Bdtjtk

I'm trying to convert this code:

double *pB = b[voiceIndex];

double *pC = c[voiceIndex];

double phase = mPhase;

double bp0 = mNoteFrequency * mHostPitch;



for (int sampleIndex = 0; sampleIndex < blockSize; sampleIndex++) {

    // some other code (that will use phase, like sin(phase))



    phase += std::clamp(radiansPerSample * (bp0 * pB[sampleIndex] + pC[sampleIndex]), 0.0, PI);

}



mPhase = phase;

in SSE2, trying to speed up the whole block (which is called often). I'm using MSVC with the Fast optimizazion flag, but auto-vectorization is very crap. Since I'm also learning vectorization, I find it a nice challenge.

So I've take the formula above, and simplified, such as:

radiansPerSampleBp0 = radiansPerSample * bp0;

phase += std::clamp(radiansPerSampleBp0 * pB[sampleIndex] + radiansPerSample * pC[sampleIndex]), 0.0, PI);

Which can be muted into a serial dependency such as:

phase[0] += (radiansPerSampleBp0 * pB[0] + radiansPerSample * pC[0])

phase[1] += (radiansPerSampleBp0 * pB[1] + radiansPerSample * pC[1]) + (radiansPerSampleBp0 * pB[0] + radiansPerSample * pC[0])



phase[2] += (radiansPerSampleBp0 * pB[2] + radiansPerSample * pC[2]) + (radiansPerSampleBp0 * pB[1] + radiansPerSample * pC[1])

phase[3] += (radiansPerSampleBp0 * pB[3] + radiansPerSample * pC[3]) + (radiansPerSampleBp0 * pB[2] + radiansPerSample * pC[2])



phase[4] += (radiansPerSampleBp0 * pB[4] + radiansPerSample * pC[4]) + (radiansPerSampleBp0 * pB[3] + radiansPerSample * pC[3])

phase[5] += (radiansPerSampleBp0 * pB[5] + radiansPerSample * pC[5]) + (radiansPerSampleBp0 * pB[4] + radiansPerSample * pC[4]) 

Hence, the code I did:

double *pB = b[voiceIndex];

double *pC = c[voiceIndex];

double phase = mPhase;

double bp0 = mNoteFrequency * mHostPitch;



__m128d v_boundLower = _mm_set1_pd(0.0);

__m128d v_boundUpper = _mm_set1_pd(PI);

__m128d v_radiansPerSampleBp0 = _mm_set1_pd(mRadiansPerSample * bp0);

__m128d v_radiansPerSample = _mm_set1_pd(mRadiansPerSample);



__m128d v_pB0 = _mm_load_pd(pB);

v_pB0 = _mm_mul_pd(v_pB0, v_radiansPerSampleBp0);

__m128d v_pC0 = _mm_load_pd(pC);

v_pC0 = _mm_mul_pd(v_pC0, v_radiansPerSample);



__m128d v_pB1 = _mm_setr_pd(0.0, pB[0]);

v_pB1 = _mm_mul_pd(v_pB1, v_radiansPerSampleBp0);

__m128d v_pC1 = _mm_setr_pd(0.0, pC[0]);

v_pC1 = _mm_mul_pd(v_pC1, v_radiansPerSample);



__m128d v_phase = _mm_set1_pd(phase);

__m128d v_phaseAcc;



for (int sampleIndex = 0; sampleIndex < blockSize; sampleIndex += 2, pB += 2, pC += 2) {

    // some other code (that will use phase, like sin(phase))



    v_phaseAcc = _mm_add_pd(v_pB0, v_pC0);

    v_phaseAcc = _mm_max_pd(v_phaseAcc, v_boundLower);

    v_phaseAcc = _mm_min_pd(v_phaseAcc, v_boundUpper);

    v_phaseAcc = _mm_add_pd(v_phaseAcc, v_pB1);

    v_phaseAcc = _mm_add_pd(v_phaseAcc, v_pC1);

    v_phase = _mm_add_pd(v_phase, v_phaseAcc);



    v_pB0 = _mm_load_pd(pB + 2);

    v_pB0 = _mm_mul_pd(v_pB0, v_radiansPerSampleBp0);

    v_pC0 = _mm_load_pd(pC + 2);

    v_pC0 = _mm_mul_pd(v_pC0, v_radiansPerSample);



    v_pB1 = _mm_load_pd(pB + 1);

    v_pB1 = _mm_mul_pd(v_pB1, v_radiansPerSampleBp0);

    v_pC1 = _mm_load_pd(pC + 1);

    v_pC1 = _mm_mul_pd(v_pC1, v_radiansPerSample);

