cuda-samples/Samples/3_CUDA_Features/cdpQuadtree/cdpQuadtree.cu

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/* Copyright (c) 2022, 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 <thrust/random.h>
#include <thrust/device_vector.h>
#include <thrust/host_vector.h>
#include <cooperative_groups.h>
namespace cg = cooperative_groups;
#include <helper_cuda.h>
////////////////////////////////////////////////////////////////////////////////
// A structure of 2D points (structure of arrays).
////////////////////////////////////////////////////////////////////////////////
class Points {
float *m_x;
float *m_y;
public:
// Constructor.
__host__ __device__ Points() : m_x(NULL), m_y(NULL) {}
// Constructor.
__host__ __device__ Points(float *x, float *y) : m_x(x), m_y(y) {}
// Get a point.
__host__ __device__ __forceinline__ float2 get_point(int idx) const {
return make_float2(m_x[idx], m_y[idx]);
}
// Set a point.
__host__ __device__ __forceinline__ void set_point(int idx, const float2 &p) {
m_x[idx] = p.x;
m_y[idx] = p.y;
}
// Set the pointers.
__host__ __device__ __forceinline__ void set(float *x, float *y) {
m_x = x;
m_y = y;
}
};
////////////////////////////////////////////////////////////////////////////////
// A 2D bounding box
////////////////////////////////////////////////////////////////////////////////
class Bounding_box {
// Extreme points of the bounding box.
float2 m_p_min;
float2 m_p_max;
public:
// Constructor. Create a unit box.
__host__ __device__ Bounding_box() {
m_p_min = make_float2(0.0f, 0.0f);
m_p_max = make_float2(1.0f, 1.0f);
}
// Compute the center of the bounding-box.
__host__ __device__ void compute_center(float2 &center) const {
center.x = 0.5f * (m_p_min.x + m_p_max.x);
center.y = 0.5f * (m_p_min.y + m_p_max.y);
}
// The points of the box.
__host__ __device__ __forceinline__ const float2 &get_max() const {
return m_p_max;
}
__host__ __device__ __forceinline__ const float2 &get_min() const {
return m_p_min;
}
// Does a box contain a point.
__host__ __device__ bool contains(const float2 &p) const {
return p.x >= m_p_min.x && p.x < m_p_max.x && p.y >= m_p_min.y &&
p.y < m_p_max.y;
}
// Define the bounding box.
__host__ __device__ void set(float min_x, float min_y, float max_x,
float max_y) {
m_p_min.x = min_x;
m_p_min.y = min_y;
m_p_max.x = max_x;
m_p_max.y = max_y;
}
};
////////////////////////////////////////////////////////////////////////////////
// A node of a quadree.
////////////////////////////////////////////////////////////////////////////////
class Quadtree_node {
// The identifier of the node.
int m_id;
// The bounding box of the tree.
Bounding_box m_bounding_box;
// The range of points.
int m_begin, m_end;
public:
// Constructor.
__host__ __device__ Quadtree_node() : m_id(0), m_begin(0), m_end(0) {}
// The ID of a node at its level.
__host__ __device__ int id() const { return m_id; }
// The ID of a node at its level.
__host__ __device__ void set_id(int new_id) { m_id = new_id; }
// The bounding box.
__host__ __device__ __forceinline__ const Bounding_box &bounding_box() const {
return m_bounding_box;
}
// Set the bounding box.
__host__ __device__ __forceinline__ void set_bounding_box(float min_x,
float min_y,
float max_x,
float max_y) {
m_bounding_box.set(min_x, min_y, max_x, max_y);
}
// The number of points in the tree.
__host__ __device__ __forceinline__ int num_points() const {
return m_end - m_begin;
}
// The range of points in the tree.
__host__ __device__ __forceinline__ int points_begin() const {
return m_begin;
}
__host__ __device__ __forceinline__ int points_end() const { return m_end; }
// Define the range for that node.
__host__ __device__ __forceinline__ void set_range(int begin, int end) {
m_begin = begin;
m_end = end;
}
};
////////////////////////////////////////////////////////////////////////////////
// Algorithm parameters.
