CUDA计算直方图(一)原子操作 atomicAdd

最新推荐文章于 2025-06-27 23:07:12 发布

Full_Speed_Turbo

最新推荐文章于 2025-06-27 23:07:12 发布

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分类专栏： CUDA CUDA 编程

本文链接：https://blog.youkuaiyun.com/full_speed_turbo/article/details/86714710

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本文探讨了使用CUDA并行计算技术加速直方图计算的方法，对比了CPU串行算法和GPU并行算法的性能，展示了如何利用原子操作atomicAdd进行高效的数据处理。

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参考: Shane Cook. CUDA Programming: A developer’s guide to parallel computing with GPUs

背景

计算直方图是图像处理和机器学习等常用的操作.
对于大数据集, 使用串行算法十分浪费时间.
这里使用CUDA来加速直方图的计算.
对于一个较大的整数数组, 值域0~255. 求取直方图.

使用CPU计算

void cpuHist(Cuda8u *pHist_data, Cuda32u* pBin_data, Cuda32u arraySize, Cuda32u binSize)
{
	for (Cuda32u i = 0; i < arraySize;i++)
	{
		if (pHist_data[i] < binSize)
		{
			pBin_data[pHist_data[i]]++;
		}
	}

}

main函数调用:

	// CPU 数据初始化
    const Cuda32u uArraySize = 256*256;
	const Cuda32u uBinSize = 256;
	Cuda8u *h_puchData = (Cuda8u *)malloc(uArraySize*sizeof(Cuda8u));
	for (int i = 0; i < uArraySize; i++)
	{
		h_puchData[i] = rand() % uBinSize;
	}
	Cuda32u h_puHist[uBinSize] = { 0 };
	Cuda32u N = 64;
	Cuda32u iIterNum = 10;
	// 使用CPU计算
	StartTimer();
	for (Cuda32u i = 0; i < iIterNum;i++)
	{
		cpuHist(h_puchData, h_puHist, uArraySize, uBinSize);
	}
	double dblTimeElps = GetTimer();
	Cuda32u iSumC = 0;
	for (Cuda32u i = 0; i < uBinSize; i++)
	{
		iSumC += h_puHist[i];
	}
	printf("\n%%%%%%%%%%%%%% CPU 计算直方图:%%%%%%%%%%%%%%\n");
	printf("序列长度 = %d\n", uArraySize);
	printf("重复次数 = %d\n", iIterNum);
	printf("Hist累计 = %d\n", iSumC / iIterNum);
	printf("平均用时 = %fms\n", dblTimeElps / (Cuda64f)iIterNum);
	printf("%%%%%%%%%%%%%% CPU 计算直方图:%%%%%%%%%%%%%%\n");

使用CUDA 原子操作atomicAdd

__global__ void myhistogram256Kernel_01(const Cuda8u *d_hist_data, Cuda32u *d_bin_data)
{
	const Cuda32u idx = blockIdx.x*blockDim.x + threadIdx.x;
	const Cuda32u idy = blockIdx.y*blockDim.y + threadIdx.y;
	const Cuda32u tid = idx + idy*blockDim.x*gridDim.x;

	const Cuda8u value = d_hist_data[tid];
	atomicAdd(&(d_bin_data[value]), 1);

}
void cudaHist_01(Cuda8u* d_puchData, Cuda32u *d_puHist)
{
	// 总的thread数量要和数组长度相同.
	dim3 thread_rect(16, 16);
	dim3 block_rect(16, 16);
	myhistogram256Kernel_01 << <block_rect, thread_rect >> >(d_puchData, d_puHist);
}

main函数调用:

	// 先将CPU里的数据搬移到GPU中!
	memset((void*)h_puHist, 0, uBinSize*sizeof(Cuda32u));
	Cuda8u * d_puchData = NULL;
	Cuda32u * d_puHist = NULL;
	checkCudaErrors(cudaMalloc((void**)&d_puchData, uArraySize*sizeof(Cuda8u)));
	checkCudaErrors(cudaMalloc((void**)&d_puHist, uBinSize*sizeof(Cuda32u)));
	checkCudaErrors(cudaMemcpy((void*)d_puchData, (void*)h_puchData, uArraySize*sizeof(Cuda8u), cudaMemcpyHostToDevice));
	checkCudaErrors(cudaMemcpy((void*)d_puHist, (void*)h_puHist, uBinSize*sizeof(Cuda32u), cudaMemcpyHostToDevice));
	// 预热
	cudaAdd();
	// 开始计时
	cudaEvent_t start, stop;
	Cuda32f elapsedTime = 0.0;
	cudaEventCreate(&start);
	cudaEventCreate(&stop);
	cudaEventRecord(start, 0);
	for (Cuda32u i = 0; i < iIterNum;i++)
	{
		// 求直方图
		//cudaHist_07((Cuda32u*)d_puchData, d_puHist, N);
		cudaHist_01(d_puchData, d_puHist);
	}
	// 结束计时
	cudaEventRecord(stop, 0);
	cudaEventSynchronize(stop);
	cudaEventElapsedTime(&elapsedTime, start, stop);
	cudaEventDestroy(start);
	cudaEventDestroy(stop);
	// 将GPU内的数据拷回CPU
	checkCudaErrors(cudaMemcpy((void*)h_puHist, (void*)d_puHist, uBinSize*sizeof(Cuda32u), cudaMemcpyDeviceToHost));
	iSumC = 0;
	for (Cuda32u i = 0; i < uBinSize; i++)
	{
		iSumC += h_puHist[i];
	}
	printf("\n%%%%%%%%%%%%%% CUDA 计算直方图:%%%%%%%%%%%%%%\n");
	printf("序列长度 = %d\n", uArraySize);
	printf("重复次数 = %d\n", iIterNum);
	printf("Hist累计 = %d\n", iSumC / iIterNum);
	printf("平均用时 = %f ms\n", elapsedTime / (Cuda32u)iIterNum);
	printf("%%%%%%%%%%%%%% CUDA 计算直方图:%%%%%%%%%%%%%%\n\n");
	// 释放资源
	checkCudaErrors(cudaFree((void*)d_puchData));
	checkCudaErrors(cudaFree((void*)d_puHist));

	cudaDeviceReset();