gpuSplineUtils.cu File Reference

#include "Splines/gpuSplineUtils.cuh"

Include dependency graph for gpuSplineUtils.cu:

Go to the source code of this file.

Macros
#define	_N_SPLINES_   NSplines_GPU

Functions
__host__ void	SynchroniseSplines ()
	Make sure all Cuda threads finished execution. More...
__global__ void	EvalOnGPU_Splines (const short int *__restrict__ gpu_paramNo_arr, const unsigned int *__restrict__ gpu_nKnots_arr, const float *__restrict__ gpu_coeff_many, float *__restrict__ gpu_weights, const cudaTextureObject_t __restrict__ text_coeff_x)
	Evaluate the spline on the GPU Using one {y,b,c,d} array and one {x} array Should be most efficient at cache hitting and memory coalescence But using spline segments rather than the parameter value: avoids doing binary search on GPU. More...
__global__ void	EvalOnGPU_TF1 (const float *__restrict__ gpu_coeffs_tf1, const short int *__restrict__ gpu_paramNo_arr_tf1, float *__restrict__ gpu_weights_tf1)
	Evaluate the TF1 on the GPU Using 5th order polynomial. More...
__global__ void	EvalOnGPU_TotWeight (const float *__restrict__ gpu_weights, const float *__restrict__ gpu_weights_tf1, float *__restrict__ gpu_total_weights, const cudaTextureObject_t __restrict__ text_nParamPerEvent, const cudaTextureObject_t __restrict__ text_nParamPerEvent_TF1)
	KS: Evaluate the total spline event weight on the GPU, as in most cases GPU is faster, even more this significant reduce memory transfer from GPU to CPU. More...

Variables
__device__ __constant__ unsigned int	d_n_splines
	Number of splines living on GPU. More...
__device__ __constant__ unsigned int	d_n_TF1
	Number of tf1 living on GPU. More...
__device__ __constant__ short int	d_spline_size
	Size of splines living on GPU. More...
__device__ __constant__ int	d_n_events
	Number of events living on GPU. More...
__device__ __constant__ float	val_gpu [NSplines_GPU]
	CW: Constant memory needs to be hard-coded on compile time. Could make this texture memory instead, but don't care enough right now... More...
__device__ __constant__ short int	segment_gpu [NSplines_GPU]

Macro Definition Documentation

_N_SPLINES_

#define _N_SPLINES_   NSplines_GPU

Hard code the number of splines Not entirely necessary: only used for val_gpu and segment_gpu being device constants. Could move them to not being device constants

Definition at line 6 of file gpuSplineUtils.cu.

Function Documentation

EvalOnGPU_Splines()

__global__ void EvalOnGPU_Splines	(	const short int *__restrict__	gpu_paramNo_arr,
		const unsigned int *__restrict__	gpu_nKnots_arr,
		const float *__restrict__	gpu_coeff_many,
		float *__restrict__	gpu_weights,
		const cudaTextureObject_t __restrict__	text_coeff_x
	)

Evaluate the spline on the GPU Using one {y,b,c,d} array and one {x} array Should be most efficient at cache hitting and memory coalescence But using spline segments rather than the parameter value: avoids doing binary search on GPU.

Parameters

gpu_paramNo_arr	has length = spln_counter (keeps track of which parameter we're using on this thread)
gpu_nKnots_arr	has length = spln_counter (keeps track where current spline starts)
gpu_coeff_many	has length = nKnots * 4, stores all coefficients for all splines and knots
gpu_weights	has length = spln_counter * spline_size
text_coeff_x	array storing info about X coeff, uses texture memory. Has length = n_params * spline_size,

Definition at line 347 of file gpuSplineUtils.cu.

                                                       {
//*********************************************************
  // points per spline is the offset to skip in the index to move between splines
  const unsigned int splineNum = (blockIdx.x * blockDim.x + threadIdx.x);
 
  // this is the stopping condition!
  if (splineNum < d_n_splines) {
    // This is the segment we want for this parameter variation
    // for this particular splineNum; 0 = MACCQE, 1 = pFC, 2 = EBC, etc
 
    //CW: Which Parameter we are accessing
    const short int Param = gpu_paramNo_arr[splineNum];
 
    //CW: Avoids doing costly binary search on GPU
    const short int segment = segment_gpu[Param];
 
    //KS: Segment for coeff_x is simply parameter*max knots + segment as each parmeters has the same spacing
    const short int segment_X = Param*d_spline_size+segment;
 
    //KS: Find knot position in out monolitical structure
    const unsigned int CurrentKnotPos = gpu_nKnots_arr[splineNum]*_nCoeff_+segment*_nCoeff_;
 
    // We've read the segment straight from CPU and is saved in segment_gpu
    // polynomial parameters from the monolithic splineMonolith
    const float fY = gpu_coeff_many[CurrentKnotPos];
    const float fB = gpu_coeff_many[CurrentKnotPos + 1];
    const float fC = gpu_coeff_many[CurrentKnotPos + 2];
    const float fD = gpu_coeff_many[CurrentKnotPos + 3];
    // The is the variation itself (needed to evaluate variation - stored spline point = dx)
    const float dx = val_gpu[Param] - tex1Dfetch<float>(text_coeff_x, segment_X);
 
    //CW: Wooow, let's use some fancy intrinsics and pull down the processing time by <1% from normal multiplication! HURRAY
    gpu_weights[splineNum] = fmaf(dx, fmaf(dx, fmaf(dx, fD, fC), fB), fY);
    // Or for the more "easy to read" version:
    //gpu_weights[splineNum] = (fY+dx*(fB+dx*(fC+dx*fD)));
 
