/**
* Copyright 1993-2012 NVIDIA Corporation.  All rights reserved.
*	OSTATECZNY KOD
* Please refer to the NVIDIA end user license agreement (EULA) associated
* with this source code for terms and conditions that govern your use of
* this software. Any use, reproduction, disclosure, or distribution of
* this software and related documentation outside the terms of the EULA
* is strictly prohibited.
*
*/

/**
* Matrix multiplication: C = A * B.
* Host code.
*
* This sample implements matrix multiplication as described in Chapter 3
* of the programming guide.
* It has been written for clarity of exposition to illustrate various CUDA
* programming principles, not with the goal of providing the most
* performant generic kernel for matrix multiplication.
*
* See also:
* V. Volkov and J. Demmel, "Benchmarking GPUs to tune dense linear algebra,"
* in Proc. 2008 ACM/IEEE Conf. on Superconducting (SC '08),
* Piscataway, NJ: IEEE Press, 2008, pp. Art. 31:1-11.
*/

// System includes
#define WIN32
#include <stdio.h>
#include <assert.h>
#include "stdio.h"
// CUDA runtime
#include <cuda_runtime.h>

// Helper functions and utilities to work with CUDA
#include "helper_functions.h"

/**
* Matrix multiplication (CUDA Kernel) on the device: C = A * B
* wA is A's width and wB is B's width
*/
template <int BLOCK_SIZE> __global__ void
matrixMulCUDA(float *C, float *A, float *B, int wA, int wB)
{
	// Block index
	int bx = blockIdx.x;
	int by = blockIdx.y;

	// Thread index
	int tx = threadIdx.x;
	int ty = threadIdx.y;
	// Index of the first sub-matrix of A processed by the block
	int aBegin = wA * BLOCK_SIZE * by * 2;

	// Index of the last sub-matrix of A processed by the block
	int aEnd = aBegin + wA - 1;

	// Step size used to iterate through the sub-matrices of A
	int aStep = BLOCK_SIZE;

	// Index of the first sub-matrix of B processed by the block
	int bBegin = BLOCK_SIZE * bx;

	// Step size used to iterate through the sub-matrices of B
	int bStep = BLOCK_SIZE * wB;
	// Csub is used to store the element of the block sub-matrix
	// that is computed by the thread
	float Csub = 0;

	__syncthreads();
	// Loop over all the sub-matrices of A and B
	// required to compute the block sub-matrix
	for (int a = aBegin, b = bBegin;
		a <= aEnd;
		a += aStep, b += bStep)
	{

		// Declaration of the shared memory array As used to
		// store the sub-matrix of A

		// Load the matrices from device memory
		// to shared memory; each thread loads
		// one element of each matrix
		// Synchronize to make sure the matrices are loaded

		// Multiply the two matrices together;
		// each thread computes one element
		// of the block sub-matrix
#pragma unroll

		for (int k = 0; k < BLOCK_SIZE; ++k)
		{
			Csub += A[aBegin + wA * ty + tx] * B[bBegin + wB * ty + tx];
		}

		// Synchronize to make sure that the preceding
		// computation is done before loading two new
		// sub-matrices of A and B in the next iteration
	}
	__syncthreads();
	// Write the block sub-matrix to device memory;
	// each thread writes one element
	int c = wB * BLOCK_SIZE * by * 2 + BLOCK_SIZE * bx;
	C[c + wB * ty + tx] = Csub;
	//printf(" %f, %i, %i \n", Csub, c + wB * ty + tx, c + wB * ty + 10240 + tx);

}

void constantInit(float *data, int size, float val)
{
	for (int i = 0; i < size; ++i)
	{
		data[i] = (float)rand() / RAND_MAX;
	}
}

/**
* Run a simple test of matrix multiplication using CUDA
*/
int matrixMultiply(int argc, char **argv, int block_size, dim3 &dimsA, dim3 &dimsB)
{
	// Allocate host memory for matrices A and B and C
	unsigned int size_A = dimsA.x * dimsA.y;
	unsigned int mem_size_A = sizeof(float) * size_A;
	float *h_A = (float *)malloc(mem_size_A);

	unsigned int size_B = dimsB.x * dimsB.y;
	unsigned int mem_size_B = sizeof(float) * size_B;
	float *h_B = (float *)malloc(mem_size_B);

	dim3 dimsC(dimsB.x, dimsA.y, 1);
	unsigned int mem_size_C = dimsC.x * dimsC.y * sizeof(float);
	float *h_C = (float *)malloc(mem_size_C);



	// Allocate device memory
	float *d_A, *d_B, *d_C;


	const float valB = 0.01f;
	constantInit(h_A, size_A, 1.0f);
	constantInit(h_B, size_B, valB);


	cudaError_t error;
	error = cudaMalloc((void **)&d_A, mem_size_A);
	error = cudaMalloc((void **)&d_B, mem_size_B);
	error = cudaMalloc((void **)&d_C, mem_size_C);

