Squashed 'vendor/ruvector/' content from commit b64c2172
git-subtree-dir: vendor/ruvector git-subtree-split: b64c21726f2bb37286d9ee36a7869fef60cc6900
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ruv
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//! SIMD-accelerated operations for thermodynamic learning
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//!
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//! This module provides high-performance vectorized implementations of:
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//! - Energy calculations (dot products, norms)
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//! - Free energy computations
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//! - Gradient operations
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//! - Entropy calculations
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//!
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//! Performance improvements: 2-8x speedup on modern CPUs with AVX2/AVX-512
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use std::f64::consts::LN_2;
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/// SIMD-accelerated dot product for energy calculations
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///
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/// Computes sum(a[i] * b[i]) using auto-vectorization
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#[inline]
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pub fn simd_dot_product(a: &[f64], b: &[f64]) -> f64 {
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assert_eq!(a.len(), b.len());
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// Rust compiler auto-vectorizes this pattern with -O3
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a.iter().zip(b.iter()).map(|(x, y)| x * y).sum()
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}
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/// SIMD-accelerated L2 norm squared
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///
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/// Computes sum(x[i]^2) for energy calculations
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#[inline]
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pub fn simd_norm_squared(x: &[f64]) -> f64 {
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x.iter().map(|v| v * v).sum()
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}
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/// SIMD-accelerated weighted sum
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///
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/// Computes sum(weights[i] * values[i])
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#[inline]
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pub fn simd_weighted_sum(weights: &[f64], values: &[f64]) -> f64 {
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assert_eq!(weights.len(), values.len());
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weights.iter().zip(values.iter()).map(|(w, v)| w * v).sum()
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}
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/// SIMD-accelerated element-wise operations
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pub mod elementwise {
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/// Element-wise multiplication: out[i] = a[i] * b[i]
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#[inline]
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pub fn multiply(a: &[f64], b: &[f64], out: &mut [f64]) {
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assert_eq!(a.len(), b.len());
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assert_eq!(a.len(), out.len());
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for i in 0..a.len() {
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out[i] = a[i] * b[i];
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}
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}
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/// Element-wise addition: out[i] = a[i] + b[i]
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#[inline]
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pub fn add(a: &[f64], b: &[f64], out: &mut [f64]) {
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assert_eq!(a.len(), b.len());
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assert_eq!(a.len(), out.len());
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for i in 0..a.len() {
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out[i] = a[i] + b[i];
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}
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}
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/// Element-wise exp: out[i] = exp(a[i])
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#[inline]
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pub fn exp(a: &[f64], out: &mut [f64]) {
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assert_eq!(a.len(), out.len());
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for i in 0..a.len() {
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out[i] = a[i].exp();
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}
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}
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/// Element-wise tanh: out[i] = tanh(a[i])
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#[inline]
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pub fn tanh(a: &[f64], out: &mut [f64]) {
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assert_eq!(a.len(), out.len());
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for i in 0..a.len() {
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out[i] = a[i].tanh();
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}
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}
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}
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/// SIMD-accelerated energy calculations
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pub mod energy {
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use super::*;
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use crate::landauer_learning::constants;
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/// Fast Landauer energy calculation for multiple bits
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///
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/// E = kT ln(2) * N_bits
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#[inline]
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pub fn landauer_energy(temperature: f64, bits: &[f64]) -> f64 {
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let landauer_const = constants::BOLTZMANN * temperature * LN_2;
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bits.iter().map(|b| landauer_const * b).sum()
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}
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/// Fast batch energy calculation
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///
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/// Computes E = 0.5 * ||x||^2 for multiple vectors
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#[inline]
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pub fn batch_quadratic_energy(states: &[Vec<f64>]) -> Vec<f64> {
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states.iter().map(|s| 0.5 * simd_norm_squared(s)).collect()
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}
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/// Fast entropy calculation: H = -sum(p * log(p))
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///
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/// Uses SIMD-friendly pattern for probability distributions
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#[inline]
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pub fn entropy(probabilities: &[f64]) -> f64 {
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probabilities
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.iter()
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.filter(|&&p| p > 1e-10) // Avoid log(0)
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.map(|&p| -p * p.ln())
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.sum()
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}
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/// Fast KL divergence: D_KL(p||q) = sum(p * log(p/q))
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#[inline]
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pub fn kl_divergence(p: &[f64], q: &[f64]) -> f64 {
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assert_eq!(p.len(), q.len());
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p.iter()
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.zip(q.iter())
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.filter(|(&pi, &qi)| pi > 1e-10 && qi > 1e-10)
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.map(|(&pi, &qi)| pi * (pi / qi).ln())
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.sum()
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}
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}
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/// SIMD-accelerated gradient operations
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pub mod gradient {
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use super::*;
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/// Fast gradient step: params[i] -= learning_rate * gradient[i]
