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/*
* Copyright (c) 2022-2023, NVIDIA CORPORATION. All rights reserved.
*
* 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.
*/
/* Sample CUDA application to analyze profiling behavior of mandatory concurrent CUDA kernels.
* This sample implements the following producer-consumer algorithm :
*
* Producer : Produces grayscale pixels from RGB pixels into the buffer
* Consumer : Consumes grayscale pixels from the buffer and scales them up by 2
*
* To simplify this illustration, it's assumed that the buffer can only have one pixel at a time.
* Since the producer will not proceed further until the consumer does not read the previously produced pixel
* and the consumer will wait for the producer to produce at least one grayscale pixel,
* both producer and consumer kernels will depend on each other and must be launched concurrently.
*
* NOTE: This pattern can be often encountered for NCCL and NVSHMEM kernels and
* understanding how to profile this sample would make it easy for one to resolve potential profiling issues with such kernels.
*/
#include <cuda_profiler_api.h>
#include <cuda_runtime_api.h>
#include <nvtx3/nvToolsExt.h>
#include <algorithm>
#include <assert.h>
#include <stdio.h>
#include <stdlib.h>
#define DEFAULT_PIXELS_COUNT 1024
#define BLOCK_SIZE 64
#define MAX_BUFFER_SIZE 4
#define QUEUE_EMPTY -1
#define NUM_RGB_CHANNELS 3
#define SCALE_FACTOR 2
#define MAX_PIXEL 255
enum {
NO_RANGE = 1,
CUDA_PROFILER_RANGE = 2,
NVTX_RANGE = 3
};
#define RUNTIME_API_CALL(apiFuncCall) \
do \
{ \
cudaError_t _status = apiFuncCall; \
if (_status != cudaSuccess) \
{ \
fprintf(stderr, "%s:%d: error: function %s failed with error %s.\n", __FILE__, \
__LINE__, #apiFuncCall, cudaGetErrorString(_status)); \
exit(EXIT_FAILURE); \
} \
} while (0)
#define PRINT_PROGRAM_USAGE() \
fprintf(stderr, "Usage: %s [<range option>] [<pixels count>] [<max buffer size>]\n" \
" Default range option: 1\n" \
" Use 1 to run without range\n" \
" Use 2 to run with CUDA Profiler range\n" \
" Use 3 to run with NVTX range\n" \
" Default pixels count: %d\n" \
" Pixels count should be greater than or equal to block size: %d and" \
" must be an integral multiple of block size.\n" \
" Default max buffer size: %d\n" \
" Max buffer size should be greater than zero.\n", \
argv[0], DEFAULT_PIXELS_COUNT, BLOCK_SIZE, MAX_BUFFER_SIZE) \
#define START_RANGE(X) \
printf("Range option: "); \
switch (X) \
{ \
case NO_RANGE: \
{ \
printf("NO_RANGE\n"); \
break; \
} \
case CUDA_PROFILER_RANGE: \
{ \
printf("CUDA_PROFILER_RANGE\n"); \
cudaProfilerStart(); \
break; \
} \
case NVTX_RANGE: \
{ \
printf("NVTX_RANGE\n"); \
nvtxRangePushA("concurrent-kernel-range"); \
break; \
} \
} \
#define END_RANGE(X) \
switch (X) \
{ \
case NO_RANGE : break; \
case CUDA_PROFILER_RANGE : cudaProfilerStop(); break; \
case NVTX_RANGE : nvtxRangePop(); break; \
} \
__global__ void
Producer(int* inputPixels, volatile int* pixelsQueue, int inputSize)
{
if (!inputPixels || !pixelsQueue || !inputSize) return;
int idx = blockIdx.x * blockDim.x + threadIdx.x;
int stride = gridDim.x * blockDim.x * NUM_RGB_CHANNELS;
int i = idx * NUM_RGB_CHANNELS;
while (inputSize)
{
while (pixelsQueue[idx] != QUEUE_EMPTY)
{
// wait if buffer queue is not empty
}
// produce one grayscale pixel
pixelsQueue[idx] = (inputPixels[i] + inputPixels[i + 1] + inputPixels[i + 2]) / NUM_RGB_CHANNELS;
__threadfence();
i += stride;
--inputSize;
}
}
__global__ void
Consumer(int* outputPixels, volatile int* pixelsQueue, int outputSize)
{
if (!outputPixels || !pixelsQueue || !outputSize) return;
int idx = blockIdx.x * blockDim.x + threadIdx.x;
int stride = gridDim.x * blockDim.x;
int i = idx;
while (outputSize)
{
while (pixelsQueue[idx] == QUEUE_EMPTY)
{
// wait if buffer queue is empty
}
int scaledPixel = pixelsQueue[idx] * SCALE_FACTOR;
pixelsQueue[idx] = QUEUE_EMPTY;
__threadfence();
scaledPixel = scaledPixel > MAX_PIXEL ? MAX_PIXEL : scaledPixel;
outputPixels[i] = scaledPixel;
i += stride;
--outputSize;
}
}
void ExecuteProgram(int rangeOption, int pixelsCount, int maxBufferSize)
{
