bioem_algorithm.h 9.67 KB
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#ifndef BIOEM_ALGORITHM_H
#define BIOEM_ALGORITHM_H

template <int GPUAlgo, class RefT>
__device__ static inline void compareRefMap(const int iRefMap, const int iOrient, const int iConv, const bioem_map& Mapconv, bioem_Probability* pProb, const bioem_param_device& param, const RefT& RefMap,
	const int cent_x, const int cent_y, const int myShift = 0, const int nShifts2 = 0, const int myRef = 0)
{
    /**************************************************************************************/
    /**********************  Calculating BioEM Probability ********************************/
    /************************* Loop of center displacement here ***************************/

    // Taking into account the center displacement

    /*** Inizialzing crosscorrelations of calculated projected convolutions ***/
    myfloat_t sum=0.0;
    myfloat_t sumsquare=0.0;
    myfloat_t crossproMapConv=0.0;

    /****** Loop over Pixels to calculate dot product and cross-correlations of displaced Ref Conv. Map***/
	if (GPUAlgo != 2 || iRefMap < RefMap.ntotRefMap) 
	{
		int iStart, jStart, iEnd, jEnd;
		if (cent_x < 0)
		{
			iStart = -cent_x;
			iEnd = param.NumberPixels;
		}
		else
		{
			iStart = 0;
			iEnd = param.NumberPixels - cent_x;
		}
		if (cent_y < 0)
		{
			jStart = -cent_y;
			jEnd = param.NumberPixels;
		}
		else
		{
			jStart = 0;
			jEnd = param.NumberPixels - cent_y;
		}

		for (int i = iStart; i < iEnd; i += 1)
		{
			for (int j = jStart; j < jEnd; j += 1)
			{
				const myfloat_t pointMap = Mapconv.points[i+cent_x][j+cent_y];
				const myfloat_t pointRefMap = RefMap.get(iRefMap, i, j);
				crossproMapConv += pointMap * pointRefMap;
				// Crosscorrelation of calculated displaced map
				sum += pointMap;
				// Calculate Sum of pixels squared
				sumsquare += pointMap*pointMap;
			}
		}
	}
	
    /********** Calculating elements in BioEM Probability formula ********/
    // Related to Reference calculated Projection
    const myfloat_t ForLogProb = (sumsquare * param.Ntotpi - sum * sum);

    // Products of different cross-correlations (first element in formula)
    const myfloat_t firstele = param.Ntotpi * (RefMap.sumsquare_RefMap[iRefMap] * sumsquare-crossproMapConv * crossproMapConv) +
                             2 * RefMap.sum_RefMap[iRefMap] * sum * crossproMapConv - RefMap.sumsquare_RefMap[iRefMap] * sum * sum - RefMap.sum_RefMap[iRefMap] * RefMap.sum_RefMap[iRefMap] * sumsquare;

    //******* Calculating log of Prob*********/
    // As in fortran code: logpro=(3-Ntotpi)*0.5*log(firstele/pConvMap[iOrient].ForLogProbfromConv[iConv])+(Ntotpi*0.5-2)*log(Ntotpi-2)-0.5*log(pConvMap[iOrient].ForLogProbfromConv[iConv])+0.5*log(PI)+(1-Ntotpi*0.5)*(log(2*PI)+1);
    const myfloat_t logpro = (3 - param.Ntotpi) * 0.5 * log(firstele) + (param.Ntotpi * 0.5 - 2) * log((param.Ntotpi - 2) * ForLogProb);

#ifdef BIOEM_GPUCODE
	if (GPUAlgo == 2)
	{
		extern __shared__ myfloat_t buf[];
		myfloat_t* buf2 = &buf[myBlockDimX];
		myfloat_t* buf3 = &buf2[myBlockDimX + 4 * myRef];
		int* bufint = (int*) buf3;
		
		buf[myThreadIdxX] = logpro;
		if (myShift == 0)
		{
			bufint[0] = 0;
		}
		__syncthreads();
		
		if (nShifts2 == CUDA_MAX_SHIFT_REDUCE) // 1024
		{
			if (myShift < 512) if (buf[myThreadIdxX + 512] > buf[myThreadIdxX]) buf[myThreadIdxX] = buf[myThreadIdxX + 512];
			__syncthreads();
		}
		
		if (nShifts2 >= 512)
		{
			if (myShift < 256) if (buf[myThreadIdxX + 256] > buf[myThreadIdxX]) buf[myThreadIdxX] = buf[myThreadIdxX + 256];
			__syncthreads();
		}

