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241 lines
7.9 KiB
Plaintext
241 lines
7.9 KiB
Plaintext
/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions
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* are met:
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* * Redistributions of source code must retain the above copyright
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* notice, this list of conditions and the following disclaimer.
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* * Redistributions in binary form must reproduce the above copyright
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* notice, this list of conditions and the following disclaimer in the
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* documentation and/or other materials provided with the distribution.
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* * Neither the name of NVIDIA CORPORATION nor the names of its
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* contributors may be used to endorse or promote products derived
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* from this software without specific prior written permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
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* EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
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* PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
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* CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
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* EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
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* PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
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* PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
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* OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*/
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#include <iostream>
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#include <helper_cuda.h> // helper functions for CUDA error check
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const int manualBlockSize = 32;
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////////////////////////////////////////////////////////////////////////////////
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// Test kernel
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//
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// This kernel squares each array element. Each thread addresses
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// himself with threadIdx and blockIdx, so that it can handle any
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// execution configuration, including anything the launch configurator
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// API suggests.
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////////////////////////////////////////////////////////////////////////////////
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__global__ void square(int *array, int arrayCount) {
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extern __shared__ int dynamicSmem[];
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int idx = threadIdx.x + blockIdx.x * blockDim.x;
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if (idx < arrayCount) {
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array[idx] *= array[idx];
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}
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}
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////////////////////////////////////////////////////////////////////////////////
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// Potential occupancy calculator
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//
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// The potential occupancy is calculated according to the kernel and
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// execution configuration the user desires. Occupancy is defined in
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// terms of active blocks per multiprocessor, and the user can convert
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// it to other metrics.
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//
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// This wrapper routine computes the occupancy of kernel, and reports
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// it in terms of active warps / maximum warps per SM.
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////////////////////////////////////////////////////////////////////////////////
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static double reportPotentialOccupancy(void *kernel, int blockSize,
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size_t dynamicSMem) {
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int device;
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cudaDeviceProp prop;
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int numBlocks;
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int activeWarps;
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int maxWarps;
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double occupancy;
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checkCudaErrors(cudaGetDevice(&device));
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checkCudaErrors(cudaGetDeviceProperties(&prop, device));
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checkCudaErrors(cudaOccupancyMaxActiveBlocksPerMultiprocessor(
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&numBlocks, kernel, blockSize, dynamicSMem));
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activeWarps = numBlocks * blockSize / prop.warpSize;
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maxWarps = prop.maxThreadsPerMultiProcessor / prop.warpSize;
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occupancy = (double)activeWarps / maxWarps;
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return occupancy;
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}
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////////////////////////////////////////////////////////////////////////////////
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// Occupancy-based launch configurator
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//
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// The launch configurator, cudaOccupancyMaxPotentialBlockSize and
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// cudaOccupancyMaxPotentialBlockSizeVariableSMem, suggests a block
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// size that achieves the best theoretical occupancy. It also returns
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// the minimum number of blocks needed to achieve the occupancy on the
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// whole device.
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//
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// This launch configurator is purely occupancy-based. It doesn't
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// translate directly to performance, but the suggestion should
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// nevertheless be a good starting point for further optimizations.
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//
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// This function configures the launch based on the "automatic"
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// argument, records the runtime, and reports occupancy and runtime.
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////////////////////////////////////////////////////////////////////////////////
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static int launchConfig(int *array, int arrayCount, bool automatic) {
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int blockSize;
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int minGridSize;
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int gridSize;
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size_t dynamicSMemUsage = 0;
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cudaEvent_t start;
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cudaEvent_t end;
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float elapsedTime;
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double potentialOccupancy;
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checkCudaErrors(cudaEventCreate(&start));
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checkCudaErrors(cudaEventCreate(&end));
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if (automatic) {
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checkCudaErrors(cudaOccupancyMaxPotentialBlockSize(
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&minGridSize, &blockSize, (void *)square, dynamicSMemUsage,
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arrayCount));
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std::cout << "Suggested block size: " << blockSize << std::endl
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<< "Minimum grid size for maximum occupancy: " << minGridSize
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<< std::endl;
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} else {
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// This block size is too small. Given limited number of
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// active blocks per multiprocessor, the number of active
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// threads will be limited, and thus unable to achieve maximum
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// occupancy.
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//
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blockSize = manualBlockSize;
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}
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// Round up
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//
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gridSize = (arrayCount + blockSize - 1) / blockSize;
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// Launch and profile
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//
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checkCudaErrors(cudaEventRecord(start));
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square<<<gridSize, blockSize, dynamicSMemUsage>>>(array, arrayCount);
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checkCudaErrors(cudaEventRecord(end));
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checkCudaErrors(cudaDeviceSynchronize());
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// Calculate occupancy
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//
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potentialOccupancy =
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reportPotentialOccupancy((void *)square, blockSize, dynamicSMemUsage);
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std::cout << "Potential occupancy: " << potentialOccupancy * 100 << "%"
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<< std::endl;
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// Report elapsed time
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//
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checkCudaErrors(cudaEventElapsedTime(&elapsedTime, start, end));
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std::cout << "Elapsed time: " << elapsedTime << "ms" << std::endl;
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return 0;
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}
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////////////////////////////////////////////////////////////////////////////////
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// The test
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//
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// The test generates an array and squares it with a CUDA kernel, then
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// verifies the result.
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////////////////////////////////////////////////////////////////////////////////
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static int test(bool automaticLaunchConfig, const int count = 1000000) {
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int *array;
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int *dArray;
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int size = count * sizeof(int);
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array = new int[count];
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for (int i = 0; i < count; i += 1) {
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array[i] = i;
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}
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checkCudaErrors(cudaMalloc(&dArray, size));
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checkCudaErrors(cudaMemcpy(dArray, array, size, cudaMemcpyHostToDevice));
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for (int i = 0; i < count; i += 1) {
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array[i] = 0;
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}
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launchConfig(dArray, count, automaticLaunchConfig);
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checkCudaErrors(cudaMemcpy(array, dArray, size, cudaMemcpyDeviceToHost));
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checkCudaErrors(cudaFree(dArray));
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// Verify the return data
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//
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for (int i = 0; i < count; i += 1) {
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if (array[i] != i * i) {
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std::cout << "element " << i << " expected " << i * i << " actual "
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<< array[i] << std::endl;
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return 1;
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}
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}
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delete[] array;
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return 0;
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}
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////////////////////////////////////////////////////////////////////////////////
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// Sample Main
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//
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// The sample runs the test with manually configured launch and
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// automatically configured launch, and reports the occupancy and
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// performance.
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////////////////////////////////////////////////////////////////////////////////
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int main() {
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int status;
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std::cout << "starting Simple Occupancy" << std::endl << std::endl;
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std::cout << "[ Manual configuration with " << manualBlockSize
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<< " threads per block ]" << std::endl;
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status = test(false);
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if (status) {
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std::cerr << "Test failed\n" << std::endl;
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return -1;
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}
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std::cout << std::endl;
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std::cout << "[ Automatic, occupancy-based configuration ]" << std::endl;
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status = test(true);
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if (status) {
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std::cerr << "Test failed\n" << std::endl;
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return -1;
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}
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std::cout << std::endl;
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std::cout << "Test PASSED\n" << std::endl;
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return 0;
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}
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