单线程、SSE、AVX运行效率对比——乘法累加运算

前言

_mm_fmadd_ps执行效率比_mm_mul_ps + _mm_add_ps快！

同样_mm256_fmadd_ps也是如此！

math_function.h

#pragma once

#include <immintrin.h>
#include <stdio.h>

float MathMulAdd(const float *input1, const float *input2, int size);
float SSEMulAdd(const float *input1, const float *input2, int size);
float SSEFmAdd(const float *input1, const float *input2, int size);
float AVXMulAdd(const float *input1, const float *input2, int size);
float AVXFmAdd(const float *input1, const float *input2, int size);

math_function.cpp

#include "math_function.h"

float MathMulAdd(const float *input1, const float *input2, int size)
{
	float output = 0.0;
	for (int i = 0; i < size; i++)
	{
		output += input1[i] * input2[i];
	}
	return output;
}

float SSEMulAdd(const float *input1, const float *input2, int size)
{
	if (input1 == nullptr || input2 == nullptr)
	{
		printf("input data is null\n");
		return -1;
	}
	int nBlockWidth = 4;
	int cntBlock = size / nBlockWidth;
	int cntRem = size % nBlockWidth;

	float output = 0;
	__m128 loadData1, loadData2;
	__m128 mulData = _mm_setzero_ps();
	__m128 sumData = _mm_setzero_ps();
	const float *p1 = input1;
	const float *p2 = input2;
	for (int i = 0; i < cntBlock; i++)
	{
		loadData1 = _mm_load_ps(p1);
		loadData2 = _mm_load_ps(p2);
		mulData = _mm_mul_ps(loadData1, loadData2);
		sumData = _mm_add_ps(sumData, mulData);
		p1 += nBlockWidth;
		p2 += nBlockWidth;
	}
	sumData = _mm_hadd_ps(sumData, sumData); // p[0] + p[1] + p[4] + p[5] + ...
	sumData = _mm_hadd_ps(sumData, sumData); // p[2] + p[3] + p[6] + p[7] + ...
	output += sumData.m128_f32[(0)];         // 前4组

	for (int i = 0; i < cntRem; i++)
	{
		output += p1[i] * p2[i];
	}

	return output;
}

float SSEFmAdd(const float *input1, const float *input2, int size)
{
	if (input1 == nullptr || input2 == nullptr)
	{
		printf("input data is null\n");
		return -1;
	}
	int nBlockWidth = 4;
	int cntBlock = size / nBlockWidth;
	int cntRem = size % nBlockWidth;

	float output = 0;
	__m128 loadData1, loadData2;
	//__m128 mulData = _mm_setzero_ps();
	__m128 sumData = _mm_setzero_ps();
	const float *p1 = input1;
	const float *p2 = input2;
	for (int i = 0; i < cntBlock; i++)
	{
		loadData1 = _mm_load_ps(p1);
		loadData2 = _mm_load_ps(p2);
		//mulData = _mm_mul_ps(loadData1, loadData2);
		//sumData = _mm_add_ps(sumData, mulData);
		sumData = _mm_fmadd_ps(loadData1, loadData2, sumData);
		p1 += nBlockWidth;
		p2 += nBlockWidth;
	}
	sumData = _mm_hadd_ps(sumData, sumData); // p[0] + p[1] + p[4] + p[5] + ...
	sumData = _mm_hadd_ps(sumData, sumData); // p[2] + p[3] + p[6] + p[7] + ...
	output += sumData.m128_f32[(0)];         // 前4组

	for (int i = 0; i < cntRem; i++)
	{
		output += p1[i] * p2[i];
	}

	return output;
}

float AVXMulAdd(const float *input1, const float *input2, int size)
{
	if (input1 == nullptr || input2 == nullptr)
	{
		printf("input data is null\n");
		return -1;
	}
	int nBlockWidth = 8;
	int cntBlock = size / nBlockWidth;
	int cntRem = size % nBlockWidth;

