How do I use SIMD via std::simd or intrinsics in C++?

In C++, SIMD (Single Instruction, Multiple Data) allows for parallel processing of data, significantly improving performance for tasks such as numerical computations, image processing, and more. You can utilize SIMD either through standard libraries like std::simd or using specific hardware intrinsics. This guide will provide a clear example of how to work with both approaches.

Using std::simd

The std::simd library provides a high-level interface for SIMD operations, making it easier to write portable code. Below is an example of how to use std::simd for vector addition:

#include void add_vectors(const float* a, const float* b, float* result, std::size_t size) { for (std::size_t i = 0; i < size; i += 4) { // Load 4 floats from each vector auto va = std::simd::load(a + i); auto vb = std::simd::load(b + i); // Perform addition auto vr = va + vb; // Store the result std::simd::store(result + i, vr); } }

Using Intrinsics

For more fine-grained control, you can use intrinsics directly. Below is an example using Intel's SSE intrinsics:

#include void add_vectors_intrinsics(const float* a, const float* b, float* result, std::size_t size) { for (std::size_t i = 0; i < size; i += 4) { // Load 4 floats from each vector into SSE registers __m128 va = _mm_load_ps(a + i); __m128 vb = _mm_load_ps(b + i); // Perform addition __m128 vr = _mm_add_ps(va, vb); // Store the result _mm_store_ps(result + i, vr); } }

SIMD std::simd C++ intrinsics vector processing performance optimization

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