#include <iostream>
#include <memory>
#include <cmath>
#include <complex>
#include <device_launch_parameters.h>
#include <cuda_runtime.h>
#include <cufft.h>
#include <cufftw.h>
#include <cufftXt.h>
#include <cublas_v2.h>
#include <cuComplex.h>
#include <chrono>
#include "BaseConstVariable.h"
#include "GPUTool.cuh"
#include "PrintMsgToQDebug.h"

#ifdef __CUDANVCC___
#define BLOCK_DIM 1024
#define REDUCE_SCALE 4

// CUDA核函数用于缩放数据
__global__ void scaleKernel(cuComplex* data, int size, float scale) {
	int idx = blockIdx.x * blockDim.x + threadIdx.x;
	if (idx < size) {
		data[idx].x *= scale;
		data[idx].y *= scale;
	}
}


// 打印GPU参数
void printDeviceInfo(int deviceId) {
	cudaDeviceProp deviceProp;
	cudaGetDeviceProperties(&deviceProp, deviceId);
	
	std::cout << "Device " << deviceId << ": " << deviceProp.name << std::endl;
	std::cout << "  Compute Capability: " << deviceProp.major << "." << deviceProp.minor << std::endl;
	std::cout << "  Total Global Memory: " << deviceProp.totalGlobalMem / (1024 * 1024) << " MB" << std::endl;
	std::cout << "  Shared Memory per Block: " << deviceProp.sharedMemPerBlock << " Bytes" << std::endl;
	std::cout << "  Registers per Block: " << deviceProp.regsPerBlock << std::endl;
	std::cout << "  Warp Size: " << deviceProp.warpSize << std::endl;
	std::cout << "  Max Threads per Block: " << deviceProp.maxThreadsPerBlock << std::endl;
	std::cout << "  Max Threads Dim: (" << deviceProp.maxThreadsDim[0] << ", "
		<< deviceProp.maxThreadsDim[1] << ", " << deviceProp.maxThreadsDim[2] << ")" << std::endl;
	std::cout << "  Max Grid Size: (" << deviceProp.maxGridSize[0] << ", "
		<< deviceProp.maxGridSize[1] << ", " << deviceProp.maxGridSize[2] << ")" << std::endl;
	std::cout  << "  Multiprocessor Count: " << deviceProp.multiProcessorCount << std::endl;
	std::cout  << "  Clock Rate: " << deviceProp.clockRate / 1000 << " MHz" << std::endl;
	std::cout  << "  Memory Clock Rate: " << deviceProp.memoryClockRate / 1000 << " MHz" << std::endl;
	std::cout  << "  Memory Bus Width: " << deviceProp.memoryBusWidth << " bits" << std::endl;
	std::cout  << "  L2 Cache Size: " << deviceProp.l2CacheSize / 1024 << " KB" << std::endl;
	std::cout  << "  Max Texture Dimensions: (" << deviceProp.maxTexture1D << ", "
		<< deviceProp.maxTexture2D[0] << "x" << deviceProp.maxTexture2D[1] << ", "
		<< deviceProp.maxTexture3D[0] << "x" << deviceProp.maxTexture3D[1] << "x" << deviceProp.maxTexture3D[2] << ")" << std::endl;
	std::cout << "  Unified Addressing: " << (deviceProp.unifiedAddressing ? "Yes" : "No") << std::endl;
	std::cout << "  Concurrent Kernels: " << (deviceProp.concurrentKernels ? "Yes" : "No") << std::endl;
	std::cout << "  ECC Enabled: " << (deviceProp.ECCEnabled ? "Yes" : "No") << std::endl;
	std::cout << "  PCI Bus ID: " << deviceProp.pciBusID << std::endl;
	std::cout << "  PCI Device ID: " << deviceProp.pciDeviceID << std::endl;
	std::cout << "  PCI Domain ID: " << deviceProp.pciDomainID << std::endl;
	std::cout << std::endl;
}





// 定义参数
__device__  cuComplex cuCexpf(cuComplex d)
{
	float factor = exp(d.x);
	return make_cuComplex(factor * cos(d.y), factor * sin(d.y));
}