		if (nShifts2 >= 256)
		{
			if (myShift < 128) if (buf[myThreadIdxX + 128] > buf[myThreadIdxX]) buf[myThreadIdxX] = buf[myThreadIdxX + 128];
			__syncthreads();
		}

		if (nShifts2 >= 128)
		{
			if (myShift < 64) if (buf[myThreadIdxX + 64] > buf[myThreadIdxX]) buf[myThreadIdxX] = buf[myThreadIdxX + 64];
			__syncthreads();
		}

		if (myShift < 32) //Warp Size is 32, threads are synched automatically
		{
			volatile float* vbuf = buf; //Mem must be volatile such that memory access is not reordered
			if (nShifts2 >= 64 && vbuf[myThreadIdxX + 32] > vbuf[myThreadIdxX]) vbuf[myThreadIdxX] = vbuf[myThreadIdxX + 32];
			if (nShifts2 >= 32 && vbuf[myThreadIdxX + 16] > vbuf[myThreadIdxX]) vbuf[myThreadIdxX] = vbuf[myThreadIdxX + 16];
			if (nShifts2 >= 16 && vbuf[myThreadIdxX + 8] > vbuf[myThreadIdxX]) vbuf[myThreadIdxX] = vbuf[myThreadIdxX + 8];
			if (nShifts2 >= 8 && vbuf[myThreadIdxX + 4] > vbuf[myThreadIdxX]) vbuf[myThreadIdxX] = vbuf[myThreadIdxX + 4];
			if (nShifts2 >= 4 && vbuf[myThreadIdxX + 2] > vbuf[myThreadIdxX]) vbuf[myThreadIdxX] = vbuf[myThreadIdxX + 2];
			if (nShifts2 >= 2 && vbuf[myThreadIdxX + 1] > vbuf[myThreadIdxX]) vbuf[myThreadIdxX] = vbuf[myThreadIdxX + 1];
			if (myShift == 0 && iRefMap < RefMap.ntotRefMap)
			{
				const myfloat_t logpro_max = vbuf[myThreadIdxX];
				if(pProb[iRefMap].Constoadd < logpro_max)
				{
					pProb[iRefMap].Total = pProb[iRefMap].Total * exp(-logpro_max + pProb[iRefMap].Constoadd);
					pProb[iRefMap].Constoadd = logpro_max;
				}
				if(pProb[iRefMap].ConstAngle[iOrient] < logpro_max)
				{
					pProb[iRefMap].forAngles[iOrient] = pProb[iRefMap].forAngles[iOrient] * exp(-logpro_max + pProb[iRefMap].ConstAngle[iOrient]);
					pProb[iRefMap].ConstAngle[iOrient] = logpro_max;
				}
				if(pProb[iRefMap].max_prob < logpro_max)
				{
					pProb[iRefMap].max_prob = logpro_max;
					pProb[iRefMap].max_prob_orient = iOrient;
					pProb[iRefMap].max_prob_conv = iConv;
					bufint[0] = 1;
					buf3[1] = logpro_max;
				}
			}
		}
		
		__syncthreads();
		if (bufint[0] == 1 && buf3[1] == logpro && iRefMap < RefMap.ntotRefMap && atomicAdd(&bufint[0], 1) == 1)
		{
			pProb[iRefMap].max_prob_cent_x = cent_x;
			pProb[iRefMap].max_prob_cent_y = cent_y;
		}
		
		__syncthreads();
		
		if (iRefMap < RefMap.ntotRefMap)
		{
			buf[myThreadIdxX] = exp(logpro - pProb[iRefMap].Constoadd);
			buf2[myThreadIdxX] = exp(logpro - pProb[iRefMap].ConstAngle[iOrient]);
		}
		__syncthreads();
		
		if (nShifts2 == CUDA_MAX_SHIFT_REDUCE) // 1024
		{
			if (myShift < 512)
			{
				buf[myThreadIdxX] += buf[myThreadIdxX + 512];
				buf2[myThreadIdxX] += buf2[myThreadIdxX + 512];
			}
			__syncthreads();
		}
		
		if (nShifts2 >= 512)
		{
			if (myShift < 256)
			{
				buf[myThreadIdxX] += buf[myThreadIdxX + 256];
				buf2[myThreadIdxX] += buf2[myThreadIdxX + 256];
			}
			__syncthreads();
		}

		if (nShifts2 >= 256)
		{
			if (myShift < 128)
			{
				buf[myThreadIdxX] += buf[myThreadIdxX + 128];
				buf2[myThreadIdxX] += buf2[myThreadIdxX + 128];
			}
			__syncthreads();
		}

