mirror of
https://github.com/NVIDIA/cuda-samples.git
synced 2024-11-24 19:09:14 +08:00
247 lines
8.3 KiB
Plaintext
247 lines
8.3 KiB
Plaintext
/* 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 <cstdio>
|
|
#include <helper_cuda.h>
|
|
#include <helper_string.h>
|
|
|
|
#define MAX_DEPTH 16
|
|
#define INSERTION_SORT 32
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
// Selection sort used when depth gets too big or the number of elements drops
|
|
// below a threshold.
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
__device__ void selection_sort(unsigned int *data, int left, int right) {
|
|
for (int i = left; i <= right; ++i) {
|
|
unsigned min_val = data[i];
|
|
int min_idx = i;
|
|
|
|
// Find the smallest value in the range [left, right].
|
|
for (int j = i + 1; j <= right; ++j) {
|
|
unsigned val_j = data[j];
|
|
|
|
if (val_j < min_val) {
|
|
min_idx = j;
|
|
min_val = val_j;
|
|
}
|
|
}
|
|
|
|
// Swap the values.
|
|
if (i != min_idx) {
|
|
data[min_idx] = data[i];
|
|
data[i] = min_val;
|
|
}
|
|
}
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
// Very basic quicksort algorithm, recursively launching the next level.
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
__global__ void cdp_simple_quicksort(unsigned int *data, int left, int right,
|
|
int depth) {
|
|
// If we're too deep or there are few elements left, we use an insertion
|
|
// sort...
|
|
if (depth >= MAX_DEPTH || right - left <= INSERTION_SORT) {
|
|
selection_sort(data, left, right);
|
|
return;
|
|
}
|
|
|
|
unsigned int *lptr = data + left;
|
|
unsigned int *rptr = data + right;
|
|
unsigned int pivot = data[(left + right) / 2];
|
|
|
|
// Do the partitioning.
|
|
while (lptr <= rptr) {
|
|
// Find the next left- and right-hand values to swap
|
|
unsigned int lval = *lptr;
|
|
unsigned int rval = *rptr;
|
|
|
|
// Move the left pointer as long as the pointed element is smaller than the
|
|
// pivot.
|
|
while (lval < pivot) {
|
|
lptr++;
|
|
lval = *lptr;
|
|
}
|
|
|
|
// Move the right pointer as long as the pointed element is larger than the
|
|
// pivot.
|
|
while (rval > pivot) {
|
|
rptr--;
|
|
rval = *rptr;
|
|
}
|
|
|
|
// If the swap points are valid, do the swap!
|
|
if (lptr <= rptr) {
|
|
*lptr++ = rval;
|
|
*rptr-- = lval;
|
|
}
|
|
}
|
|
|
|
// Now the recursive part
|
|
int nright = rptr - data;
|
|
int nleft = lptr - data;
|
|
|
|
// Launch a new block to sort the left part.
|
|
if (left < (rptr - data)) {
|
|
cudaStream_t s;
|
|
cudaStreamCreateWithFlags(&s, cudaStreamNonBlocking);
|
|
cdp_simple_quicksort<<<1, 1, 0, s>>>(data, left, nright, depth + 1);
|
|
cudaStreamDestroy(s);
|
|
}
|
|
|
|
// Launch a new block to sort the right part.
|
|
if ((lptr - data) < right) {
|
|
cudaStream_t s1;
|
|
cudaStreamCreateWithFlags(&s1, cudaStreamNonBlocking);
|
|
cdp_simple_quicksort<<<1, 1, 0, s1>>>(data, nleft, right, depth + 1);
|
|
cudaStreamDestroy(s1);
|
|
}
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
// Call the quicksort kernel from the host.
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
void run_qsort(unsigned int *data, unsigned int nitems) {
|
|
// Prepare CDP for the max depth 'MAX_DEPTH'.
|
|
checkCudaErrors(cudaDeviceSetLimit(cudaLimitDevRuntimeSyncDepth, MAX_DEPTH));
|
|
|
|
// Launch on device
|
|
int left = 0;
|
|
int right = nitems - 1;
|
|
std::cout << "Launching kernel on the GPU" << std::endl;
|
|
cdp_simple_quicksort<<<1, 1>>>(data, left, right, 0);
|
|
checkCudaErrors(cudaDeviceSynchronize());
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
// Initialize data on the host.