__device__ CUDAVector GPU_VectorAB(CUDAVector A, CUDAVector B) {
	CUDAVector C;
	C.x = B.x - A.x;
	C.y = B.y - A.y;
	C.z = B.z - A.z;
	return C;
}

__device__ float GPU_VectorNorm2(CUDAVector A) {
	return sqrtf(A.x * A.x + A.y * A.y + A.z * A.z);
}

__device__ float GPU_dotVector(CUDAVector A, CUDAVector B) {
	return A.x * B.x + A.y * B.y + A.z * B.z;
}

__device__ float GPU_CosAngle_VectorA_VectorB(CUDAVector A, CUDAVector B) {
	return GPU_dotVector(A, B) / (GPU_VectorNorm2(A) * GPU_VectorNorm2(B));
}



extern __global__ void CUDAKernel_MemsetBlock(cuComplex* data, cuComplex init0, long len) {
	long  idx = blockIdx.x * blockDim.x + threadIdx.x;
	if (idx < len) {
		data[idx] = init0;
	}
}


extern __global__ void CUDAKernel_MemsetBlock(float* data, float init0, long len) {
	long  idx = blockIdx.x * blockDim.x + threadIdx.x;
	if (idx < len) {
		data[idx] = init0;
	}
}



__global__ void  CUDACkernel_SUM_reduce_dynamicshared(float* d_x, float* d_y, long N)
{
	const int tid = threadIdx.x;              //  某个block内的线程标号 index
	const int bid = blockIdx.x;               //  某个block在网格grid内的标号 index
	const int n = bid * blockDim.x + tid;      //  n 是某个线程的标号 index
	__shared__ float s_y[128];                  //  分配共享内存空间，不同的block都有共享内存变量的副本
	s_y[tid] = (n < N) ? d_x[n] : 0.0; //  每个block的共享内存变量副本，都用全局内存数组d_x来赋值，最后一个多出来的用0
	__syncthreads();  //  线程块内部直接同步

	for (int offset = blockDim.x >> 1; offset > 0; offset >>= 1) // 折半
	{

		if (tid < offset)           // 线程标号的index 不越界  折半
		{
			s_y[tid] += s_y[tid + offset];  //  某个block内的线程做折半规约
		}
		__syncthreads();        // 同步block内部的线程
	}

	if (tid == 0)        // 某个block只做一次操作
	{
		d_y[bid] = s_y[0];     //  复制共享内存变量累加的结果到全局内存
	}
}





__global__ void CUDA_DistanceAB(float* Ax, float* Ay, float* Az, float* Bx, float* By, float* Bz, float* R, long len) {
	long  idx = blockIdx.x * blockDim.x + threadIdx.x;
	if (idx < len) {
		R[idx] = sqrtf(powf(Ax[idx] - Bx[idx], 2) + powf(Ay[idx] - By[idx], 2) + powf(Az[idx] - Bz[idx], 2));
	}
}

__global__ void CUDA_B_DistanceA(float* Ax, float* Ay, float* Az, float Bx, float By, float Bz, float* R, long len) {
	long  idx = blockIdx.x * blockDim.x + threadIdx.x;
	if (idx < len) {
		R[idx] = sqrtf(powf(Ax[idx] - Bx, 2) + powf(Ay[idx] - By, 2) + powf(Az[idx] - Bz, 2));
	}
}

__global__ void CUDA_make_VectorA_B(float sX, float sY, float sZ, float* tX, float* tY, float* tZ, float* RstX, float* RstY, float* RstZ, long len) {
	long  idx = blockIdx.x * blockDim.x + threadIdx.x;
	if (idx < len) {
		RstX[idx] = sX - tX[idx]; // 地面->天
		RstY[idx] = sY - tY[idx];
		RstZ[idx] = sZ - tZ[idx];
	}
}

__global__ void CUDA_Norm_Vector(float* Vx, float* Vy, float* Vz, float* R, long len) {
	long  idx = blockIdx.x * blockDim.x + threadIdx.x;
	if (idx < len) {
		R[idx] = sqrtf(powf(Vx[idx], 2) + powf(Vy[idx], 2) + powf(Vz[idx], 2));
	}
}