////////////////////////////////////////////////////////////////////////////////
struct Parameters {
// Choose the right set of points to use as in/out.
int point_selector;
// The number of nodes at a given level (2^k for level k).
int num_nodes_at_this_level;
// The recursion depth.
int depth;
// The max value for depth.
const int max_depth;
// The minimum number of points in a node to stop recursion.
const int min_points_per_node;
// Constructor set to default values.
__host__ __device__ Parameters(int max_depth, int min_points_per_node)
: point_selector(0),
num_nodes_at_this_level(1),
depth(0),
max_depth(max_depth),
min_points_per_node(min_points_per_node) {}
// Copy constructor. Changes the values for next iteration.
__host__ __device__ Parameters(const Parameters &params, bool)
: point_selector((params.point_selector + 1) % 2),
num_nodes_at_this_level(4 * params.num_nodes_at_this_level),
depth(params.depth + 1),
max_depth(params.max_depth),
min_points_per_node(params.min_points_per_node) {}
};
////////////////////////////////////////////////////////////////////////////////
// Build a quadtree on the GPU. Use CUDA Dynamic Parallelism.
//
// The algorithm works as follows. The host (CPU) launches one block of
// NUM_THREADS_PER_BLOCK threads. That block will do the following steps:
//
// 1- Check the number of points and its depth.
//
// We impose a maximum depth to the tree and a minimum number of points per
// node. If the maximum depth is exceeded or the minimum number of points is
// reached. The threads in the block exit.
//
// Before exiting, they perform a buffer swap if it is needed. Indeed, the
// algorithm uses two buffers to permute the points and make sure they are
// properly distributed in the quadtree. By design we want all points to be
// in the first buffer of points at the end of the algorithm. It is the reason
// why we may have to swap the buffer before leavin (if the points are in the
// 2nd buffer).
//
// 2- Count the number of points in each child.
//
// If the depth is not too high and the number of points is sufficient, the
// block has to dispatch the points into four geometrical buckets: Its
// children. For that purpose, we compute the center of the bounding box and
// count the number of points in each quadrant.
//
// The set of points is divided into sections. Each section is given to a
// warp of threads (32 threads). Warps use __ballot and __popc intrinsics
// to count the points. See the Programming Guide for more information about
// those functions.
//
// 3- Scan the warps' results to know the "global" numbers.
//
// Warps work independently from each other. At the end, each warp knows the
// number of points in its section. To know the numbers for the block, the
// block has to run a scan/reduce at the block level. It's a traditional
// approach. The implementation in that sample is not as optimized as what
// could be found in fast radix sorts, for example, but it relies on the same
// idea.
//
// 4- Move points.
//
// Now that the block knows how many points go in each of its 4 children, it
// remains to dispatch the points. It is straightforward.
//
// 5- Launch new blocks.
//
// The block launches four new blocks: One per children. Each of the four blocks
// will apply the same algorithm.
////////////////////////////////////////////////////////////////////////////////
template <int NUM_THREADS_PER_BLOCK>
__global__ void build_quadtree_kernel(Quadtree_node *nodes, Points *points,
Parameters params) {
// Handle to thread block group
cg::thread_block cta = cg::this_thread_block();
// The number of warps in a block.
const int NUM_WARPS_PER_BLOCK = NUM_THREADS_PER_BLOCK / warpSize;
// Shared memory to store the number of points.
extern __shared__ int smem[];
// s_num_pts[4][NUM_WARPS_PER_BLOCK];
// Addresses of shared memory.
volatile int *s_num_pts[4];
for (int i = 0; i < 4; ++i)
s_num_pts[i] = (volatile int *)&smem[i * NUM_WARPS_PER_BLOCK];
// Compute the coordinates of the threads in the block.
const int warp_id = threadIdx.x / warpSize;
const int lane_id = threadIdx.x % warpSize;
// Mask for compaction.
// Same as: asm( "mov.u32 %0, %%lanemask_lt;" : "=r"(lane_mask_lt) );
int lane_mask_lt = (1 << lane_id) - 1;
// The current node.
Quadtree_node &node = nodes[blockIdx.x];
// The number of points in the node.
int num_points = node.num_points();
float2 center;
int range_begin, range_end;
int warp_cnts[4] = {0, 0, 0, 0};
//
// 1- Check the number of points and its depth.
