Fast 1D convolution with AVX

fast_convolve_1D.cpp

// Fast 1D convolution with AXV
// By Joannes Vermorel, Lokad, January 2018
// Released under MIT license

#include <string.h>
#include <stdio.h>
#include <malloc.h> 

/* A simple implementation of a 1D convolution that just iterates over
* scalar values of the input array.
*
* Returns the same as numpy.convolve(in, kernel, mode='full')
* */

extern "C" _declspec(dllexport) int convolve_naive(float* in, int input_length,
 float* kernel,	int kernel_length, float* out)
{
	for (int i = 0; i < input_length + kernel_length - 1; i++) {

		out[i] = 0.0;
		int startk = i >= input_length ? i - input_length + 1 : 0;
		int endk = i < kernel_length ? i : kernel_length - 1;
		for (int k = startk; k <= endk; k++) {
			out[i] += in[i - k] * kernel[k];
		}
	}

	return 0;
}

/* Vectorize the algorithm to compute 4 output samples in parallel.
*
* Each kernel value is repeated 4 times, which can then be used on
* 4 input samples in parallel. Stepping over these as in naive
* means that we get 4 output samples for each inner kernel loop.
*
*/

extern "C" _declspec(dllexport) int convolve_sse(float* in, int input_length,
 float* kernel,	int kernel_length, float* out)
{
	float* in_padded = (float*)(_alloca(sizeof(float) * (input_length + 8)));

	__m128* kernel_many = (__m128*)(_alloca(16 * kernel_length));
	__m128 block;

	__m128 prod;
	__m128 acc;

	// surrounding zeroes, before and after
	_mm_storeu_ps(in_padded, _mm_set1_ps(0));
	memcpy(&in_padded[4], in, sizeof(float) * input_length);
	_mm_storeu_ps(in_padded + input_length + 4, _mm_set1_ps(0));

	// Repeat each kernal value across a 4-vector
	int i;
	for (i = 0; i < kernel_length; i++) {
		kernel_many[i] = _mm_set1_ps(kernel[i]); // broadcast
	}

	for (i = 0; i < input_length + kernel_length - 4; i += 4) {

		// Zero the accumulator
		acc = _mm_setzero_ps();

		int startk = i > (input_length - 1) ? i - (input_length - 1) : 0;
		int endk = (i + 3) < kernel_length ? (i + 3) : kernel_length - 1;

		/* After this loop, we have computed 4 output samples
		* for the price of one.
		* */
		for (int k = startk; k <= endk; k++) {

			// Load 4-float data block. These needs to be an unaliged
			// load (_mm_loadu_ps) as we step one sample at a time.
			block = _mm_loadu_ps(in_padded + 4 + i - k);
			prod = _mm_mul_ps(block, kernel_many[k]);

			// Accumulate the 4 parallel values
			acc = _mm_add_ps(acc, prod);
		}
		_mm_storeu_ps(out + i, acc);
	}

	// Left-overs
	for (; i < input_length + kernel_length - 1; i++) {

		out[i] = 0.0;
		int startk = i >= input_length ? i - input_length + 1 : 0;
		int endk = i < kernel_length ? i : kernel_length - 1;
		for (int k = startk; k <= endk; k++) {
			out[i] += in[i - k] * kernel[k];
		}
	}

	return 0;
}

/* Vectorize the algorithm to compute 8 output samples in parallel.
*
* Each kernel value is repeated 8 times, which can then be used on
* 8 input samples in parallel. Stepping over these as in naive
* means that we get 8 output samples for each inner kernel loop.
*
*/

extern "C" _declspec(dllexport) int convolve_avx(float* in, int input_length, 
	float* kernel, int kernel_length, float* out)
{
	float* in_padded = (float*)(_alloca(sizeof(float) * (input_length + 16)));

	__m256* kernel_many = (__m256*)(_alloca(sizeof(__m256) * kernel_length));

	__m256 block;
	__m256 prod;
	__m256 acc;

	// Repeat the kernel across the vector
	int i;
	for (i = 0; i < kernel_length; i++) {
		kernel_many[i] = _mm256_broadcast_ss(&kernel[i]); // broadcast
	}

	/* Create a set of 4 aligned arrays
	* Each array is offset by one sample from the one before
	*/
	block = _mm256_setzero_ps();
	_mm256_storeu_ps(in_padded, block);
	memcpy(&(in_padded[8]), in, input_length * sizeof(float));
	_mm256_storeu_ps(in_padded + input_length + 8, block);

	for (i = 0; i < input_length + kernel_length - 8; i += 8) {

		// Zero the accumulator
		acc = _mm256_setzero_ps();

		int startk = i >(input_length - 1) ? i - (input_length - 1) : 0;
		int endk = (i + 7) < kernel_length ? (i + 7) : kernel_length - 1;

		int k = startk;

		// Manual unrolling of the loop to trigger pipelining speed-up (x2 perf)
		for (; k + 3 <= endk; k += 4)
		{
			block = _mm256_loadu_ps(in_padded + 8 + i - k);
			prod = _mm256_mul_ps(block, kernel_many[k]);
			acc = _mm256_add_ps(acc, prod);

			block = _mm256_loadu_ps(in_padded + 7 + i - k);
			prod = _mm256_mul_ps(block, kernel_many[k + 1]);
			acc = _mm256_add_ps(acc, prod);

			block = _mm256_loadu_ps(in_padded + 6 + i - k);
			prod = _mm256_mul_ps(block, kernel_many[k + 2]);
			acc = _mm256_add_ps(acc, prod);

			block = _mm256_loadu_ps(in_padded + 5 + i - k);
			prod = _mm256_mul_ps(block, kernel_many[k + 3]);
			acc = _mm256_add_ps(acc, prod);
		}

		for (; k <= endk; k++) {
			block = _mm256_loadu_ps(in_padded + 8 + i - k);
			prod = _mm256_mul_ps(block, kernel_many[k]);
			acc = _mm256_add_ps(acc, prod);
		}

		_mm256_storeu_ps(out + i, acc);
	}

	// Left-overs
	for (; i < input_length + kernel_length - 1; i++) {

		out[i] = 0.0;
		int startk = i >= input_length ? i - input_length + 1 : 0;
		int endk = i < kernel_length ? i : kernel_length - 1;
		for (int k = startk; k <= endk; k++) {
			out[i] += in[i - k] * kernel[k];
		}
	}

	return 0;
}

although convolve_avx doesn't have a description, it works with 8 inputs in parallel right?

awesome!