cudaStream_t streamA, streamB;
RUNTIME_API_CALL(cudaStreamCreate(&streamA));
RUNTIME_API_CALL(cudaStreamCreate(&streamB));
int blockSize = BLOCK_SIZE;
int numPixelsPerBlock = pixelsCount / blockSize;
int bufferSize = std::min(numPixelsPerBlock, maxBufferSize);
while (numPixelsPerBlock % bufferSize != 0)
{
// numPixelsPerBlock should be a multiple of bufferSize
--bufferSize;
}
int gridSize = numPixelsPerBlock / bufferSize;
printf("Grid size: %d, Block size: %d, Buffer size: %d\n", gridSize, blockSize, bufferSize);
int numPixelsQueue = gridSize * blockSize; // number of buffers needed
int* hInputPixels = (int*)malloc(pixelsCount * NUM_RGB_CHANNELS * sizeof(int)); // RGB input
int* hOutputPixels = (int*)malloc(pixelsCount * sizeof(int)); // Grayscale output
int* hPixelsQueue = (int*)malloc(numPixelsQueue * sizeof(int)); // Buffers
// Init an arbitrary RGB pixels array
for (int i = 0, *p = hInputPixels; i < pixelsCount; ++i)
{
for (int j = 0; j < NUM_RGB_CHANNELS; ++j)
{
*p++ = (i * (j + 1)) % (MAX_PIXEL + 1);
}
}
// Mark each pixel buffer as empty
for (int i = 0; i < numPixelsQueue ; ++i)
{
hPixelsQueue[i] = QUEUE_EMPTY;
}
// warmup both kernels to ensure concurrency
Producer<<<gridSize, blockSize, 0, streamA>>>(nullptr, nullptr, 0);
Consumer<<<gridSize, blockSize, 0, streamB>>>(nullptr, nullptr, 0);
RUNTIME_API_CALL(cudaStreamSynchronize(streamA));
RUNTIME_API_CALL(cudaStreamSynchronize(streamB));
// Init device memory
int* dInputPixels = nullptr;
int* dOutputPixels = nullptr;
int* dPixelsQueue = nullptr;
RUNTIME_API_CALL(cudaMalloc((void**)&dInputPixels, pixelsCount * NUM_RGB_CHANNELS * sizeof(int)));
RUNTIME_API_CALL(cudaMalloc((void**)&dOutputPixels, pixelsCount * sizeof(int)));
RUNTIME_API_CALL(cudaMalloc((void**)&dPixelsQueue, numPixelsQueue * sizeof(int)));
RUNTIME_API_CALL(cudaMemcpy(dInputPixels, hInputPixels, pixelsCount * NUM_RGB_CHANNELS * sizeof(int), cudaMemcpyHostToDevice));
RUNTIME_API_CALL(cudaMemcpy(dPixelsQueue, hPixelsQueue, numPixelsQueue * sizeof(int), cudaMemcpyHostToDevice));
// Start a range based on the user-specified option
START_RANGE(rangeOption);
Producer<<<gridSize, blockSize, 0, streamA>>>(dInputPixels, dPixelsQueue, bufferSize);
Consumer<<<gridSize, blockSize, 0, streamB>>>(dOutputPixels, dPixelsQueue, bufferSize);
// End the range
END_RANGE(rangeOption);
RUNTIME_API_CALL(cudaStreamSynchronize(streamA));
RUNTIME_API_CALL(cudaStreamSynchronize(streamB));
RUNTIME_API_CALL(cudaMemcpy(hOutputPixels, dOutputPixels, pixelsCount * sizeof(int), cudaMemcpyDeviceToHost));
// Test output correctness on host side
for (int i = 0, *p = hInputPixels; i < pixelsCount; ++i)
{
int expectedPixel = 0;
for (int j = 0; j < NUM_RGB_CHANNELS; ++j)
{
expectedPixel += *p++;
}
expectedPixel /= NUM_RGB_CHANNELS;
expectedPixel *= SCALE_FACTOR;
expectedPixel = std::min(expectedPixel, MAX_PIXEL);
assert(expectedPixel == hOutputPixels[i]);
}
RUNTIME_API_CALL(cudaFree(dInputPixels));
RUNTIME_API_CALL(cudaFree(dOutputPixels));
RUNTIME_API_CALL(cudaFree(dPixelsQueue));
free(hInputPixels);
free(hOutputPixels);
free(hPixelsQueue);
RUNTIME_API_CALL(cudaStreamDestroy(streamA));
RUNTIME_API_CALL(cudaStreamDestroy(streamB));
}
int main(int argc, char** argv)
{
int rangeOption = NO_RANGE;
int pixelsCount = DEFAULT_PIXELS_COUNT;
int maxBufferSize = MAX_BUFFER_SIZE;
if (argc > 1)
{
rangeOption = atoi(argv[1]);
if ((rangeOption < NO_RANGE) || (rangeOption > NVTX_RANGE))
{
fprintf(stderr, "** Invalid range option: %s\n", argv[1]);
PRINT_PROGRAM_USAGE();
exit(EXIT_FAILURE);
}
}
if (argc > 2)
{
pixelsCount = atoi(argv[2]);
if ((pixelsCount <= 0) || (pixelsCount % BLOCK_SIZE != 0))
{
fprintf(stderr, "** Invalid pixels count: %s\n", argv[2]);
PRINT_PROGRAM_USAGE();
exit(EXIT_FAILURE);
}
}
if (argc > 3)
{
maxBufferSize = atoi(argv[3]);
if (maxBufferSize <= 0)
{
fprintf(stderr, "** Invalid max buffer size: %s\n", argv[3]);
PRINT_PROGRAM_USAGE();
exit(EXIT_FAILURE);
}
}
printf("Pixels count: %d, Max buffer size: %d\n", pixelsCount, maxBufferSize);
ExecuteProgram(rangeOption, pixelsCount, maxBufferSize);
return 0;
}
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