//
// Stop the recursion here. Make sure points[0] contains all the points.
if (params.depth >= params.max_depth ||
num_points <= params.min_points_per_node) {
if (params.point_selector == 1) {
int it = node.points_begin(), end = node.points_end();
for (it += threadIdx.x; it < end; it += NUM_THREADS_PER_BLOCK)
if (it < end) points[0].set_point(it, points[1].get_point(it));
}
return;
}
// Compute the center of the bounding box of the points.
const Bounding_box &bbox = node.bounding_box();
bbox.compute_center(center);
// Find how many points to give to each warp.
int num_points_per_warp = max(
warpSize, (num_points + NUM_WARPS_PER_BLOCK - 1) / NUM_WARPS_PER_BLOCK);
// Each warp of threads will compute the number of points to move to each
// quadrant.
range_begin = node.points_begin() + warp_id * num_points_per_warp;
range_end = min(range_begin + num_points_per_warp, node.points_end());
//
// 2- Count the number of points in each child.
//
// Input points.
const Points &in_points = points[params.point_selector];
cg::thread_block_tile<32> tile32 = cg::tiled_partition<32>(cta);
// Compute the number of points.
for (int range_it = range_begin + tile32.thread_rank();
tile32.any(range_it < range_end); range_it += warpSize) {
// Is it still an active thread?
bool is_active = range_it < range_end;
// Load the coordinates of the point.
float2 p =
is_active ? in_points.get_point(range_it) : make_float2(0.0f, 0.0f);
// Count top-left points.
int num_pts =
__popc(tile32.ballot(is_active && p.x < center.x && p.y >= center.y));
warp_cnts[0] += tile32.shfl(num_pts, 0);
// Count top-right points.
num_pts =
__popc(tile32.ballot(is_active && p.x >= center.x && p.y >= center.y));
warp_cnts[1] += tile32.shfl(num_pts, 0);
// Count bottom-left points.
num_pts =
__popc(tile32.ballot(is_active && p.x < center.x && p.y < center.y));
warp_cnts[2] += tile32.shfl(num_pts, 0);
// Count bottom-right points.
num_pts =
__popc(tile32.ballot(is_active && p.x >= center.x && p.y < center.y));
warp_cnts[3] += tile32.shfl(num_pts, 0);
}
if (tile32.thread_rank() == 0) {
s_num_pts[0][warp_id] = warp_cnts[0];
s_num_pts[1][warp_id] = warp_cnts[1];
s_num_pts[2][warp_id] = warp_cnts[2];
s_num_pts[3][warp_id] = warp_cnts[3];
}
// Make sure warps have finished counting.
cg::sync(cta);
//
// 3- Scan the warps' results to know the "global" numbers.
//
// First 4 warps scan the numbers of points per child (inclusive scan).
if (warp_id < 4) {
int num_pts = tile32.thread_rank() < NUM_WARPS_PER_BLOCK
? s_num_pts[warp_id][tile32.thread_rank()]
: 0;
#pragma unroll
for (int offset = 1; offset < NUM_WARPS_PER_BLOCK; offset *= 2) {
int n = tile32.shfl_up(num_pts, offset);
if (tile32.thread_rank() >= offset) num_pts += n;
}
if (tile32.thread_rank() < NUM_WARPS_PER_BLOCK)
s_num_pts[warp_id][tile32.thread_rank()] = num_pts;
}
cg::sync(cta);
// Compute global offsets.
if (warp_id == 0) {
int sum = s_num_pts[0][NUM_WARPS_PER_BLOCK - 1];
for (int row = 1; row < 4; ++row) {
int tmp = s_num_pts[row][NUM_WARPS_PER_BLOCK - 1];
cg::sync(tile32);
if (tile32.thread_rank() < NUM_WARPS_PER_BLOCK)
s_num_pts[row][tile32.thread_rank()] += sum;
cg::sync(tile32);
sum += tmp;
}
}
cg::sync(cta);
// Make the scan exclusive.
int val = 0;
if (threadIdx.x < 4 * NUM_WARPS_PER_BLOCK) {
val = threadIdx.x == 0 ? 0 : smem[threadIdx.x - 1];
val += node.points_begin();
}
cg::sync(cta);
if (threadIdx.x < 4 * NUM_WARPS_PER_BLOCK) {
smem[threadIdx.x] = val;
}
cg::sync(cta);
//
// 4- Move points.
