56fd68e7dd
CCCL (CUDA C++ Core Libraries) provides: - CUB: device/block/warp-level GPU primitives (reduce, scan, sort, topk) - Thrust: high-level parallel algorithms (transform_reduce, sort, scan) - libcudacxx: CUDA C++ standard library (atomics, barriers, memory) - cudax: experimental features (memory resources, allocators) - Tuning policies: per-SM hardware-specific algorithm parameters Competition optimization vectors mapped to CCCL: - Output TPS (83% weight): warp_reduce, block_reduce, device_topk - Input TPS (14% weight): device_scan, block_load, prefetch - Cache TPS (3% weight): prefix caching strategy patterns - Memory (0.9 util): pooled/cached/buddy allocators Source: https://github.com/NVIDIA/cccl (shallow clone, HEAD only) License: Apache-2.0
77 lines
2.1 KiB
Plaintext
77 lines
2.1 KiB
Plaintext
#include <thrust/copy.h>
|
|
#include <thrust/count.h>
|
|
#include <thrust/device_vector.h>
|
|
#include <thrust/remove.h>
|
|
#include <thrust/sequence.h>
|
|
|
|
#include <cuda/std/iterator>
|
|
|
|
#include <iostream>
|
|
#include <string>
|
|
|
|
// this functor returns true if the argument is odd, and false otherwise
|
|
template <typename T>
|
|
struct is_odd
|
|
{
|
|
__host__ __device__ bool operator()(T x)
|
|
{
|
|
return x % 2;
|
|
}
|
|
};
|
|
|
|
template <typename Iterator>
|
|
void print_range(const std::string& name, Iterator first, Iterator last)
|
|
{
|
|
using T = cuda::std::iter_value_t<Iterator>;
|
|
|
|
std::cout << name << ": ";
|
|
thrust::copy(first, last, std::ostream_iterator<T>(std::cout, " "));
|
|
std::cout << "\n";
|
|
}
|
|
|
|
int main()
|
|
{
|
|
// input size
|
|
size_t N = 10;
|
|
|
|
// define some types
|
|
using Vector = thrust::device_vector<int>;
|
|
using Iterator = Vector::iterator;
|
|
|
|
// allocate storage for array
|
|
Vector values(N);
|
|
|
|
// initialize array to [0, 1, 2, ... ]
|
|
thrust::sequence(values.begin(), values.end());
|
|
|
|
print_range("values", values.begin(), values.end());
|
|
|
|
// allocate output storage, here we conservatively assume all values will be copied
|
|
Vector output(values.size());
|
|
|
|
// copy odd numbers to separate array
|
|
Iterator output_end = thrust::copy_if(values.begin(), values.end(), output.begin(), is_odd<int>());
|
|
|
|
print_range("output", output.begin(), output_end);
|
|
|
|
// another approach is to count the number of values that will
|
|
// be copied, and allocate an array of the right size
|
|
size_t N_odd = thrust::count_if(values.begin(), values.end(), is_odd<int>());
|
|
|
|
Vector small_output(N_odd);
|
|
|
|
thrust::copy_if(values.begin(), values.end(), small_output.begin(), is_odd<int>());
|
|
|
|
print_range("small_output", small_output.begin(), small_output.end());
|
|
|
|
// we can also compact sequences with the remove functions, which do the opposite of copy
|
|
Iterator values_end = thrust::remove_if(values.begin(), values.end(), is_odd<int>());
|
|
|
|
// since the values after values_end are garbage, we'll resize the vector
|
|
values.resize(values_end - values.begin());
|
|
|
|
print_range("values", values.begin(), values.end());
|
|
|
|
return 0;
|
|
}
|