My data like
value = [1, 2, 3, 4, 5, 6]
key = [0, 1, 0, 2, 1, 2]
I need to now maximum(value and index) per each group(key). So the result should be
max = [3, 5, 6]
index = [2, 4, 5]
key = [0, 1, 2]
How can I get it with cuda thrust?
I can do sort -> reduce_by_key but it's not really efficient. In my case vector size > 10M and key space ~ 1K(starts from 0
without gaps).
Since the original question focused on thrust, I didn't have any suggestions other than what I mentioned in the comments,
However, based on further dialog in the comments, I thought I would post an answer that covers both CUDA and thrust.
The thrust method uses a sort_by_key operation to group like keys together, followed by a reduce_by_key operation to find the max + index for each key-group.
The CUDA method uses a custom atomic approach I describe here to find a 32-bit max plus 32-bit index (for each key-group).
The CUDA method is substantially (~10x) faster, for this specific test case. I used a vector size of 10M and a key size of 10K for this test.
My test platform was CUDA 8RC, RHEL 7, and Tesla K20X GPU. K20X is a member of the Kepler generation which has much faster global atomics than previous GPU generations.
Here's a fully worked example, covering both cases, and providing a timing comparison:
$ cat t1234.cu
#include <iostream>
#include <thrust/copy.h>
#include <thrust/reduce.h>
#include <thrust/sort.h>
#include <thrust/device_vector.h>
#include <thrust/iterator/zip_iterator.h>
#include <thrust/sequence.h>
#include <thrust/functional.h>
#include <cstdlib>
#include <time.h>
#include <sys/time.h>
#define USECPSEC 1000000ULL
unsigned long long dtime_usec(unsigned long long start){
timeval tv;
gettimeofday(&tv, 0);
return ((tv.tv_sec*USECPSEC)+tv.tv_usec)-start;
}
const size_t ksize = 10000;
const size_t vsize = 10000000;
const int nTPB = 256;
struct my_max_func
{
template <typename T1, typename T2>
__host__ __device__
T1 operator()(const T1 t1, const T2 t2){
T1 res;
if (thrust::get<0>(t1) > thrust::get<0>(t2)){
thrust::get<0>(res) = thrust::get<0>(t1);
thrust::get<1>(res) = thrust::get<1>(t1);}
else {
thrust::get<0>(res) = thrust::get<0>(t2);
thrust::get<1>(res) = thrust::get<1>(t2);}
return res;
}
};
typedef union {
float floats[2]; // floats[0] = maxvalue
int ints[2]; // ints[1] = maxindex
unsigned long long int ulong; // for atomic update
} my_atomics;
__device__ unsigned long long int my_atomicMax(unsigned long long int* address, float val1, int val2)
{
my_atomics loc, loctest;
loc.floats[0] = val1;
loc.ints[1] = val2;
loctest.ulong = *address;
while (loctest.floats[0] < val1)
loctest.ulong = atomicCAS(address, loctest.ulong, loc.ulong);
return loctest.ulong;
}
__global__ void my_max_idx(const float *data, const int *keys,const int ds, my_atomics *res)
{
int idx = (blockDim.x * blockIdx.x) + threadIdx.x;
if (idx < ds)
my_atomicMax(&(res[keys[idx]].ulong), data[idx],idx);
}
int main(){
float *h_vals = new float[vsize];
int *h_keys = new int[vsize];
for (int i = 0; i < vsize; i++) {h_vals[i] = rand(); h_keys[i] = rand()%ksize;}
// thrust method
thrust::device_vector<float> d_vals(h_vals, h_vals+vsize);
thrust::device_vector<int> d_keys(h_keys, h_keys+vsize);
thrust::device_vector<int> d_keys_out(ksize);
thrust::device_vector<float> d_vals_out(ksize);
thrust::device_vector<int> d_idxs(vsize);
thrust::device_vector<int> d_idxs_out(ksize);
thrust::sequence(d_idxs.begin(), d_idxs.end());
cudaDeviceSynchronize();
unsigned long long et = dtime_usec(0);
thrust::sort_by_key(d_keys.begin(), d_keys.end(), thrust::make_zip_iterator(thrust::make_tuple(d_vals.begin(), d_idxs.begin())));
thrust::reduce_by_key(d_keys.begin(), d_keys.end(), thrust::make_zip_iterator(thrust::make_tuple(d_vals.begin(),d_idxs.begin())), d_keys_out.begin(), thrust::make_zip_iterator(thrust::make_tuple(d_vals_out.begin(), d_idxs_out.begin())), thrust::equal_to<int>(), my_max_func());
cudaDeviceSynchronize();
et = dtime_usec(et);
std::cout << "Thrust time: " << et/(float)USECPSEC << "s" << std::endl;
// cuda method
float *vals;
int *keys;
my_atomics *results;
cudaMalloc(&keys, vsize*sizeof(int));
cudaMalloc(&vals, vsize*sizeof(float));
cudaMalloc(&results, ksize*sizeof(my_atomics));
cudaMemset(results, 0, ksize*sizeof(my_atomics)); // works because vals are all positive
cudaMemcpy(keys, h_keys, vsize*sizeof(int), cudaMemcpyHostToDevice);
cudaMemcpy(vals, h_vals, vsize*sizeof(float), cudaMemcpyHostToDevice);
et = dtime_usec(0);
my_max_idx<<<(vsize+nTPB-1)/nTPB, nTPB>>>(vals, keys, vsize, results);
cudaDeviceSynchronize();
et = dtime_usec(et);
std::cout << "CUDA time: " << et/(float)USECPSEC << "s" << std::endl;
// verification
my_atomics *h_results = new my_atomics[ksize];
cudaMemcpy(h_results, results, ksize*sizeof(my_atomics), cudaMemcpyDeviceToHost);
for (int i = 0; i < ksize; i++){
if (h_results[i].floats[0] != d_vals_out[i]) {std::cout << "value mismatch at index: " << i << " thrust: " << d_vals_out[i] << " CUDA: " << h_results[i].floats[0] << std::endl; return -1;}
if (h_results[i].ints[1] != d_idxs_out[i]) {std::cout << "index mismatch at index: " << i << " thrust: " << d_idxs_out[i] << " CUDA: " << h_results[i].ints[1] << std::endl; return -1;}
}
std::cout << "Success!" << std::endl;
return 0;
}
$ nvcc -arch=sm_35 -o t1234 t1234.cu
$ ./t1234
Thrust time: 0.026593s
CUDA time: 0.002451s
Success!
$