__global__ void CUDA_cosAngle_VA_AB(float* Ax, float* Ay, float* Az, float* Bx, float* By, float* Bz, float* anglecos, long len) {
	long  idx = blockIdx.x * blockDim.x + threadIdx.x;
	if (idx < len) {
		float tAx = Ax[idx];
		float tAy = Ay[idx];
		float tAz = Az[idx];
		float tBx = Bx[idx];
		float tBy = By[idx];
		float tBz = Bz[idx];
		float AR = sqrtf(powf(tAx, 2) + powf(tAy, 2) + powf(tAz, 2));
		float BR = sqrtf(powf(tBx, 2) + powf(tBy, 2) + powf(tBz, 2));
		float dotAB = tAx * tBx + tAy * tBy + tAz * tBz;
		float result = acosf(dotAB / (AR * BR));
		anglecos[idx] = result;
	}
}

__global__ void CUDA_GridPoint_Linear_Interp1(float* v, float* q, float* qv, long xlen, long qlen)
{
	long  idx = blockIdx.x * blockDim.x + threadIdx.x;
	if (idx < qlen) {
		float qx = q[idx];
		// 检索循环
		if (qx < 0 || qx > xlen - 1) {}
		else {
			long x1 = floor(qx);
			long x2 = ceil(qx);

			if (x1 >= 0 && x2 < xlen) {
				float y1 = v[x1];
				float y2 = v[x2];
				float y = y1 + (y2 - y1) * (qx - x1) / (x2 - x1);
				qv[idx] = y;
			}
			else {

			}
		}
	}
}

extern __global__ void CUDA_D_sin(double* y, double* X, int n) {
	int idx = blockIdx.x * blockDim.x + threadIdx.x;
	if (idx < n) {
		y[idx] = sin(X[idx]);
	}
}

extern __global__ void CUDA_D_cos(double* y, double* X, int n) {
	int idx = blockIdx.x * blockDim.x + threadIdx.x;
	if (idx < n) {
		y[idx] = cos(X[idx]);
	}
}

extern "C" void CUDA_MemsetBlock(cuComplex* data, cuComplex init0, long len) {
	int blockSize = 256; // 每个块的线程数
	int numBlocks = (len + blockSize - 1) / blockSize; // 根据 pixelcount 计算网格大小
	// 调用 CUDA 核函数
	CUDAKernel_MemsetBlock << <numBlocks, blockSize >> > (data, init0, len);

#ifdef __CUDADEBUG__
	cudaError_t err = cudaGetLastError();
	if (err != cudaSuccess) {
		printf("CUDAmake_VectorA_B CUDA Error: %s\n", cudaGetErrorString(err));
		exit(2);
	}
#endif // __CUDADEBUG__
	cudaDeviceSynchronize();
}


//错误提示
extern "C"  void checkCudaError(cudaError_t err, const char* msg) {
	if (err != cudaSuccess) {
		std::cerr << "CUDA error: " << msg << " (" << cudaGetErrorString(err) << ")" << std::endl;
		exit(EXIT_FAILURE);
	}

}

// 主机参数内存声明
extern "C"  void* mallocCUDAHost(long memsize) {
	void* ptr;
	cudaMallocHost(&ptr, memsize);

#ifdef __CUDADEBUG__
	cudaError_t err = cudaGetLastError();
	if (err != cudaSuccess) {
		printf("mallocCUDAHost CUDA Error: %s\n", cudaGetErrorString(err));
		exit(2);
	}
#endif // __CUDADEBUG__
	cudaDeviceSynchronize();
	return ptr;
}

// 主机参数内存释放
extern "C"  void FreeCUDAHost(void* ptr) {
	cudaFreeHost(ptr);
#ifdef __CUDADEBUG__
	cudaError_t err = cudaGetLastError();
	if (err != cudaSuccess) {
		printf("FreeCUDAHost CUDA Error: %s\n", cudaGetErrorString(err));
		exit(2);
	}
#endif // __CUDADEBUG__
	cudaDeviceSynchronize();
}

