cuda-samples/Samples/simpleOccupancy/simpleOccupancy.cu

/* Copyright (c) 2021, 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.
 */

#include <iostream>
#include <helper_cuda.h>  // helper functions for CUDA error check

const int manualBlockSize = 32;

////////////////////////////////////////////////////////////////////////////////
// Test kernel
//
// This kernel squares each array element. Each thread addresses
// himself with threadIdx and blockIdx, so that it can handle any
// execution configuration, including anything the launch configurator
// API suggests.
////////////////////////////////////////////////////////////////////////////////
__global__ void square(int *array, int arrayCount) {
  extern __shared__ int dynamicSmem[];
  int idx = threadIdx.x + blockIdx.x * blockDim.x;

  if (idx < arrayCount) {
    array[idx] *= array[idx];
  }
}

////////////////////////////////////////////////////////////////////////////////
// Potential occupancy calculator
//
// The potential occupancy is calculated according to the kernel and
// execution configuration the user desires. Occupancy is defined in
// terms of active blocks per multiprocessor, and the user can convert
// it to other metrics.
//
// This wrapper routine computes the occupancy of kernel, and reports
// it in terms of active warps / maximum warps per SM.
////////////////////////////////////////////////////////////////////////////////
static double reportPotentialOccupancy(void *kernel, int blockSize,
                                       size_t dynamicSMem) {
  int device;
  cudaDeviceProp prop;

  int numBlocks;
  int activeWarps;
  int maxWarps;

  double occupancy;

  checkCudaErrors(cudaGetDevice(&device));
  checkCudaErrors(cudaGetDeviceProperties(&prop, device));

  checkCudaErrors(cudaOccupancyMaxActiveBlocksPerMultiprocessor(
      &numBlocks, kernel, blockSize, dynamicSMem));

  activeWarps = numBlocks * blockSize / prop.warpSize;
  maxWarps = prop.maxThreadsPerMultiProcessor / prop.warpSize;

  occupancy = (double)activeWarps / maxWarps;

  return occupancy;
}

////////////////////////////////////////////////////////////////////////////////
// Occupancy-based launch configurator
//
// The launch configurator, cudaOccupancyMaxPotentialBlockSize and
// cudaOccupancyMaxPotentialBlockSizeVariableSMem, suggests a block
// size that achieves the best theoretical occupancy. It also returns
// the minimum number of blocks needed to achieve the occupancy on the
// whole device.
//
// This launch configurator is purely occupancy-based. It doesn't
// translate directly to performance, but the suggestion should
// nevertheless be a good starting point for further optimizations.
//
// This function configures the launch based on the "automatic"
// argument, records the runtime, and reports occupancy and runtime.
////////////////////////////////////////////////////////////////////////////////
static int launchConfig(int *array, int arrayCount, bool automatic) {
  int blockSize;
  int minGridSize;
  int gridSize;
  size_t dynamicSMemUsage = 0;

  cudaEvent_t start;
  cudaEvent_t end;

  float elapsedTime;

  double potentialOccupancy;

  checkCudaErrors(cudaEventCreate(&start));
  checkCudaErrors(cudaEventCreate(&end));

  if (automatic) {
    checkCudaErrors(cudaOccupancyMaxPotentialBlockSize(
        &minGridSize, &blockSize, (void *)square, dynamicSMemUsage,
        arrayCount));

    std::cout << "Suggested block size: " << blockSize << std::endl
              << "Minimum grid size for maximum occupancy: " << minGridSize
              << std::endl;
  } else {
    // This block size is too small. Given limited number of
    // active blocks per multiprocessor, the number of active
    // threads will be limited, and thus unable to achieve maximum
    // occupancy.
    //
    blockSize = manualBlockSize;
  }

  // Round up
  //
  gridSize = (arrayCount + blockSize - 1) / blockSize;

  // Launch and profile
  //
  checkCudaErrors(cudaEventRecord(start));
  square<<<gridSize, blockSize, dynamicSMemUsage>>>(array, arrayCount);
  checkCudaErrors(cudaEventRecord(end));

  checkCudaErrors(cudaDeviceSynchronize());

  // Calculate occupancy
  //
  potentialOccupancy =
      reportPotentialOccupancy((void *)square, blockSize, dynamicSMemUsage);

  std::cout << "Potential occupancy: " << potentialOccupancy * 100 << "%"
            << std::endl;

  // Report elapsed time
  //
  checkCudaErrors(cudaEventElapsedTime(&elapsedTime, start, end));
  std::cout << "Elapsed time: " << elapsedTime << "ms" << std::endl;

  return 0;
}

////////////////////////////////////////////////////////////////////////////////
// The test
//
// The test generates an array and squares it with a CUDA kernel, then
// verifies the result.
////////////////////////////////////////////////////////////////////////////////
static int test(bool automaticLaunchConfig, const int count = 1000000) {
  int *array;
  int *dArray;
  int size = count * sizeof(int);

  array = new int[count];

  for (int i = 0; i < count; i += 1) {
    array[i] = i;
  }

  checkCudaErrors(cudaMalloc(&dArray, size));
  checkCudaErrors(cudaMemcpy(dArray, array, size, cudaMemcpyHostToDevice));

  for (int i = 0; i < count; i += 1) {
    array[i] = 0;
  }

  launchConfig(dArray, count, automaticLaunchConfig);

  checkCudaErrors(cudaMemcpy(array, dArray, size, cudaMemcpyDeviceToHost));
  checkCudaErrors(cudaFree(dArray));

  // Verify the return data
  //
  for (int i = 0; i < count; i += 1) {
    if (array[i] != i * i) {
      std::cout << "element " << i << " expected " << i * i << " actual "
                << array[i] << std::endl;
      return 1;
    }
  }
  delete[] array;

  return 0;
}

////////////////////////////////////////////////////////////////////////////////
// Sample Main
//
// The sample runs the test with manually configured launch and
// automatically configured launch, and reports the occupancy and
// performance.
////////////////////////////////////////////////////////////////////////////////
int main() {
  int status;

  std::cout << "starting Simple Occupancy" << std::endl << std::endl;

  std::cout << "[ Manual configuration with " << manualBlockSize
            << " threads per block ]" << std::endl;

  status = test(false);
  if (status) {
    std::cerr << "Test failed\n" << std::endl;
    return -1;
  }

  std::cout << std::endl;

  std::cout << "[ Automatic, occupancy-based configuration ]" << std::endl;
  status = test(true);
  if (status) {
    std::cerr << "Test failed\n" << std::endl;
    return -1;
  }

  std::cout << std::endl;
  std::cout << "Test PASSED\n" << std::endl;

  return 0;
}