}



mPhase = v_phase.m128d_f64[blockSize % 2 == 0 ? 1 : 0]; 

But, unfortunately, after sum "steps", the results become very different for each phase value.
Tried to debug, but I'm not really able to find where the problem is.

Also, it's not really so "fast" rather than the old version.

Are you able to recognize the trouble? And how you will speed-up the code?

Here's the whole code, if you want to check the two different outputs:

#include <iostream>

#include <algorithm>

#include <immintrin.h>

#include <emmintrin.h>



#define PI 3.14159265358979323846



constexpr int voiceSize = 1;

constexpr int bufferSize = 256;



class Param

{

public:

    alignas(16) double mPhase = 0.0;

    alignas(16) double mPhaseOptimized = 0.0;

    alignas(16) double mNoteFrequency = 10.0;

    alignas(16) double mHostPitch = 1.0;

    alignas(16) double mRadiansPerSample = 1.0;



    alignas(16) double b[voiceSize][bufferSize];

    alignas(16) double c[voiceSize][bufferSize];



    Param() { }



    inline void Process(int voiceIndex, int blockSize) {

        double *pB = b[voiceIndex];

        double *pC = c[voiceIndex];

        double phase = mPhase;

        double bp0 = mNoteFrequency * mHostPitch;



        for (int sampleIndex = 0; sampleIndex < blockSize; sampleIndex++) {

            // some other code (that will use phase, like sin(phase))



            phase += std::clamp(mRadiansPerSample * (bp0 * pB[sampleIndex] + pC[sampleIndex]), 0.0, PI);



            std::cout << sampleIndex << ": " << phase << std::endl;

        }



        mPhase = phase;

    }

    inline void ProcessOptimized(int voiceIndex, int blockSize) {

        double *pB = b[voiceIndex];

        double *pC = c[voiceIndex];

        double phase = mPhaseOptimized;

        double bp0 = mNoteFrequency * mHostPitch;



        __m128d v_boundLower = _mm_set1_pd(0.0);

        __m128d v_boundUpper = _mm_set1_pd(PI);

        __m128d v_radiansPerSampleBp0 = _mm_set1_pd(mRadiansPerSample * bp0);

        __m128d v_radiansPerSample = _mm_set1_pd(mRadiansPerSample);



        __m128d v_pB0 = _mm_load_pd(pB);

        v_pB0 = _mm_mul_pd(v_pB0, v_radiansPerSampleBp0);

        __m128d v_pC0 = _mm_load_pd(pC);

        v_pC0 = _mm_mul_pd(v_pC0, v_radiansPerSample);



        __m128d v_pB1 = _mm_setr_pd(0.0, pB[0]);

        v_pB1 = _mm_mul_pd(v_pB1, v_radiansPerSampleBp0);

        __m128d v_pC1 = _mm_setr_pd(0.0, pC[0]);

        v_pC1 = _mm_mul_pd(v_pC1, v_radiansPerSample);



        __m128d v_phase = _mm_set1_pd(phase);

        __m128d v_phaseAcc;



        for (int sampleIndex = 0; sampleIndex < blockSize; sampleIndex += 2, pB += 2, pC += 2) {

