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project_6/cccl_upstream/thrust/examples/norm.cu
EngineX CI 56fd68e7dd [INFRA] Import NVIDIA/CCCL upstream as optimization reference library
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
2026-07-30 09:35:51 +00:00

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#include <thrust/device_vector.h>
#include <thrust/functional.h>
#include <thrust/host_vector.h>
#include <thrust/transform_reduce.h>
#include <cmath>
#include <iostream>
// This example computes the norm [1] of a vector. The norm is
// computed by squaring all numbers in the vector, summing the
// squares, and taking the square root of the sum of squares. In
// Thrust this operation is efficiently implemented with the
// transform_reduce() algorithm. Specifically, we first transform
// x -> x^2 and the compute a standard plus reduction. Since there
// is no built-in functor for squaring numbers, we define our own
// square functor.
//
// [1] http://en.wikipedia.org/wiki/Norm_(mathematics)#Euclidean_norm
// square<T> computes the square of a number f(x) -> x*x
template <typename T>
struct square
{
__host__ __device__ T operator()(const T& x) const
{
return x * x;
}
};
int main()
{
// initialize device vector directly
thrust::device_vector<float> d_x = {1.0, 2.0, 3.0, 4.0};
// setup arguments
square<float> unary_op;
cuda::std::plus<float> binary_op;
float init = 0;
// compute norm
float norm = std::sqrt(thrust::transform_reduce(d_x.begin(), d_x.end(), unary_op, init, binary_op));
std::cout << "norm is " << norm << '\n';
return 0;
}