// GPU参数内存声明
extern "C" void* mallocCUDADevice(long memsize) {
	void* ptr;
	cudaMalloc(&ptr, memsize);
#ifdef __CUDADEBUG__
	cudaError_t err = cudaGetLastError();
	if (err != cudaSuccess) {
		printf("mallocCUDADevice CUDA Error: %s\n", cudaGetErrorString(err));
		exit(2);
	}
#endif // __CUDADEBUG__
	cudaDeviceSynchronize();
	return ptr;
}

// GPU参数内存释放
extern "C" void FreeCUDADevice(void* ptr) {
	cudaFree(ptr);
#ifdef __CUDADEBUG__
	cudaError_t err = cudaGetLastError();
	if (err != cudaSuccess) {
		printf("FreeCUDADevice CUDA Error: %s\n", cudaGetErrorString(err));
		exit(2);
	}
#endif // __CUDADEBUG__
	cudaDeviceSynchronize();
}

// GPU 内存数据转移
extern "C" void HostToDevice(void* hostptr, void* deviceptr, long memsize) {
	cudaMemcpy(deviceptr, hostptr, memsize, cudaMemcpyHostToDevice);

#ifdef __CUDADEBUG__
	cudaError_t err = cudaGetLastError();
	if (err != cudaSuccess) {
		printf("HostToDevice CUDA Error: %s\n", cudaGetErrorString(err));
		exit(2);
	}
	 
#endif // __CUDADEBUG__
	cudaDeviceSynchronize();
}

extern "C" void DeviceToHost(void* hostptr, void* deviceptr, long memsize) {
	cudaMemcpy(hostptr, deviceptr, memsize, cudaMemcpyDeviceToHost);
#ifdef __CUDADEBUG__
	cudaError_t err = cudaGetLastError();
	if (err != cudaSuccess) {
		printf("DeviceToHost CUDA Error: %s\n", cudaGetErrorString(err));
		exit(2);
	}
#endif // __CUDADEBUG__
	cudaDeviceSynchronize();
}

void DeviceToDevice(void* s_deviceptr, void* t_deviceptr, long memsize)
{
	cudaMemcpy(t_deviceptr, s_deviceptr, memsize, cudaMemcpyDeviceToDevice);
#ifdef __CUDADEBUG__
	cudaError_t err = cudaGetLastError();
	if (err != cudaSuccess) {
		printf("DeviceToDevice CUDA Error: %s\n", cudaGetErrorString(err));
		exit(2);
	}
#endif // __CUDADEBUG__
	cudaDeviceSynchronize();

}

// 基础运算函数
extern "C" void CUDAdistanceAB(float* Ax, float* Ay, float* Az, float* Bx, float* By, float* Bz, float* R, long len) {
	// 设置 CUDA 核函数的网格和块的尺寸
	int blockSize = 256; // 每个块的线程数
	int numBlocks = (len + blockSize - 1) / blockSize; // 根据 pixelcount 计算网格大小
	// 调用 CUDA 核函数
	CUDA_DistanceAB << <numBlocks, blockSize >> > (Ax, Ay, Az, Bx, By, Bz, R, len);

#ifdef __CUDADEBUG__
	cudaError_t err = cudaGetLastError();
	if (err != cudaSuccess) {
		printf("CUDAdistanceAB CUDA Error: %s\n", cudaGetErrorString(err));
		exit(2);
	}
#endif // __CUDADEBUG__
	cudaDeviceSynchronize();
}

extern "C" void CUDABdistanceAs(float* Ax, float* Ay, float* Az, float Bx, float By, float Bz, float* R, long len) {
	// 设置 CUDA 核函数的网格和块的尺寸
	int blockSize = 256; // 每个块的线程数
	int numBlocks = (len + blockSize - 1) / blockSize; // 根据 pixelcount 计算网格大小
	// 调用 CUDA 核函数
	CUDA_B_DistanceA << <numBlocks, blockSize >> > (Ax, Ay, Az, Bx, By, Bz, R, len);