//
if (!(params.depth >= params.max_depth ||
num_points <= params.min_points_per_node)) {
// Output points.
Points &out_points = points[(params.point_selector + 1) % 2];
warp_cnts[0] = s_num_pts[0][warp_id];
warp_cnts[1] = s_num_pts[1][warp_id];
warp_cnts[2] = s_num_pts[2][warp_id];
warp_cnts[3] = s_num_pts[3][warp_id];
const Points &in_points = points[params.point_selector];
// Reorder points.
for (int range_it = range_begin + tile32.thread_rank();
tile32.any(range_it < range_end); range_it += warpSize) {
// Is it still an active thread?
bool is_active = range_it < range_end;
// Load the coordinates of the point.
float2 p =
is_active ? in_points.get_point(range_it) : make_float2(0.0f, 0.0f);
// Count top-left points.
bool pred = is_active && p.x < center.x && p.y >= center.y;
int vote = tile32.ballot(pred);
int dest = warp_cnts[0] + __popc(vote & lane_mask_lt);
if (pred) out_points.set_point(dest, p);
warp_cnts[0] += tile32.shfl(__popc(vote), 0);
// Count top-right points.
pred = is_active && p.x >= center.x && p.y >= center.y;
vote = tile32.ballot(pred);
dest = warp_cnts[1] + __popc(vote & lane_mask_lt);
if (pred) out_points.set_point(dest, p);
warp_cnts[1] += tile32.shfl(__popc(vote), 0);
// Count bottom-left points.
pred = is_active && p.x < center.x && p.y < center.y;
vote = tile32.ballot(pred);
dest = warp_cnts[2] + __popc(vote & lane_mask_lt);
if (pred) out_points.set_point(dest, p);
warp_cnts[2] += tile32.shfl(__popc(vote), 0);
// Count bottom-right points.
pred = is_active && p.x >= center.x && p.y < center.y;
vote = tile32.ballot(pred);
dest = warp_cnts[3] + __popc(vote & lane_mask_lt);
if (pred) out_points.set_point(dest, p);
warp_cnts[3] += tile32.shfl(__popc(vote), 0);
}
}
cg::sync(cta);
if (tile32.thread_rank() == 0) {
s_num_pts[0][warp_id] = warp_cnts[0];
s_num_pts[1][warp_id] = warp_cnts[1];
s_num_pts[2][warp_id] = warp_cnts[2];
s_num_pts[3][warp_id] = warp_cnts[3];
}
cg::sync(cta);
//
// 5- Launch new blocks.
//
if (!(params.depth >= params.max_depth ||
num_points <= params.min_points_per_node)) {
// The last thread launches new blocks.
if (threadIdx.x == NUM_THREADS_PER_BLOCK - 1) {
// The children.
Quadtree_node *children =
&nodes[params.num_nodes_at_this_level - (node.id() & ~3)];
// The offsets of the children at their level.
int child_offset = 4 * node.id();
// Set IDs.
children[child_offset + 0].set_id(4 * node.id() + 0);
children[child_offset + 1].set_id(4 * node.id() + 1);
children[child_offset + 2].set_id(4 * node.id() + 2);
children[child_offset + 3].set_id(4 * node.id() + 3);
const Bounding_box &bbox = node.bounding_box();
// Points of the bounding-box.
const float2 &p_min = bbox.get_min();
const float2 &p_max = bbox.get_max();
// Set the bounding boxes of the children.
children[child_offset + 0].set_bounding_box(p_min.x, center.y, center.x,
p_max.y); // Top-left.
children[child_offset + 1].set_bounding_box(center.x, center.y, p_max.x,
p_max.y); // Top-right.
children[child_offset + 2].set_bounding_box(p_min.x, p_min.y, center.x,
center.y); // Bottom-left.
children[child_offset + 3].set_bounding_box(center.x, p_min.y, p_max.x,
center.y); // Bottom-right.