    //#ifdef DEBUG
    //printf("splineNum = %i/%i, paramNo = %i, variation = %f, segment = %i, fX = %f, fX+1 = %f, dx = %f, d_n_splines = %i, d_spline_size = %i, weight = %f \n", splineNum, d_n_splines, gpu_paramNo_arr[splineNum], val_gpu[Param], segment, tex1Dfetch<float>(text_coeff_x, segment_X), tex1Dfetch<float>(text_coeff_x, segment_X+1), dx, d_n_splines, d_spline_size, gpu_weights[splineNum]);
    //#endif
  }
}

EvalOnGPU_TF1()

__global__ void EvalOnGPU_TF1	(	const float *__restrict__	gpu_coeffs_tf1,
		const short int *__restrict__	gpu_paramNo_arr_tf1,
		float *__restrict__	gpu_weights_tf1
	)

Evaluate the TF1 on the GPU Using 5th order polynomial.

Parameters

gpu_coeffs_tf1	coefficients of TF1, has length = tf1 coeef counter
gpu_paramNo_arr_tf1	has length = spln_counter (keeps track of which parameter we're using on this thread)
gpu_weights_tf1	has length = spln_counter * spline_size

Definition at line 396 of file gpuSplineUtils.cu.

                                         {
//*********************************************************
  // points per spline is the offset to skip in the index to move between splines
  const unsigned int tf1Num = (blockIdx.x * blockDim.x + threadIdx.x);
 
  if (tf1Num < d_n_TF1) {
    // The is the variation itself (needed to evaluate variation - stored spline point = dx)
    const float x = val_gpu[gpu_paramNo_arr_tf1[tf1Num]];
 
    // Read the coefficients
    const unsigned int TF1_Index = tf1Num * _nTF1Coeff_;
    const float a = gpu_coeffs_tf1[TF1_Index];
    const float b = gpu_coeffs_tf1[TF1_Index+1];
 
    gpu_weights_tf1[tf1Num] = fmaf(a, x, b);
 
    // gpu_weights_tf1[tf1Num] = a*x + b;
    //gpu_weights_tf1[tf1Num] = 1 + a*x + b*x*x + c*x*x*x + d*x*x*x*x + e*x*x*x*x*x;
  }
}

EvalOnGPU_TotWeight()

__global__ void EvalOnGPU_TotWeight	(	const float *__restrict__	gpu_weights,
		const float *__restrict__	gpu_weights_tf1,
		float *__restrict__	gpu_total_weights,
		const cudaTextureObject_t __restrict__	text_nParamPerEvent,
		const cudaTextureObject_t __restrict__	text_nParamPerEvent_TF1
	)

KS: Evaluate the total spline event weight on the GPU, as in most cases GPU is faster, even more this significant reduce memory transfer from GPU to CPU.

Parameters

gpu_weights	Weight for each spline object
gpu_weights_tf1	Weight for each TF1 object
gpu_total_weights	Total weight for each event
text_nParamPerEvent	map keeping track how many parameters applies to each event, we keep two numbers here {number of splines per event, index where splines start for a given event}
text_nParamPerEvent_TF1	map keeping track how many parameters applies to each event, we keep two numbers here {number of splines per event, index where splines start for a given event}

Definition at line 423 of file gpuSplineUtils.cu.

                                                                  {
//*********************************************************
  const unsigned int EventNum = (blockIdx.x * blockDim.x + threadIdx.x);
 
  if(EventNum < d_n_events) //stopping condition
  {
    float local_total_weight = 1.f;
 
    const unsigned int EventOffset = 2 * EventNum;
 
    for (unsigned int id = 0; id < tex1Dfetch<unsigned int>(text_nParamPerEvent, EventOffset); ++id) {
      local_total_weight *= gpu_weights[tex1Dfetch<unsigned int>(text_nParamPerEvent, EventOffset+1) + id];
    }
 
    for (unsigned int id = 0; id < tex1Dfetch<unsigned int>(text_nParamPerEvent_TF1, EventOffset); ++id) {
      local_total_weight *= gpu_weights_tf1[tex1Dfetch<unsigned int>(text_nParamPerEvent_TF1, EventOffset+1) + id];
    }
    gpu_total_weights[EventNum] = local_total_weight;
  }
}

SynchroniseSplines()

__host__ void SynchroniseSplines

(

)

Make sure all Cuda threads finished execution.

Definition at line 73 of file gpuSplineUtils.cu.

                                   {
  cudaDeviceSynchronize();
}

Variable Documentation

d_n_events

__device__ __constant__ int d_n_events

Number of events living on GPU.

Definition at line 64 of file gpuSplineUtils.cu.

d_n_splines

__device__ __constant__ unsigned int d_n_splines

Number of splines living on GPU.

Definition at line 57 of file gpuSplineUtils.cu.

d_n_TF1

__device__ __constant__ unsigned int d_n_TF1

Number of tf1 living on GPU.

Definition at line 59 of file gpuSplineUtils.cu.

d_spline_size

__device__ __constant__ short int d_spline_size

Size of splines living on GPU.

Definition at line 61 of file gpuSplineUtils.cu.

segment_gpu

__device__ __constant__ short int segment_gpu[NSplines_GPU]

Definition at line 68 of file gpuSplineUtils.cu.

val_gpu

__device__ __constant__ float val_gpu[NSplines_GPU]

CW: Constant memory needs to be hard-coded on compile time. Could make this texture memory instead, but don't care enough right now...

Definition at line 67 of file gpuSplineUtils.cu.