	// copy host memory to device
	error = cudaMemcpy(d_A, h_A, mem_size_A, cudaMemcpyHostToDevice);

	error = cudaMemcpy(d_B, h_B, mem_size_B, cudaMemcpyHostToDevice);
	// Setup execution parameters
	dim3 threads(block_size, block_size);
	dim3 grid(dimsB.x / threads.x, dimsA.y / threads.y / 2);//Zmniejszam gridy o połowę
															// Create and start timer
	printf("Computing result using CUDA Kernel..%i \n", grid.y);

	// Performs warmup operation using matrixMul CUDA kernel
	matrixMulCUDA<32> << < grid, threads >> > (d_C, d_A, d_B, dimsA.x, dimsB.x);
	printf("done\n");

	cudaDeviceSynchronize();

	// Allocate CUDA events that we'll use for timing
	cudaEvent_t start;
	error = cudaEventCreate(&start);


	// Record the start event
	error = cudaEventRecord(start, NULL);

	// Execute the kernel
	int nIter = 100;

	for (int j = 0; j < nIter; j++)
	{
		if (block_size == 16)
		{
			matrixMulCUDA<16> << < grid, threads >> > (d_C, d_A, d_B, dimsA.x, dimsB.x);
		}
		else
		{
		matrixMulCUDA<32> << < grid, threads >> > (d_C, d_A, d_B, dimsA.x, dimsB.x);
		}

	}


	cudaEvent_t stop;
	error = cudaEventCreate(&stop);

	// Record the stop event
	error = cudaEventRecord(stop, NULL);

	// Wait for the stop event to complete
	error = cudaEventSynchronize(stop);


	float msecTotal = 0.0f;
	error = cudaEventElapsedTime(&msecTotal, start, stop);


	// Compute and print the performance
	float msecPerMatrixMul = msecTotal / nIter;
	double flopsPerMatrixMul = 2.0 * (double)dimsA.x * (double)dimsA.y * (double)dimsB.x;
	double gigaFlops = (flopsPerMatrixMul * 1.0e-9f) / (msecPerMatrixMul / 1000.0f);
	printf(
		"Performance= %.2f GFlop/s, Time= %.3f msec, Size= %.0f Ops, WorkgroupSize= %u threads/block\n",
		gigaFlops,
		msecPerMatrixMul,
		flopsPerMatrixMul,
		threads.x * threads.y);

	// Copy result from device to host
	error = cudaMemcpy(h_C, d_C, mem_size_C, cudaMemcpyDeviceToHost);

	if (error != cudaSuccess)
	{
		printf("cudaMemcpy (h_C,d_C) returned error code %d, line(%d)\n", error, __LINE__);
		exit(EXIT_FAILURE);
	}

	printf("Checking computed result for correctness: ");
	bool correct = true;

	//for (int i = 0; i < (int)(dimsC.x * dimsC.y); i++)
	//{
	//	if (fabs(h_C[i] - (dimsA.x * valB)) > 1e-3)
	//	{
	//		//printf("Error! Matrix[%05d]=%.8f, ref=%.8f error term is > 1e-3\n", i, h_C[i], dimsA.x*valB);
	//		correct = false;
	//	}
	//}

	//printf("%s\n", correct ? "OK" : "FAIL");

	// Clean up memory
	free(h_A);
	free(h_B);
	free(h_C);
	cudaFree(d_A);
	cudaFree(d_B);
	cudaFree(d_C);

	cudaDeviceReset();

}


/**
* Program main
*/
int main(int argc, char **argv)
{
	printf("[Matrix Multiply Using CUDA] - Starting...\n");

	if (checkCmdLineFlag(argc, (const char **)argv, "help") ||
		checkCmdLineFlag(argc, (const char **)argv, "?"))
	{
		printf("Usage -device=n (n >= 0 for deviceID)\n");
		printf("      -wA=WidthA -hA=HeightA (Width x Height of Matrix A)\n");
		printf("      -wB=WidthB -hB=HeightB (Width x Height of Matrix B)\n");
		printf("  Note: Outer matrix dimensions of A & B matrices must be equal.\n");

		exit(EXIT_SUCCESS);
	}

	// By default, we use device 0, otherwise we override the device ID based on what is provided at the command line
	int devID = 0;
	cudaError_t error;
	cudaDeviceProp deviceProp;
	error = cudaGetDevice(&devID);
	error = cudaGetDeviceProperties(&deviceProp, devID);
	// Use a larger block size for Fermi and above
	int block_size = (deviceProp.major < 2) ? 16 : 32;

	int i = 512;

	dim3 dimsA(i, i, 1);
	dim3 dimsB(i, i, 1);

	printf("MatrixA(%d,%d), MatrixB(%d,%d)\n", dimsA.x, dimsA.y, dimsB.x, dimsB.y);
	int matrix_result = matrixMultiply(argc, argv, block_size, dimsA, dimsB);
	std::cin >> i;
	exit(matrix_result);
	return 0;

}