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I have an array of vertices with this kind of structure:
[x0, y0, z0, empty float, x1, y1, z1, empty float, x2, y2, z2, empty float, ...]
I need to find minX, minY, minZ, maxX, maxY and maxZ using CUDA. I wrote a proper reduction algorithm, but it occurs to be a little too slow. I decided to use the THRUST library. There is a highly optimized reduce(), or even better minmax_element(), method which is a way to find max and min of an array simultaneously, but I can't find a fast way to use then only every 4th index. Copying data to 3 separated arrays is not a solution I'm looking for.
Is there a way (some kind of tricks with Thrust iterators or something like this) to pass a stride to reduce()?
634
You can use a strided range method, along with 3 calls to thrust::minmax_element, to get your desired result without modifying data storage.
Here's a worked example:
$ cat t491.cu
#include <thrust/device_vector.h>
#include <thrust/host_vector.h>
#include <iostream>
#include <thrust/copy.h>
#include <thrust/iterator/permutation_iterator.h>
#include <thrust/iterator/counting_iterator.h>
#include <thrust/iterator/transform_iterator.h>
#include <thrust/functional.h>
#include <thrust/extrema.h>
#include <thrust/transform_reduce.h>
#define DSIZE (1048576*2)
#define SSIZE 4
#define FSIZE (DSIZE*SSIZE)
#define nTPB 256
#define BSIZE nTPB
#define nBLKS 64
#define FLOAT_MIN (-99999)
#define FLOAT_MAX 99999
typedef thrust::tuple<float, float, float, float, float, float> tpl6;
struct expand_functor
{
__host__ __device__
tpl6 operator()(const float4 a){
tpl6 result;
result.get<0>() = a.x;
result.get<1>() = a.x;
result.get<2>() = a.y;
result.get<3>() = a.y;
result.get<4>() = a.z;
result.get<5>() = a.z;
return result;
}
};
struct minmax3_functor
{
__host__ __device__
tpl6 operator()(const tpl6 a, const tpl6 b) {
tpl6 result;
result.get<0>() = (a.get<0>() < b.get<0>()) ? a.get<0>():b.get<0>();
result.get<1>() = (a.get<1>() > b.get<1>()) ? a.get<1>():b.get<1>();
result.get<2>() = (a.get<2>() < b.get<2>()) ? a.get<2>():b.get<2>();
result.get<3>() = (a.get<3>() > b.get<3>()) ? a.get<3>():b.get<3>();
result.get<4>() = (a.get<4>() < b.get<4>()) ? a.get<4>():b.get<4>();
result.get<5>() = (a.get<5>() > b.get<5>()) ? a.get<5>():b.get<5>();
return result;
}
};
__device__ int bcount = 0;
__device__ float xmins[nBLKS];
__device__ float xmaxs[nBLKS];
__device__ float ymins[nBLKS];
__device__ float ymaxs[nBLKS];
__device__ float zmins[nBLKS];
__device__ float zmaxs[nBLKS];
__global__ void my_minmax3(float4 *data, float *results, size_t dsize){
// assumes power-of-2 threadblock size
// assumes nBLKS <= nTPB, nBLKS also power-of-2
__shared__ float xmin[BSIZE], xmax[BSIZE], ymin[BSIZE], ymax[BSIZE], zmin[BSIZE], zmax[BSIZE];
__shared__ int lblock;
float my_xmin = FLOAT_MAX;
float my_ymin = FLOAT_MAX;
float my_zmin = FLOAT_MAX;
float my_xmax = FLOAT_MIN;
float my_ymax = FLOAT_MIN;
float my_zmax = FLOAT_MIN;
int idx = threadIdx.x+blockDim.x*blockIdx.x;
while (idx < dsize){
float4 my_temp = data[idx];
if (my_xmin > my_temp.x) my_xmin = my_temp.x;
if (my_ymin > my_temp.y) my_ymin = my_temp.y;
if (my_zmin > my_temp.z) my_zmin = my_temp.z;
if (my_xmax < my_temp.x) my_xmax = my_temp.x;
if (my_ymax < my_temp.y) my_ymax = my_temp.y;
if (my_zmax < my_temp.z) my_zmax = my_temp.z;
idx += blockDim.x*gridDim.x;}
xmin[threadIdx.x] = my_xmin;
ymin[threadIdx.x] = my_ymin;
zmin[threadIdx.x] = my_zmin;
xmax[threadIdx.x] = my_xmax;
ymax[threadIdx.x] = my_ymax;
zmax[threadIdx.x] = my_zmax;