	float output = 0;
	__m256 loadData1, loadData2;
	__m256 mulData = _mm256_setzero_ps();
	__m256 sumData = _mm256_setzero_ps();
	const float *p1 = input1;
	const float *p2 = input2;
	for (int i = 0; i < cntBlock; i++)
	{
		loadData1 = _mm256_load_ps(p1);
		loadData2 = _mm256_load_ps(p2);
		mulData = _mm256_mul_ps(loadData1, loadData2);
		sumData = _mm256_add_ps(sumData, mulData);
		p1 += nBlockWidth;
		p2 += nBlockWidth;
	}
	sumData = _mm256_hadd_ps(sumData, sumData); // p[0] + p[1] + p[4] + p[5] + p[8] + p[9] + p[12] + p[13] + ... 
	sumData = _mm256_hadd_ps(sumData, sumData); // p[2] + p[3] + p[6] + p[7] + p[10] + p[11] + p[14] + p[15] + ... 
	output += sumData.m256_f32[(0)];            // 前4组
	output += sumData.m256_f32[(4)];            // 后4组

	for (int i = 0; i < cntRem; i++)
	{
		output += p1[i] * p2[i];
	}

	return output;
}

float AVXFmAdd(const float *input1, const float *input2, int size)
{
	if (input1 == nullptr || input2 == nullptr)
	{
		printf("input data is null\n");
		return -1;
	}
	int nBlockWidth = 8;
	int cntBlock = size / nBlockWidth;
	int cntRem = size % nBlockWidth;

	float output = 0;
	__m256 loadData1, loadData2;
	//__m256 mulData = _mm256_setzero_ps();
	__m256 sumData = _mm256_setzero_ps();
	const float *p1 = input1;
	const float *p2 = input2;
	for (int i = 0; i < cntBlock; i++)
	{
		loadData1 = _mm256_load_ps(p1);
		loadData2 = _mm256_load_ps(p2);
		//mulData = _mm256_mul_ps(loadData1, loadData2);
		//sumData = _mm256_add_ps(sumData, mulData);
		sumData = _mm256_fmadd_ps(loadData1, loadData2, sumData);
		p1 += nBlockWidth;
		p2 += nBlockWidth;
	}
	sumData = _mm256_hadd_ps(sumData, sumData); // p[0] + p[1] + p[4] + p[5] + p[8] + p[9] + p[12] + p[13] + ... 
	sumData = _mm256_hadd_ps(sumData, sumData); // p[2] + p[3] + p[6] + p[7] + p[10] + p[11] + p[14] + p[15] + ... 
	output += sumData.m256_f32[(0)];            // 前4组
	output += sumData.m256_f32[(4)];            // 后4组

	for (int i = 0; i < cntRem; i++)
	{
		output += p1[i] * p2[i];
	}

	return output;
}

main.cpp

#include "math_function.h"
#include <random>
#include <time.h>

using std::default_random_engine;
using std::uniform_real_distribution;

int main(int argc, char* argv[])
{
	int size = 33;
	float *input1 = (float *)malloc(sizeof(float) * size);
	float *input2 = (float *)malloc(sizeof(float) * size);

	default_random_engine e;
	uniform_real_distribution<float> u(0, 1); //随机数分布对象 
	for (int i = 0; i < size; i++)
	{
		input1[i] = u(e);
		input2[i] = u(e);
	}

	int cntLoop = 10000000;

	clock_t start_t = clock();
	float org = 0.0;
	for (int i = 0; i < cntLoop; i++)
		org = MathMulAdd(input1, input2, size);
	printf("org = %f\t", org);
	printf("cost time: %d(ms)\n", clock() - start_t);

	start_t = clock();
	float sse = 0.0;
	for (int i = 0; i < cntLoop; i++)
		sse = SSEMulAdd(input1, input2, size);
	printf("sse = %f\t", sse);
	printf("cost time: %d(ms)\n", clock() - start_t);

	start_t = clock();
	float sse_ = 0.0;
	for (int i = 0; i < cntLoop; i++)
		sse_ = SSEFmAdd(input1, input2, size);
	printf("sse_= %f\t", sse_);
	printf("cost time: %d(ms)\n", clock() - start_t);

	start_t = clock();
	float avx = 0.0;
	for (int i = 0; i < cntLoop; i++)
		avx = AVXMulAdd(input1, input2, size);
	printf("avx = %f\t", avx);
	printf("cost time: %d(ms)\n", clock() - start_t);

	start_t = clock();
	float avx_ = 0.0;
	for (int i = 0; i < cntLoop; i++)
		avx_ = AVXFmAdd(input1, input2, size);
	printf("avx_= %f\t", avx_);
	printf("cost time: %d(ms)\n", clock() - start_t);

	getchar();
	free(input1);
	free(input2);

	return 0;
}

运行结果

测试硬件：CPU-i7-9700K

预处理器：_WINDOWS

命令行：/arch:AVX 

优化项：/O2

 

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