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
void initialize_data(unsigned int *dst, unsigned int nitems) {
|
|
// Fixed seed for illustration
|
|
srand(2047);
|
|
|
|
// Fill dst with random values
|
|
for (unsigned i = 0; i < nitems; i++) dst[i] = rand() % nitems;
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
// Verify the results.
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
void check_results(int n, unsigned int *results_d) {
|
|
unsigned int *results_h = new unsigned[n];
|
|
checkCudaErrors(cudaMemcpy(results_h, results_d, n * sizeof(unsigned),
|
|
cudaMemcpyDeviceToHost));
|
|
|
|
for (int i = 1; i < n; ++i)
|
|
if (results_h[i - 1] > results_h[i]) {
|
|
std::cout << "Invalid item[" << i - 1 << "]: " << results_h[i - 1]
|
|
<< " greater than " << results_h[i] << std::endl;
|
|
exit(EXIT_FAILURE);
|
|
}
|
|
|
|
std::cout << "OK" << std::endl;
|
|
delete[] results_h;
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
// Main entry point.
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
int main(int argc, char **argv) {
|
|
int num_items = 128;
|
|
bool verbose = false;
|
|
|
|
if (checkCmdLineFlag(argc, (const char **)argv, "help") ||
|
|
checkCmdLineFlag(argc, (const char **)argv, "h")) {
|
|
std::cerr << "Usage: " << argv[0]
|
|
<< " num_items=<num_items>\twhere num_items is the number of "
|
|
"items to sort"
|
|
<< std::endl;
|
|
exit(EXIT_SUCCESS);
|
|
}
|
|
|
|
if (checkCmdLineFlag(argc, (const char **)argv, "v")) {
|
|
verbose = true;
|
|
}
|
|
|
|
if (checkCmdLineFlag(argc, (const char **)argv, "num_items")) {
|
|
num_items = getCmdLineArgumentInt(argc, (const char **)argv, "num_items");
|
|
|
|
if (num_items < 1) {
|
|
std::cerr << "ERROR: num_items has to be greater than 1" << std::endl;
|
|
exit(EXIT_FAILURE);
|
|
}
|
|
}
|
|
|
|
// Find/set device and get device properties
|
|
int device = -1;
|
|
cudaDeviceProp deviceProp;
|
|
device = findCudaDevice(argc, (const char **)argv);
|
|
checkCudaErrors(cudaGetDeviceProperties(&deviceProp, device));
|
|
|
|
if (!(deviceProp.major > 3 ||
|
|
(deviceProp.major == 3 && deviceProp.minor >= 5))) {
|
|
printf("GPU %d - %s does not support CUDA Dynamic Parallelism\n Exiting.",
|
|
device, deviceProp.name);
|
|
exit(EXIT_WAIVED);
|
|
}
|
|
|
|
// Create input data
|
|
unsigned int *h_data = 0;
|
|
unsigned int *d_data = 0;
|
|
|
|
// Allocate CPU memory and initialize data.
|
|
std::cout << "Initializing data:" << std::endl;
|
|
h_data = (unsigned int *)malloc(num_items * sizeof(unsigned int));
|
|
initialize_data(h_data, num_items);
|
|
|
|
if (verbose) {
|
|
for (int i = 0; i < num_items; i++)
|
|
std::cout << "Data [" << i << "]: " << h_data[i] << std::endl;
|
|
}
|
|
|
|
// Allocate GPU memory.
|
|
checkCudaErrors(
|
|
cudaMalloc((void **)&d_data, num_items * sizeof(unsigned int)));
|
|
checkCudaErrors(cudaMemcpy(d_data, h_data, num_items * sizeof(unsigned int),
|
|
cudaMemcpyHostToDevice));
|
|
|
|
// Execute
|
|
std::cout << "Running quicksort on " << num_items << " elements" << std::endl;
|
|
run_qsort(d_data, num_items);
|
|
|
|
// Check result
|
|
std::cout << "Validating results: ";
|
|
check_results(num_items, d_data);
|
|
|
|
free(h_data);
|
|
checkCudaErrors(cudaFree(d_data));
|
|
|
|
exit(EXIT_SUCCESS);
|
|
}
|