__syncthreads();
for (int i = blockDim.x/2; i > 0; i>>=1){
if (threadIdx.x < i){
if (xmin[threadIdx.x] > xmin[threadIdx.x+i]) xmin[threadIdx.x] = xmin[threadIdx.x + i];
if (ymin[threadIdx.x] > ymin[threadIdx.x+i]) ymin[threadIdx.x] = ymin[threadIdx.x + i];
if (zmin[threadIdx.x] > zmin[threadIdx.x+i]) zmin[threadIdx.x] = zmin[threadIdx.x + i];
if (xmax[threadIdx.x] < xmax[threadIdx.x+i]) xmax[threadIdx.x] = xmax[threadIdx.x + i];
if (ymax[threadIdx.x] < ymax[threadIdx.x+i]) ymax[threadIdx.x] = ymax[threadIdx.x + i];
if (zmax[threadIdx.x] < zmax[threadIdx.x+i]) zmax[threadIdx.x] = zmax[threadIdx.x + i];
}
__syncthreads();
}
if (!threadIdx.x){
xmins[blockIdx.x] = xmin[0];
xmaxs[blockIdx.x] = xmax[0];
ymins[blockIdx.x] = ymin[0];
ymaxs[blockIdx.x] = ymax[0];
zmins[blockIdx.x] = zmin[0];
zmaxs[blockIdx.x] = zmax[0];
__threadfence();
if (atomicAdd(&bcount, 1) == (nBLKS-1)) lblock = 1;
else lblock = 0;
}
__syncthreads();
if (lblock){ // last block does final reduction
if (threadIdx.x < nBLKS){
xmin[threadIdx.x] = xmins[threadIdx.x];
xmax[threadIdx.x] = xmaxs[threadIdx.x];
ymin[threadIdx.x] = ymins[threadIdx.x];
ymax[threadIdx.x] = ymaxs[threadIdx.x];
zmin[threadIdx.x] = zmins[threadIdx.x];
zmax[threadIdx.x] = zmaxs[threadIdx.x];}
__syncthreads();
for (int i = nBLKS/2; i > 0; i>>=1){
if (threadIdx.x < i){
if (xmin[threadIdx.x] > xmin[threadIdx.x+i]) xmin[threadIdx.x] = xmin[threadIdx.x + i];
if (ymin[threadIdx.x] > ymin[threadIdx.x+i]) ymin[threadIdx.x] = ymin[threadIdx.x + i];
if (zmin[threadIdx.x] > zmin[threadIdx.x+i]) zmin[threadIdx.x] = zmin[threadIdx.x + i];
if (xmax[threadIdx.x] < xmax[threadIdx.x+i]) xmax[threadIdx.x] = xmax[threadIdx.x + i];
if (ymax[threadIdx.x] < ymax[threadIdx.x+i]) ymax[threadIdx.x] = ymax[threadIdx.x + i];
if (zmax[threadIdx.x] < zmax[threadIdx.x+i]) zmax[threadIdx.x] = zmax[threadIdx.x + i];
}
__syncthreads();
}
if (!threadIdx.x){
results[0] = xmin[0];
results[1] = xmax[0];
results[2] = ymin[0];
results[3] = ymax[0];
results[4] = zmin[0];
results[5] = zmax[0];
}
}
}
template <typename Iterator>
class strided_range
{
public:
typedef typename thrust::iterator_difference<Iterator>::type difference_type;
struct stride_functor : public thrust::unary_function<difference_type,difference_type>
{
difference_type stride;
stride_functor(difference_type stride)
: stride(stride) {}
__host__ __device__
difference_type operator()(const difference_type& i) const
{
return stride * i;
}
};
typedef typename thrust::counting_iterator<difference_type> CountingIterator;
typedef typename thrust::transform_iterator<stride_functor, CountingIterator> TransformIterator;
typedef typename thrust::permutation_iterator<Iterator,TransformIterator> PermutationIterator;
// type of the strided_range iterator
typedef PermutationIterator iterator;
// construct strided_range for the range [first,last)
strided_range(Iterator first, Iterator last, difference_type stride)
: first(first), last(last), stride(stride) {}
iterator begin(void) const
{
return PermutationIterator(first, TransformIterator(CountingIterator(0), stride_functor(stride)));
}
iterator end(void) const
{
return begin() + ((last - first) + (stride - 1)) / stride;
}
protected:
Iterator first;
Iterator last;
difference_type stride;
};
typedef thrust::device_vector<float>::iterator Iter;
typedef strided_range<Iter>::iterator sIter;
int main(){
// set up test data
cudaEvent_t start, stop;
float et;
cudaEventCreate(&start); cudaEventCreate(&stop);
thrust::host_vector<float> h_vals(FSIZE);
for (int i = 0; i < DSIZE; i ++) {