// Set the ranges of the children.
children[child_offset + 0].set_range(node.points_begin(),
s_num_pts[0][warp_id]);
children[child_offset + 1].set_range(s_num_pts[0][warp_id],
s_num_pts[1][warp_id]);
children[child_offset + 2].set_range(s_num_pts[1][warp_id],
s_num_pts[2][warp_id]);
children[child_offset + 3].set_range(s_num_pts[2][warp_id],
s_num_pts[3][warp_id]);
// Launch 4 children.
build_quadtree_kernel<NUM_THREADS_PER_BLOCK><<<
4, NUM_THREADS_PER_BLOCK, 4 * NUM_WARPS_PER_BLOCK * sizeof(int)>>>(
&children[child_offset], points, Parameters(params, true));
}
}
}
////////////////////////////////////////////////////////////////////////////////
// Make sure a Quadtree is properly defined.
////////////////////////////////////////////////////////////////////////////////
bool check_quadtree(const Quadtree_node *nodes, int idx, int num_pts,
Points *pts, Parameters params) {
const Quadtree_node &node = nodes[idx];
int num_points = node.num_points();
if (!(params.depth == params.max_depth ||
num_points <= params.min_points_per_node)) {
int num_points_in_children = 0;
num_points_in_children +=
nodes[params.num_nodes_at_this_level + 4 * idx + 0].num_points();
num_points_in_children +=
nodes[params.num_nodes_at_this_level + 4 * idx + 1].num_points();
num_points_in_children +=
nodes[params.num_nodes_at_this_level + 4 * idx + 2].num_points();
num_points_in_children +=
nodes[params.num_nodes_at_this_level + 4 * idx + 3].num_points();
if (num_points_in_children != node.num_points()) return false;
return check_quadtree(&nodes[params.num_nodes_at_this_level], 4 * idx + 0,
num_pts, pts, Parameters(params, true)) &&
check_quadtree(&nodes[params.num_nodes_at_this_level], 4 * idx + 1,
num_pts, pts, Parameters(params, true)) &&
check_quadtree(&nodes[params.num_nodes_at_this_level], 4 * idx + 2,
num_pts, pts, Parameters(params, true)) &&
check_quadtree(&nodes[params.num_nodes_at_this_level], 4 * idx + 3,
num_pts, pts, Parameters(params, true));
}
const Bounding_box &bbox = node.bounding_box();
for (int it = node.points_begin(); it < node.points_end(); ++it) {
if (it >= num_pts) return false;
float2 p = pts->get_point(it);
if (!bbox.contains(p)) return false;
}
return true;
}
////////////////////////////////////////////////////////////////////////////////
// Parallel random number generator.
////////////////////////////////////////////////////////////////////////////////
struct Random_generator {
int count;
__host__ __device__ Random_generator() : count(0) {}
__host__ __device__ unsigned int hash(unsigned int a) {
a = (a + 0x7ed55d16) + (a << 12);
a = (a ^ 0xc761c23c) ^ (a >> 19);
a = (a + 0x165667b1) + (a << 5);
a = (a + 0xd3a2646c) ^ (a << 9);
a = (a + 0xfd7046c5) + (a << 3);
a = (a ^ 0xb55a4f09) ^ (a >> 16);
return a;
}
__host__ __device__ __forceinline__ thrust::tuple<float, float> operator()() {
#ifdef __CUDA_ARCH__
unsigned seed = hash(blockIdx.x * blockDim.x + threadIdx.x + count);
// thrust::generate may call operator() more than once per thread.
// Hence, increment count by grid size to ensure uniqueness of seed
count += blockDim.x * gridDim.x;
#else
unsigned seed = hash(0);
#endif
thrust::default_random_engine rng(seed);
thrust::random::uniform_real_distribution<float> distrib;
return thrust::make_tuple(distrib(rng), distrib(rng));
}
};
////////////////////////////////////////////////////////////////////////////////
// Allocate GPU structs, launch kernel and clean up
////////////////////////////////////////////////////////////////////////////////
bool cdpQuadtree(int warp_size) {
// Constants to control the algorithm.
const int num_points = 1024;
const int max_depth = 8;
const int min_points_per_node = 16;
// Allocate memory for points.
thrust::device_vector<float> x_d0(num_points);
thrust::device_vector<float> x_d1(num_points);
thrust::device_vector<float> y_d0(num_points);
thrust::device_vector<float> y_d1(num_points);
// Generate random points.
