cuda-samples/Samples/5_Domain_Specific/simpleD3D10RenderTarget/simpleD3D10RenderTarget_kernel.cu

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/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
2021-10-21 19:04:49 +08:00
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
* * Neither the name of NVIDIA CORPORATION nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
* EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
* PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
* CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
* EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
* PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
* PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
* OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
/* This example demonstrates how to use the CUDA Direct3D bindings with the
* runtime API.
* Device code.
*/
#ifndef SIMPLED3D10RENDERTARGET_KERNEL_CU
#define SIMPLED3D10RENDERTARGET_KERNEL_CU
// includes, C string library
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
// includes, cuda
#include <cuda.h>
#include <builtin_types.h>
#include <cuda_runtime_api.h>
// includes, project
#include <helper_cuda.h> // includes cuda.h and cuda_runtime_api.h
//#include "checkCudaErrors"
#define BIN_COUNT 256
#define HISTOGRAM_SIZE (BIN_COUNT * sizeof(unsigned int))
texture<uchar4, 2, cudaReadModeElementType> colorTex;
////////////////////////////////////////////////////////////////////////////////
// GPU-specific definitions
////////////////////////////////////////////////////////////////////////////////
// Fast mul on G8x / G9x / G100
#define IMUL(a, b) __mul24(a, b)
// Machine warp size
// G80's warp size is 32 threads
#define WARP_LOG2SIZE 5
// Warps in thread block for histogram256Kernel()
#define WARP_N 6
// Corresponding thread block size in threads for histogram256Kernel()
#define THREAD_N (WARP_N << WARP_LOG2SIZE)
// Total histogram size (in counters) per thread block for histogram256Kernel()
#define BLOCK_MEMORY (WARP_N * BIN_COUNT)
// Thread block count for histogram256Kernel()
#define BLOCK_N 64
////////////////////////////////////////////////////////////////////////////////
// If threadPos == threadIdx.x, there are always 4-way bank conflicts,
// since each group of 16 threads (half-warp) accesses different bytes,
// but only within 4 shared memory banks. Having shuffled bits of threadIdx.x
// as in histogram64GPU(), each half-warp accesses different shared memory banks
// avoiding any bank conflicts at all.
// Refer to the supplied whitepaper for detailed explanations.
////////////////////////////////////////////////////////////////////////////////
__device__ inline void addData256(volatile unsigned int *s_WarpHist,
unsigned int data, unsigned int threadTag) {
unsigned int count;
do {
count = s_WarpHist[data] & 0x07FFFFFFU;
count = threadTag | (count + 1);
s_WarpHist[data] = count;
} while (s_WarpHist[data] != count);
}
////////////////////////////////////////////////////////////////////////////////
// Main histogram calculation kernel
////////////////////////////////////////////////////////////////////////////////
static __global__ void histogramTex256Kernel(unsigned int *d_Result,
unsigned int width,
unsigned int height, int dataN) {
// Current global thread index
const int globalTid = IMUL(blockIdx.x, blockDim.x) + threadIdx.x;
// Total number of threads in the compute grid
const int numThreads = IMUL(blockDim.x, gridDim.x);
// Thread tag for addData256()
// WARP_LOG2SIZE higher bits of counter values are tagged
// by lower WARP_LOG2SIZE threadID bits
const unsigned int threadTag = threadIdx.x << (32 - WARP_LOG2SIZE);
// Shared memory storage for each warp
volatile __shared__ unsigned int s_Hist[BLOCK_MEMORY];
// Current warp shared memory base
const int warpBase = (threadIdx.x >> WARP_LOG2SIZE) * BIN_COUNT;
// Clear shared memory buffer for current thread block before processing
for (int pos = threadIdx.x; pos < BLOCK_MEMORY; pos += blockDim.x)
s_Hist[pos] = 0;
// Cycle through the entire data set, update subhistograms for each warp
__syncthreads();
for (int pos = globalTid; pos < dataN; pos += numThreads) {
// NOTE: check this... Not sure this is what needs to be done
int py = pos / width;
int px = pos - (py * width);
uchar4 data4 = tex2D(colorTex, px, py);
addData256(s_Hist + warpBase, (data4.x), threadTag);
addData256(s_Hist + warpBase, (data4.y), threadTag);
addData256(s_Hist + warpBase, (data4.z), threadTag);
addData256(s_Hist + warpBase, (data4.w), threadTag);
}
__syncthreads();
// Merge per-warp histograms into per-block and write to global memory
for (int pos = threadIdx.x; pos < BIN_COUNT; pos += blockDim.x) {
unsigned int sum = 0;
for (int base = 0; base < BLOCK_MEMORY; base += BIN_COUNT)
sum += s_Hist[base + pos] & 0x07FFFFFFU;
d_Result[blockIdx.x * BIN_COUNT + pos] = sum;
}
}
///////////////////////////////////////////////////////////////////////////////
// Merge BLOCK_N subhistograms of BIN_COUNT bins into final histogram
///////////////////////////////////////////////////////////////////////////////
// gridDim.x == BIN_COUNT
// blockDim.x == BLOCK_N
// blockIdx.x == bin counter processed by current block
// threadIdx.x == subhistogram index
static __global__ void mergeHistogramTex256Kernel(unsigned int *d_Result) {
__shared__ unsigned int data[BLOCK_N];
// Reads are uncoalesced, but this final stage takes
// only a fraction of total processing time
data[threadIdx.x] = d_Result[threadIdx.x * BIN_COUNT + blockIdx.x];
for (int stride = BLOCK_N / 2; stride > 0; stride >>= 1) {
__syncthreads();
if (threadIdx.x < stride) data[threadIdx.x] += data[threadIdx.x + stride];
}
if (threadIdx.x == 0) d_Result[blockIdx.x] = data[0];
}
////////////////////////////////////////////////////////////////////////////////
// Host interface to GPU histogram
////////////////////////////////////////////////////////////////////////////////
extern "C" void checkCudaError() {
cudaError_t err = cudaGetLastError();
if (cudaSuccess != err) {
fprintf(stderr, "Cuda error: %s.\n", cudaGetErrorString(err));
exit(2);
}
}
// Maximum block count for histogram64kernel()
// Limits input data size to 756MB
// const int MAX_BLOCK_N = 16384;
// Internal memory allocation
// const int BLOCK_N2 = 32;
extern "C" void createHistogramTex(unsigned int *h_Result, unsigned int width,
unsigned int height, cudaArray *colorArray) {
cudaBindTextureToArray(colorTex, colorArray);
checkCudaError();
histogramTex256Kernel<<<BLOCK_N, THREAD_N>>>(h_Result, width, height,
width * height / 4);
checkCudaError();
mergeHistogramTex256Kernel<<<BIN_COUNT, BLOCK_N>>>(h_Result);
checkCudaError();
cudaUnbindTexture(colorTex);
checkCudaError();
#if 0
// Dummy fill test
unsigned int toto[256];
for (int i=0; i<256; i++)
{
toto[i] = i * 100;
}
cudaMemcpy(h_Result, toto, HISTOGRAM_SIZE, cudaMemcpyHostToDevice);
#endif
checkCudaError();
}
extern "C" void bindArrayToTexture(cudaArray *pArray) {}
#endif // #ifndef SIMPLED3D10RENDERTARGET_KERNEL_CU