h_vals[i*SSIZE + 0] = rand()/(float)RAND_MAX;
h_vals[i*SSIZE + 1] = rand()/(float)RAND_MAX;
h_vals[i*SSIZE + 2] = rand()/(float)RAND_MAX;
h_vals[i*SSIZE + 3] = 0.0f;}
thrust::device_vector<float> d_vals = h_vals;
// set up strided ranges
strided_range<Iter> item_x(d_vals.begin() , d_vals.end(), SSIZE);
strided_range<Iter> item_y(d_vals.begin()+1, d_vals.end(), SSIZE);
strided_range<Iter> item_z(d_vals.begin()+2, d_vals.end(), SSIZE);
// find min and max
cudaEventRecord(start);
thrust::pair<sIter, sIter> result_x = thrust::minmax_element(item_x.begin(), item_x.end());
thrust::pair<sIter, sIter> result_y = thrust::minmax_element(item_y.begin(), item_y.end());
thrust::pair<sIter, sIter> result_z = thrust::minmax_element(item_z.begin(), item_z.end());
cudaEventRecord(stop);
cudaEventSynchronize(stop);
cudaEventElapsedTime(&et, start, stop);
std::cout << "thrust elapsed time: " << et << "ms" << std::endl;
std::cout << "thrust results: " << std::endl;
std::cout << "x min element = " << *(result_x.first) << std::endl;
std::cout << "x max element = " << *(result_x.second) << std::endl;
std::cout << "y min element = " << *(result_y.first) << std::endl;
std::cout << "y max element = " << *(result_y.second) << std::endl;
std::cout << "z min element = " << *(result_z.first) << std::endl;
std::cout << "z max element = " << *(result_z.second) << std::endl;
float *h_results, *d_results;
h_results = new float[6];
cudaMalloc(&d_results, 6*sizeof(float));
cudaEventRecord(start);
my_minmax3<<<nBLKS,nTPB>>>((float4 *)thrust::raw_pointer_cast(d_vals.data()), d_results, DSIZE);
cudaEventRecord(stop);
cudaEventSynchronize(stop);
cudaEventElapsedTime(&et, start, stop);
cudaMemcpy(h_results, d_results, 6*sizeof(float), cudaMemcpyDeviceToHost);
std::cout << "kernel elapsed time: " << et << "ms" << std::endl;
std::cout << "kernel results: " << std::endl;
std::cout << "x min element = " << h_results[0] << std::endl;
std::cout << "x max element = " << h_results[1] << std::endl;
std::cout << "y min element = " << h_results[2] << std::endl;
std::cout << "y max element = " << h_results[3] << std::endl;
std::cout << "z min element = " << h_results[4] << std::endl;
std::cout << "z max element = " << h_results[5] << std::endl;
thrust::device_ptr<float4> dptr_vals = thrust::device_pointer_cast(reinterpret_cast<float4 *>( thrust::raw_pointer_cast(d_vals.data())));
tpl6 my_init;
my_init.get<0>() = FLOAT_MAX;
my_init.get<1>() = FLOAT_MIN;
my_init.get<2>() = FLOAT_MAX;
my_init.get<3>() = FLOAT_MIN;
my_init.get<4>() = FLOAT_MAX;
my_init.get<5>() = FLOAT_MIN;
cudaEventRecord(start);
tpl6 my_result = thrust::transform_reduce(dptr_vals, dptr_vals + DSIZE, expand_functor(), my_init, minmax3_functor());
cudaEventRecord(stop);
cudaEventSynchronize(stop);
cudaEventElapsedTime(&et, start, stop);
cudaMemcpy(h_results, d_results, 6*sizeof(float), cudaMemcpyDeviceToHost);
std::cout << "thrust2 elapsed time: " << et << "ms" << std::endl;
std::cout << "thrust2 results: " << std::endl;
std::cout << "x min element = " << my_result.get<0>() << std::endl;
std::cout << "x max element = " << my_result.get<1>() << std::endl;
std::cout << "y min element = " << my_result.get<2>() << std::endl;
std::cout << "y max element = " << my_result.get<3>() << std::endl;
std::cout << "z min element = " << my_result.get<4>() << std::endl;
std::cout << "z max element = " << my_result.get<5>() << std::endl;
return 0;
}
$ nvcc -O3 -arch=sm_20 -o t491 t491.cu
$ ./t491
thrust elapsed time: 3.88784ms
thrust results:
x min element = 1.16788e-06
