Exploring SIMD and Parallelism in C++ with Modern Techniques

Discover how to leverage SIMD for parallel data processing in C++ using both intrinsics and C++20 features, with examples for practical performance enhancement.

Introduction to SIMD

Single Instruction, Multiple Data (SIMD) is a type of parallel computing where one instruction performs operations on multiple data points simultaneously. This can significantly speed up computations for algorithms that can be vectorized, like image processing, scientific simulations, or machine learning algorithms.

SIMD in C++

C++ doesn't support SIMD directly in its standard library before C++20, but compilers often have intrinsics for SIMD operations. However, with C++20, we've seen the introduction of <simd> in some compilers, although it's still experimental. Here, we'll look at both intrinsic functions and how you might use C++20 features where available.

Using Compiler Intrinsics

Let's start with SSE/AVX intrinsics, which are commonly supported by compilers like GCC, Clang, and MSVC.

#include <immintrin.h> // For AVX2 intrinsics
#include <iostream>
 
void simdAdd(float *a, float *b, float *result, int n) {
    __m256 va, vb, vresult;
    for(int i = 0; i < n; i += 8) { // 8 floats per AVX2 register
        // Load data
        va = _mm256_loadu_ps(a + i);
        vb = _mm256_loadu_ps(b + i);
        
        // Perform addition
        vresult = _mm256_add_ps(va, vb);
        
        // Store result
        _mm256_storeu_ps(result + i, vresult);
    }
}
 
int main() {
    const int N = 1024;
    float *arr1 = (float*)aligned_alloc(32, N * sizeof(float));
    float *arr2 = (float*)aligned_alloc(32, N * sizeof(float));
    float *res  = (float*)aligned_alloc(32, N * sizeof(float));
 
    for(int i = 0; i < N; ++i) {
        arr1[i] = i * 1.0f;
        arr2[i] = (N - i) * 1.0f;
    }
 
    simdAdd(arr1, arr2, res, N);
 
    for(int i = 0; i < 8; ++i) { // Print first 8 results for verification
        std::cout << res[i] << " ";
    }
    
    free(arr1); free(arr2); free(res);
    return 0;
}

This example uses AVX2 intrinsics to perform vector addition. Here, _mm256_loadu_ps loads 8 floats into an AVX register, _mm256_add_ps adds them, and _mm256_storeu_ps stores the result.

C++20 SIMD

C++20 introduces <experimental/simd> for some compilers, providing a more portable way to write SIMD code. Here's how you might rewrite the above with C++20 features:

#include <experimental/simd>
#include <iostream>
 
namespace stdv = std::experimental;
 
void simdAdd(const stdv::native_simd<float>& a, const stdv::native_simd<float>& b, stdv::native_simd<float>& result) {
    result = a + b;
}
 
int main() {
    const int N = 1024;
    stdv::fixed_size_simd<float, stdv::native_simd<float>::size()> arr1(N), arr2(N), res(N);
    
    for(int i = 0; i < N; ++i) {
        arr1[i] = i * 1.0f;
        arr2[i] = (N - i) * 1.0f;
    }
 
    for (size_t i = 0; i < arr1.size(); i += arr1.element_count()) {
        simdAdd(arr1.subsimd(i, arr1.element_count()), 
                arr2.subsimd(i, arr2.element_count()), 
                res.subsimd(i, res.element_count()));
    }
 
    for(int i = 0; i < 8; ++i) {
        std::cout << res[i] << " ";
    }
 
    return 0;
}

This version uses std::experimental::native_simd for direct SIMD operations. Note that std::experimental::simd is part of the Parallelism TS v2, and support might be limited or experimental in current compilers.

Advantages and Considerations:

• Performance: SIMD can dramatically speed up operations on large datasets by performing multiple operations in one go.

• Compatibility: Not all hardware supports the same SIMD instructions. AVX-512, for instance, is less common than SSE or AVX2. Always check for CPU capabilities at runtime or compile for the lowest common denominator.

• Alignment: Proper memory alignment (e.g., 32-byte aligned for AVX2) can significantly affect performance.

• Complexity: Writing efficient SIMD code can be complex, requiring a good understanding of both the algorithm and the underlying hardware.

Conclusion:

SIMD allows C++ developers to tap into the parallel processing capabilities of modern CPUs, offering significant performance improvements for suitable tasks. While the C++20 standard begins to offer more standardized ways to use SIMD, intrinsic functions remain widely used for their direct control over hardware capabilities. Always remember to benchmark your SIMD implementations against their scalar counterparts to ensure the expected performance gains are realized in your specific use case.