#ifdef __CUDADEBUG__
	cudaError_t err = cudaGetLastError();
	if (err != cudaSuccess) {
		printf("CUDABdistanceAs CUDA Error: %s\n", cudaGetErrorString(err));
		exit(2);
	}
#endif // __CUDADEBUG__
	cudaDeviceSynchronize();
}

extern "C" void CUDAmake_VectorA_B(float sX, float sY, float sZ, float* tX, float* tY, float* tZ, float* RstX, float* RstY, float* RstZ, long len) {
	int blockSize = 256; // 每个块的线程数
	int numBlocks = (len + blockSize - 1) / blockSize; // 根据 pixelcount 计算网格大小
	// 调用 CUDA 核函数
	CUDA_make_VectorA_B << <numBlocks, blockSize >> > (sX, sY, sZ, tX, tY, tZ, RstX, RstY, RstZ, len);

#ifdef __CUDADEBUG__
	cudaError_t err = cudaGetLastError();
	if (err != cudaSuccess) {
		printf("CUDAmake_VectorA_B CUDA Error: %s\n", cudaGetErrorString(err));
		exit(2);
	}
#endif // __CUDADEBUG__
	cudaDeviceSynchronize();
}

extern "C" void CUDANorm_Vector(float* Vx, float* Vy, float* Vz, float* R, long len) {
	// 设置 CUDA 核函数的网格和块的尺寸
	int blockSize = 256; // 每个块的线程数
	int numBlocks = (len + blockSize - 1) / blockSize; // 根据 pixelcount 计算网格大小
	// 调用 CUDA 核函数
	CUDA_Norm_Vector << <numBlocks, blockSize >> > (Vx, Vy, Vz, R, len);

#ifdef __CUDADEBUG__
	cudaError_t err = cudaGetLastError();
	if (err != cudaSuccess) {
		printf("CUDANorm_Vector CUDA Error: %s\n", cudaGetErrorString(err));
		exit(2);
	}
#endif // __CUDADEBUG__
	cudaDeviceSynchronize();
}

extern "C" void CUDAcosAngle_VA_AB(float* Ax, float* Ay, float* Az, float* Bx, float* By, float* Bz, float* anglecos, long len) {
	int blockSize = 256; // 每个块的线程数
	int numBlocks = (len + blockSize - 1) / blockSize; // 根据 pixelcount 计算网格大小
	// 调用 CUDA 核函数
	CUDA_cosAngle_VA_AB << <numBlocks, blockSize >> > (Ax, Ay, Az, Bx, By, Bz, anglecos, len);

#ifdef __CUDADEBUG__
	cudaError_t err = cudaGetLastError();
	if (err != cudaSuccess) {
		printf("CUDAcosAngle_VA_AB CUDA Error: %s\n", cudaGetErrorString(err));
		exit(2);
	}
#endif // __CUDADEBUG__
	cudaDeviceSynchronize();
}

extern "C" void  CUDAGridPointLinearInterp1(float* v, float* q, float* qv, long xlen, long qlen)
{

	int blockSize = 256; // 每个块的线程数
	int numBlocks = (qlen + blockSize - 1) / blockSize; // 根据 pixelcount 计算网格大小
	// 调用 CUDA 核函数
	CUDA_GridPoint_Linear_Interp1 << <numBlocks, blockSize >> > (v, q, qv, xlen, qlen);

#ifdef __CUDADEBUG__
	cudaError_t err = cudaGetLastError();
	if (err != cudaSuccess) {
		printf("CUDALinearInterp1 CUDA Error: %s\n", cudaGetErrorString(err));
		exit(2);
	}
#endif // __CUDADEBUG__
	cudaDeviceSynchronize();

}

extern "C" void CUDADSin(double* y, double* X, int n)
{
	// 计算 sin(temp) 并存储在 d_temp 中
	int blockSize = 256;
	int numBlocks = (n + blockSize - 1) / blockSize;
	CUDA_D_sin << <numBlocks, blockSize >> > (y, X, n);
#ifdef __CUDADEBUG__
	cudaError_t err = cudaGetLastError();
	if (err != cudaSuccess) {
		printf("sin CUDA Error: %s\n", cudaGetErrorString(err));
		exit(2);
	}
#endif // __CUDADEBUG__
	cudaDeviceSynchronize();