x max element = 0.999998
y min element = 2.85916e-07
y max element = 1
z min element = 1.72295e-08
z max element = 0.999999
kernel elapsed time: 0.462848ms
kernel results:
x min element = 1.16788e-06
x max element = 0.999998
y min element = 2.85916e-07
y max element = 1
z min element = 1.72295e-08
z max element = 0.999999
thrust2 elapsed time: 1.29728ms
thrust2 results:
x min element = 1.16788e-06
x max element = 0.999998
y min element = 2.85916e-07
y max element = 1
z min element = 1.72295e-08
z max element = 0.999999
$
I've updated the above example to include for comparison an "optimized" reduction kernel that does all 6 reductions (min and max operations) in a single kernel call.
As expected, this approach runs faster than 3 individual thrust calls to produce the same result, about 5-8 times faster in my case (RHEL5.5, Quadro5000, CUDA 6.5RC), depending on data size. Note that although I have chosen a data size (DSIZE) here that is a power of 2, the entire example works correctly for arbitrary data sizes. I have dispensed with proper cuda error checking for the sake of brevity of presentation.
EDIT: Following the suggestion by @JaredHoberock I have added a 3rd approach that allows a single call to thrust::transform_reduce to produce all 6 results. These are the "thrust2" results above. This method is about 3x faster than the first (three-thrust-call) method. Still not as fast as the cuda kernel method, but perhaps this thrust approach can be optimized further.
89
This is an application of the strided range example.
#include <thrust/iterator/counting_iterator.h>
#include <thrust/iterator/transform_iterator.h>
#include <thrust/iterator/permutation_iterator.h>
#include <thrust/functional.h>
#include <thrust/fill.h>
#include <thrust/device_vector.h>
#include <thrust/host_vector.h>
// for printing
#include <thrust/copy.h>
#include <ostream>
#define STRIDE 2
template <typename Iterator>
class strided_range
{
public:
typedef typename thrust::iterator_difference<Iterator>::type difference_type;
struct stride_functor : public thrust::unary_function<difference_type,difference_type>
{
difference_type stride;
stride_functor(difference_type stride)
: stride(stride) {}
__host__ __device__
difference_type operator()(const difference_type& i) const
{
return stride * i;
}
};
typedef typename thrust::counting_iterator<difference_type> CountingIterator;
typedef typename thrust::transform_iterator<stride_functor, CountingIterator> TransformIterator;
typedef typename thrust::permutation_iterator<Iterator,TransformIterator> PermutationIterator;
// type of the strided_range iterator
typedef PermutationIterator iterator;
// construct strided_range for the range [first,last)
strided_range(Iterator first, Iterator last, difference_type stride)
: first(first), last(last), stride(stride) {}
iterator begin(void) const
{
return PermutationIterator(first, TransformIterator(CountingIterator(0), stride_functor(stride)));
}
iterator end(void) const
{
return begin() + ((last - first) + (stride - 1)) / stride;
}
protected:
Iterator first;
Iterator last;
difference_type stride;
};
int main(void)
{
const int N = 9;
thrust::host_vector<int> h_data(N);
for (int i=0; i<N; i++) h_data[i] = i;
thrust::device_vector<int> data(h_data);
typedef thrust::device_vector<int>::iterator Iterator;
strided_range<Iterator> pos(data.begin(), data.end(), STRIDE);
int sum = thrust::reduce(pos.begin(), pos.end(), (int) 0, thrust::plus<int>());
printf("sum = %i\n",sum);
return 0;
}
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