            // some other code (that will use phase, like sin(phase))



            v_phaseAcc = _mm_add_pd(v_pB0, v_pC0);

            v_phaseAcc = _mm_max_pd(v_phaseAcc, v_boundLower);

            v_phaseAcc = _mm_min_pd(v_phaseAcc, v_boundUpper);

            v_phaseAcc = _mm_add_pd(v_phaseAcc, v_pB1);

            v_phaseAcc = _mm_add_pd(v_phaseAcc, v_pC1);

            v_phase = _mm_add_pd(v_phase, v_phaseAcc);



            v_pB0 = _mm_load_pd(pB + 2);

            v_pB0 = _mm_mul_pd(v_pB0, v_radiansPerSampleBp0);

            v_pC0 = _mm_load_pd(pC + 2);

            v_pC0 = _mm_mul_pd(v_pC0, v_radiansPerSample);



            v_pB1 = _mm_load_pd(pB + 1);

            v_pB1 = _mm_mul_pd(v_pB1, v_radiansPerSampleBp0);

            v_pC1 = _mm_load_pd(pC + 1);

            v_pC1 = _mm_mul_pd(v_pC1, v_radiansPerSample);



            std::cout << sampleIndex << ": " << v_phase.m128d_f64[0] << std::endl;

            std::cout << sampleIndex + 1 << ": " << v_phase.m128d_f64[1] << std::endl;

        }



        mPhaseOptimized = v_phase.m128d_f64[blockSize % 2 == 0 ? 1 : 0];

    }

};



class MyPlugin

{

public: 

    Param mParam1;



    MyPlugin() {

        // fill b

        for (int voiceIndex = 0; voiceIndex < voiceSize; voiceIndex++) {

            for (int sampleIndex = 0; sampleIndex < bufferSize; sampleIndex++) {

                double value = (sampleIndex / ((double)bufferSize - 1));



                mParam1.b[voiceIndex][sampleIndex] = value;

            }

        }



        // fill c

        for (int voiceIndex = 0; voiceIndex < voiceSize; voiceIndex++) {

            for (int sampleIndex = 0; sampleIndex < bufferSize; sampleIndex++) {

                double value = 0.0;



                mParam1.c[voiceIndex][sampleIndex] = value;

            }

        }

    }

    ~MyPlugin() { }



    void Process(int blockSize) {

        for (int voiceIndex = 0; voiceIndex < voiceSize; voiceIndex++) {

            mParam1.Process(voiceIndex, blockSize);

        }

    }

    void ProcessOptimized(int blockSize) {

        for (int voiceIndex = 0; voiceIndex < voiceSize; voiceIndex++) {

            mParam1.ProcessOptimized(voiceIndex, blockSize);

        }

    }

};



int main() {

    MyPlugin myPlugin;



    long long numProcessing = 1;

    long long counterProcessing = 0;



    // I'll only process once block, just for analysis

    while (counterProcessing++ < numProcessing) {

        // variable blockSize (i.e. it can vary, being even or odd)

        int blockSize = 256;



        // process data

        myPlugin.Process(blockSize);

        std::cout << "#########" << std::endl;

        myPlugin.ProcessOptimized(blockSize);

    }

}

(update: this answer was written before the edits that show v_phase being used inside the loop.)

Wait a minute, I thought in your previous question you needed the value of phase at each step. Yeah, there was a // some other code (that will use phase) comment inside the loop.

But this looks like you're only interested in the final value. So you're free to reorder things because the clamping for each step is independent.

This is just a reduction (like sum of an array) with some processing on the fly to generate the inputs to the reduction.

You want the 2 elements of v_phase to be 2 independent partial sums for the even / odd elements. Then you horizontal sum it at the end. (e.g. _mm_unpackhi_pd(v_phase, v_phase) to bring the high half to the bottom, or see Fastest way to do horizontal float vector sum on x86).

Then optionally use scalar fmod on the result to range-reduce into the [0..2Pi) range. (Occasional range-reduction during the sum could help precision by stopping the value from getting so large, if it turns out that precision becomes a problem.)

If that isn't the case, and you do need a vector of { phase[i+0], phase[i+1] } for something at every i+=2 step, then your problem seems to be related to a prefix sum. But with only 2 elements per vector, just redundantly doing everything to elements with unaligned loads probably makes sense.

There might be less savings than I thought since you need to clamp each step separately: doing pB[i+0] + pB[i+1] before multiplying could result in different clamping.

But you've apparently removed the clamping in our simplified formula, so you can potentially add elements before applying the mul/add formula.

Or maybe it's a win to do the multiply/add stuff for two steps at once, then shuffle that around to get the right stuff added.