta: Self, c: &mut Array2<Self>, )

fn simd_norm(a: &ArrayView1<'_, Self>) -> Self

fn simd_max(a: &ArrayView1<'_, Self>, b: &ArrayView1<'_, Self>) -> Array1<Self>

fn simd_min(a: &ArrayView1<'_, Self>, b: &ArrayView1<'_, Self>) -> Array1<Self>

fn simd_scalar_mul(a: &ArrayView1<'_, Self>, scalar: Self) -> Array1<Self>

fn simd_sum(a: &ArrayView1<'_, Self>) -> Self

fn simd_mean(a: &ArrayView1<'_, Self>) -> Self

fn simd_max_element(a: &ArrayView1<'_, Self>) -> Self

fn simd_min_element(a: &ArrayView1<'_, Self>) -> Self

fn simd_fma( a: &ArrayView1<'_, Self>, b: &ArrayView1<'_, Self>, c: &ArrayView1<'_, Self>, ) -> Array1<Self>

fn simd_add_cache_optimized( a: &ArrayView1<'_, Self>, b: &ArrayView1<'_, Self>, ) -> Array1<Self>

fn simd_fma_advanced_optimized( a: &ArrayView1<'_, Self>, b: &ArrayView1<'_, Self>, c: &ArrayView1<'_, Self>, ) -> Array1<Self>

fn simd_add_adaptive( a: &ArrayView1<'_, Self>, b: &ArrayView1<'_, Self>, ) -> Array1<Self>

fn simd_transpose(a: &ArrayView2<'_, Self>) -> Array2<Self>

fn simd_transpose_blocked(a: &ArrayView2<'_, Self>) -> Array2<Self>

fn simd_abs(a: &ArrayView1<'_, Self>) -> Array1<Self>

fn simd_sqrt(a: &ArrayView1<'_, Self>) -> Array1<Self>

fn simd_exp(a: &ArrayView1<'_, Self>) -> Array1<Self>

fn simd_ln(a: &ArrayView1<'_, Self>) -> Array1<Self>

fn simd_sin(a: &ArrayView1<'_, Self>) -> Array1<Self>

fn simd_cos(a: &ArrayView1<'_, Self>) -> Array1<Self>

fn simd_tan(a: &ArrayView1<'_, Self>) -> Array1<Self>

fn simd_sinh(a: &ArrayView1<'_, Self>) -> Array1<Self>

fn simd_cosh(a: &ArrayView1<'_, Self>) -> Array1<Self>

fn simd_tanh(a: &ArrayView1<'_, Self>) -> Array1<Self>

fn simd_floor(a: &ArrayView1<'_, Self>) -> Array1<Self>

fn simd_ceil(a: &ArrayView1<'_, Self>) -> Array1<Self>

fn simd_round(a: &ArrayView1<'_, Self>) -> Array1<Self>

fn simd_atan(a: &ArrayView1<'_, Self>) -> Array1<Self>

fn simd_asin(a: &ArrayView1<'_, Self>) -> Array1<Self>

fn simd_acos(a: &ArrayView1<'_, Self>) -> Array1<Self>

fn simd_atan2( y: &ArrayView1<'_, Self>, x: &ArrayView1<'_, Self>, ) -> Array1<Self>

fn simd_log10(a: &ArrayView1<'_, Self>) -> Array1<Self>

fn simd_log2(a: &ArrayView1<'_, Self>) -> Array1<Self>

fn simd_clamp(a: &ArrayView1<'_, Self>, min: Self, max: Self) -> Array1<Self>

fn simd_fract(a: &ArrayView1<'_, Self>) -> Array1<Self>

fn simd_trunc(a: &ArrayView1<'_, Self>) -> Array1<Self>

fn simd_recip(a: &ArrayView1<'_, Self>) -> Array1<Self>

fn simd_powf(base: &ArrayView1<'_, Self>, exp: Self) -> Array1<Self>

fn simd_pow( base: &ArrayView1<'_, Self>, exp: &ArrayView1<'_, Self>, ) -> Array1<Self>

fn simd_powi(base: &ArrayView1<'_, Self>, n: i32) -> Array1<Self>

fn simd_gamma(x: &ArrayView1<'_, Self>) -> Array1<Self>

fn simd_exp2(a: &ArrayView1<'_, Self>) -> Array1<Self>

fn simd_cbrt(a: &ArrayView1<'_, Self>) -> Array1<Self>

fn simd_ln_1p(a: &ArrayView1<'_, Self>) -> Array1<Self>

fn simd_exp_m1(a: &ArrayView1<'_, Self>) -> Array1<Self>

fn simd_to_radians(a: &ArrayView1<'_, Self>) -> Array1<Self>

fn simd_to_degrees(a: &ArrayView1<'_, Self>) -> Array1<Self>

fn simd_digamma(a: &ArrayView1<'_, Self>) -> Array1<Self>

fn simd_trigamma(a: &ArrayView1<'_, Self>) -> Array1<Self>

fn simd_ln_gamma(a: &ArrayView1<'_, Self>) -> Array1<Self>

fn simd_erf(a: &ArrayView1<'_, Self>) -> Array1<Self>

fn simd_erfc(a: &ArrayView1<'_, Self>) -> Array1<Self>

fn simd_erfinv(a: &ArrayView1<'_, Self>) -> Array1<Self>

fn simd_erfcinv(a: &ArrayView1<'_, Self>) -> Array1<Self>

fn simd_sigmoid(a: &ArrayView1<'_, Self>) -> Array1<Self>

fn simd_gelu(a: &ArrayView1<'_, Self>) -> Array1<Self>

fn simd_swish(a: &ArrayView1<'_, Self>) -> Array1<Self>

fn simd_softplus(a: &ArrayView1<'_, Self>) -> Array1<Self>

fn simd_mish(a: &ArrayView1<'_, Self>) -> Array1<Self>

fn simd_elu(a: &ArrayView1<'_, Self>, alpha: Self) -> Array1<Self>

fn simd_selu(a: &ArrayView1<'_, Self>) -> Array1<Self>

fn simd_hardsigmoid(a: &ArrayView1<'_, Self>) -> Array1<Self>

fn simd_hardswish(a: &ArrayView1<'_, Self>) -> Array1<Self>

fn simd_sinc(a: &ArrayView1<'_, Self>) -> Array1<Self>

fn simd_log_softmax(a: &ArrayView1<'_, Self>) -> Array1<Self>

fn simd_asinh(a: &ArrayView1<'_, Self>) -> Array1<Self>

fn simd_acosh(a: &ArrayView1<'_, Self>) -> Array1<Self>

fn simd_atanh(a: &ArrayView1<'_, Self>) -> Array1<Self>

fn simd_beta(a: &ArrayView1<'_, Self>, b: &ArrayView1<'_, Self>) -> Array1<Self>

fn simd_ln_beta( a: &ArrayView1<'_, Self>, b: &ArrayView1<'_, Self>, ) -> Array1<Self>

fn simd_lerp( a: &ArrayView1<'_, Self>, b: &ArrayView1<'_, Self>, t: Self, ) -> Array1<Self>

Trait SimdUnifiedOps