Random_generator rnd;
thrust::generate(
thrust::make_zip_iterator(thrust::make_tuple(x_d0.begin(), y_d0.begin())),
thrust::make_zip_iterator(thrust::make_tuple(x_d0.end(), y_d0.end())),
rnd);
// Host structures to analyze the device ones.
Points points_init[2];
points_init[0].set(thrust::raw_pointer_cast(&x_d0[0]),
thrust::raw_pointer_cast(&y_d0[0]));
points_init[1].set(thrust::raw_pointer_cast(&x_d1[0]),
thrust::raw_pointer_cast(&y_d1[0]));
// Allocate memory to store points.
Points *points;
checkCudaErrors(cudaMalloc((void **)&points, 2 * sizeof(Points)));
checkCudaErrors(cudaMemcpy(points, points_init, 2 * sizeof(Points),
cudaMemcpyHostToDevice));
// We could use a close form...
int max_nodes = 0;
for (int i = 0, num_nodes_at_level = 1; i < max_depth;
++i, num_nodes_at_level *= 4)
max_nodes += num_nodes_at_level;
// Allocate memory to store the tree.
Quadtree_node root;
root.set_range(0, num_points);
Quadtree_node *nodes;
checkCudaErrors(
cudaMalloc((void **)&nodes, max_nodes * sizeof(Quadtree_node)));
checkCudaErrors(
cudaMemcpy(nodes, &root, sizeof(Quadtree_node), cudaMemcpyHostToDevice));
// We set the recursion limit for CDP to max_depth.
cudaDeviceSetLimit(cudaLimitDevRuntimeSyncDepth, max_depth);
// Build the quadtree.
Parameters params(max_depth, min_points_per_node);
std::cout << "Launching CDP kernel to build the quadtree" << std::endl;
const int NUM_THREADS_PER_BLOCK = 128; // Do not use less than 128 threads.
const int NUM_WARPS_PER_BLOCK = NUM_THREADS_PER_BLOCK / warp_size;
const size_t smem_size = 4 * NUM_WARPS_PER_BLOCK * sizeof(int);
build_quadtree_kernel<
NUM_THREADS_PER_BLOCK><<<1, NUM_THREADS_PER_BLOCK, smem_size>>>(
nodes, points, params);
checkCudaErrors(cudaGetLastError());
// Copy points to CPU.
thrust::host_vector<float> x_h(x_d0);
thrust::host_vector<float> y_h(y_d0);
Points host_points;
host_points.set(thrust::raw_pointer_cast(&x_h[0]),
thrust::raw_pointer_cast(&y_h[0]));
// Copy nodes to CPU.
Quadtree_node *host_nodes = new Quadtree_node[max_nodes];
checkCudaErrors(cudaMemcpy(host_nodes, nodes,
max_nodes * sizeof(Quadtree_node),
cudaMemcpyDeviceToHost));
// Validate the results.
bool ok = check_quadtree(host_nodes, 0, num_points, &host_points, params);
std::cout << "Results: " << (ok ? "OK" : "FAILED") << std::endl;
// Free CPU memory.
delete[] host_nodes;
// Free memory.
checkCudaErrors(cudaFree(nodes));
checkCudaErrors(cudaFree(points));
return ok;
}
////////////////////////////////////////////////////////////////////////////////
// Main entry point.
////////////////////////////////////////////////////////////////////////////////
int main(int argc, char **argv) {
// Find/set the device.
// The test requires an architecture SM35 or greater (CDP capable).
int cuda_device = findCudaDevice(argc, (const char **)argv);
cudaDeviceProp deviceProps;
checkCudaErrors(cudaGetDeviceProperties(&deviceProps, cuda_device));
int cdpCapable = (deviceProps.major == 3 && deviceProps.minor >= 5) ||
deviceProps.major >= 4;
printf("GPU device %s has compute capabilities (SM %d.%d)\n",
deviceProps.name, deviceProps.major, deviceProps.minor);
if (!cdpCapable) {
std::cerr << "cdpQuadTree requires SM 3.5 or higher to use CUDA Dynamic "
"Parallelism. Exiting...\n"
<< std::endl;
exit(EXIT_WAIVED);
}
bool ok = cdpQuadtree(deviceProps.warpSize);
return (ok ? EXIT_SUCCESS : EXIT_FAILURE);
}