		if (nShifts2 >= 128)
		{
			if (myShift < 64)
			{
				buf[myThreadIdxX] += buf[myThreadIdxX + 64];
				buf2[myThreadIdxX] += buf2[myThreadIdxX + 64];
			}
			__syncthreads();
		}

		if (myShift < 32) //Warp Size is 32, threads are synched automatically
		{
			volatile float* vbuf = buf; //Mem must be volatile such that memory access is not reordered
			volatile float* vbuf2 = buf2;
			if (nShifts2 >= 64)
			{
				vbuf[myThreadIdxX] += vbuf[myThreadIdxX + 32];
				vbuf2[myThreadIdxX] += vbuf2[myThreadIdxX + 32];
			}
			if (nShifts2 >= 32)
			{
				vbuf[myThreadIdxX] += vbuf[myThreadIdxX + 16];
				vbuf2[myThreadIdxX] += vbuf2[myThreadIdxX + 16];
			}
			if (nShifts2 >= 16)
			{
				vbuf[myThreadIdxX] += vbuf[myThreadIdxX + 8];
				vbuf2[myThreadIdxX] += vbuf2[myThreadIdxX + 8];
			}
			if (nShifts2 >= 8)
			{
				vbuf[myThreadIdxX] += vbuf[myThreadIdxX + 4];
				vbuf2[myThreadIdxX] += vbuf2[myThreadIdxX + 4];
			}
			if (nShifts2 >= 4)
			{
				vbuf[myThreadIdxX] += vbuf[myThreadIdxX + 2];
				vbuf2[myThreadIdxX] += vbuf2[myThreadIdxX + 2];
			}
			if (nShifts2 >= 2)
			{
				vbuf[myThreadIdxX] += vbuf[myThreadIdxX + 1];
				vbuf2[myThreadIdxX] += vbuf2[myThreadIdxX + 1];
			}
			if (myShift == 0 && iRefMap < RefMap.ntotRefMap)
			{
				pProb[iRefMap].Total += vbuf[myThreadIdxX];
				pProb[iRefMap].forAngles[iOrient] += vbuf2[myThreadIdxX];
			}
		}
	}
	else
#endif

    /***** Summing & Storing total/Orientation Probabilites for each map ************/
    {
        /*******  Summing total Probabilities *************/
        /******* Need a constant because of numerical divergence*****/
        if(pProb[iRefMap].Constoadd < logpro)
        {
            pProb[iRefMap].Total = pProb[iRefMap].Total * exp(-logpro + pProb[iRefMap].Constoadd);
            pProb[iRefMap].Constoadd = logpro;
        }
        pProb[iRefMap].Total += exp(logpro - pProb[iRefMap].Constoadd);

		//Summing probabilities for each orientation
        if(pProb[iRefMap].ConstAngle[iOrient] < logpro)
        {
            pProb[iRefMap].forAngles[iOrient] = pProb[iRefMap].forAngles[iOrient] * exp(-logpro + pProb[iRefMap].ConstAngle[iOrient]);
            pProb[iRefMap].ConstAngle[iOrient] = logpro;
		}
        pProb[iRefMap].forAngles[iOrient] += exp(logpro - pProb[iRefMap].ConstAngle[iOrient]);

        /********** Getting parameters that maximize the probability ***********/
        if(pProb[iRefMap].max_prob < logpro)
        {
            pProb[iRefMap].max_prob = logpro;
            pProb[iRefMap].max_prob_cent_x = cent_x;
            pProb[iRefMap].max_prob_cent_y = cent_y;
            pProb[iRefMap].max_prob_orient = iOrient;
            pProb[iRefMap].max_prob_conv = iConv;
        }
    }
}

template <int GPUAlgo, class RefT>
__device__ static inline void compareRefMapShifted(const int iRefMap, const int iOrient, const int iConv, const bioem_map& Mapconv, bioem_Probability* pProb, const bioem_param_device& param, const RefT& RefMap)
{
    for (int cent_x = -param.maxDisplaceCenter; cent_x <= param.maxDisplaceCenter; cent_x=cent_x+param.GridSpaceCenter)
    {
        for (int cent_y = -param.maxDisplaceCenter; cent_y <= param.maxDisplaceCenter; cent_y=cent_y+param.GridSpaceCenter)
        {
			compareRefMap<GPUAlgo>(iRefMap, iOrient, iConv, Mapconv, pProb, param, RefMap, cent_x, cent_y);
        }
    }
}

#endif