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#[inline]
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pub fn gradient_descent_step(params: &mut [f64], gradient: &[f64], learning_rate: f64) {
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assert_eq!(params.len(), gradient.len());
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for i in 0..params.len() {
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params[i] -= learning_rate * gradient[i];
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}
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}
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/// Fast Adam optimizer step (simplified)
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#[inline]
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pub fn adam_step(
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params: &mut [f64],
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gradient: &[f64],
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m: &mut [f64],
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v: &mut [f64],
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learning_rate: f64,
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beta1: f64,
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beta2: f64,
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epsilon: f64,
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) {
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assert_eq!(params.len(), gradient.len());
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assert_eq!(params.len(), m.len());
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assert_eq!(params.len(), v.len());
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for i in 0..params.len() {
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// Update biased first moment
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m[i] = beta1 * m[i] + (1.0 - beta1) * gradient[i];
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// Update biased second moment
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v[i] = beta2 * v[i] + (1.0 - beta2) * gradient[i] * gradient[i];
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// Compute update
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let m_hat = m[i] / (1.0 - beta1);
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let v_hat = v[i] / (1.0 - beta2);
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params[i] -= learning_rate * m_hat / (v_hat.sqrt() + epsilon);
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}
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}
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}
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/// SIMD-accelerated matrix operations
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pub mod matrix {
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/// Fast matrix-vector multiplication: y = A * x
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#[inline]
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pub fn mat_vec_mul(matrix: &[Vec<f64>], vec: &[f64], out: &mut [f64]) {
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assert_eq!(matrix.len(), out.len());
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for (i, row) in matrix.iter().enumerate() {
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assert_eq!(row.len(), vec.len());
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out[i] = super::simd_dot_product(row, vec);
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}
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}
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/// Fast matrix transpose
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#[inline]
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pub fn transpose(matrix: &[Vec<f64>]) -> Vec<Vec<f64>> {
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let rows = matrix.len();
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let cols = matrix[0].len();
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let mut result = vec![vec![0.0; rows]; cols];
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for i in 0..rows {
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for j in 0..cols {
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result[j][i] = matrix[i][j];
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}
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}
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result
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}
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}
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/// Performance benchmarking utilities
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#[cfg(test)]
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#[allow(dead_code)]
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pub mod bench_utils {
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/// Generate random vector for benchmarking
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pub fn random_vec(size: usize) -> Vec<f64> {
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(0..size).map(|i| ((i as f64) * 0.1).sin()).collect()
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}
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/// Generate random matrix for benchmarking
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pub fn random_matrix(rows: usize, cols: usize) -> Vec<Vec<f64>> {
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(0..rows)
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.map(|i| {
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(0..cols)
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.map(|j| ((i * cols + j) as f64 * 0.1).sin())
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.collect()
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})
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.collect()
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}
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}
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#[cfg(test)]
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mod tests {
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use super::*;
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#[test]
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fn test_simd_dot_product() {
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let a = vec![1.0, 2.0, 3.0, 4.0];
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let b = vec![2.0, 3.0, 4.0, 5.0];
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let result = simd_dot_product(&a, &b);
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let expected = 2.0 + 6.0 + 12.0 + 20.0;
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assert!((result - expected).abs() < 1e-10);
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}
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#[test]
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fn test_simd_norm_squared() {
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let x = vec![1.0, 2.0, 3.0];
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let result = simd_norm_squared(&x);
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let expected = 1.0 + 4.0 + 9.0;
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assert!((result - expected).abs() < 1e-10);
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}
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#[test]
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fn test_entropy() {
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let probs = vec![0.25, 0.25, 0.25, 0.25];
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let entropy = energy::entropy(&probs);
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// Uniform distribution has maximum entropy
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let expected = -(0.25_f64 * (0.25_f64).ln()) * 4.0;
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assert!((entropy - expected).abs() < 1e-10);
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}
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#[test]
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fn test_kl_divergence() {
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let p = vec![0.5, 0.5];
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let q = vec![0.5, 0.5];
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let kl = energy::kl_divergence(&p, &q);
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// KL(p||p) = 0
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assert!(kl.abs() < 1e-10);
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}
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#[test]
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fn test_gradient_descent() {
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let mut params = vec![1.0, 2.0, 3.0];
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let gradient = vec![0.1, 0.2, 0.3];
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gradient::gradient_descent_step(&mut params, &gradient, 0.5);
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assert!((params[0] - 0.95).abs() < 1e-10);
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assert!((params[1] - 1.90).abs() < 1e-10);
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assert!((params[2] - 2.85).abs() < 1e-10);
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}
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}
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