}


extern "C" void CUDADCos(double* y, double* X, int n)
{
	// 计算 sin(temp) 并存储在 d_temp 中
	int blockSize = 256;
	int numBlocks = (n + blockSize - 1) / blockSize;
	CUDA_D_cos << <numBlocks, blockSize >> > (y, X, n);
#ifdef __CUDADEBUG__
	cudaError_t err = cudaGetLastError();
	if (err != cudaSuccess) {
		printf("sin CUDA Error: %s\n", cudaGetErrorString(err));
		exit(2);
	}
#endif // __CUDADEBUG__
	cudaDeviceSynchronize();

}

long NextBlockPad(long num, long blocksize)
{
	return ((num + blocksize - 1) / blocksize) * blocksize;
}

void PrintLasterError(const char* s)
{
	cudaError_t err = cudaGetLastError();
	if (err != cudaSuccess) {
		//printf("%s: %s\n", s, cudaGetErrorString(err));
		PrintTipMsgToQDebug(s, cudaGetErrorString(err));
		exit(2);
	}
}





extern "C"  void CUDAIFFT(cuComplex* inArr, cuComplex* outArr, long InRowCount, long InColCount, long outColCount) {
	cufftHandle plan;
	cufftResult result;

	// 创建批量IFFT计划
	int rank = 1;
	int n[] = { InColCount };  // 每个IFFT处理freqcount点
	int inembed[] = { InColCount };
	int onembed[] = { outColCount };
	int istride = 1;
	int ostride = 1;
	int idist = InColCount;  // 输入批次间距
	int odist = outColCount;  // 输出批次间距
	int batch = InRowCount;   // 批处理数量

	result = cufftPlanMany(&plan, rank, n,
		inembed, istride, idist,
		onembed, ostride, odist,
		CUFFT_C2C, batch);
	if (result != CUFFT_SUCCESS) {
		PrintLasterError("CUDAIFFT");
		return;
	}

	// 执行IFFT
	cuComplex* in_ptr = inArr;
	cuComplex* out_ptr = outArr;
	result = cufftExecC2C(plan, (cufftComplex*)in_ptr, (cufftComplex*)out_ptr, CUFFT_INVERSE);
 
	if (result != CUFFT_SUCCESS) {
		cufftDestroy(plan);
		return;
	}

	// 等待IFFT完成并缩放数据
	cudaDeviceSynchronize();
	cufftDestroy(plan);

}





extern "C"   void CUDAIFFTScale(cuComplex* inArr, cuComplex* outArr, long InRowCount, long InColCount, long outColCount)
{
	CUDAIFFT(inArr, outArr, InRowCount, InColCount, outColCount);

	float scale = 1.0f / InColCount;
	int totalElements = InRowCount * InColCount;
	dim3 block(256);
	dim3 grid((totalElements + block.x - 1) / block.x);
	scaleKernel << <grid, block >> > (outArr, totalElements, scale);
	cudaDeviceSynchronize();

	return   ;
}



#endif




extern "C" float CUDA_SUM(float* d_x, long N)
{
	long NUM_REPEATS = 100;

	int grid_size = (N + BLOCK_SIZE - 1) / BLOCK_SIZE;
	const int ymem = sizeof(float) * grid_size;
	const int smem = sizeof(float) * BLOCK_SIZE;
	float* d_y = (float*)mallocCUDADevice(ymem);
	float* h_y = (float*)mallocCUDAHost(ymem);
	CUDACkernel_SUM_reduce_dynamicshared << <grid_size, BLOCK_SIZE, smem >> > (d_x, d_y, N);
#ifdef __CUDADEBUG__
	cudaError_t err = cudaGetLastError();
	if (err != cudaSuccess) {
		printf("CUDALinearInterp1 CUDA Error: %s\n", cudaGetErrorString(err));
		exit(2);
	}
#endif // __CUDADEBUG__
	cudaDeviceSynchronize();

	DeviceToHost(h_y, d_y, ymem);

	float result = 0.0;
	for (int n = 0; n < grid_size; ++n)
	{
		result += h_y[n];
	}


	FreeCUDAHost(h_y);
	FreeCUDADevice(d_y);

	return result;
}