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project_6/cccl_upstream/thrust/testing/inner_product.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_free.h>
#include <thrust/device_malloc.h>
#include <thrust/device_vector.h>
#include <thrust/functional.h>
#include <thrust/inner_product.h>
#include <thrust/iterator/retag.h>
#include <unittest/unittest.h>
template <class Vector>
void TestInnerProductSimple()
{
using T = typename Vector::value_type;
Vector v1{1, -2, 3};
Vector v2{-4, 5, 6};
T init = 3;
T result = thrust::inner_product(v1.begin(), v1.end(), v2.begin(), init);
ASSERT_EQUAL(result, 7);
}
DECLARE_VECTOR_UNITTEST(TestInnerProductSimple);
template <typename InputIterator1, typename InputIterator2, typename OutputType>
int inner_product(my_system& system, InputIterator1, InputIterator1, InputIterator2, OutputType)
{
system.validate_dispatch();
return 13;
}
void TestInnerProductDispatchExplicit()
{
thrust::device_vector<int> vec;
my_system sys(0);
thrust::inner_product(sys, vec.begin(), vec.end(), vec.begin(), 0);
ASSERT_EQUAL(true, sys.is_valid());
}
DECLARE_UNITTEST(TestInnerProductDispatchExplicit);
template <typename InputIterator1, typename InputIterator2, typename OutputType>
int inner_product(my_tag, InputIterator1, InputIterator1, InputIterator2, OutputType)
{
return 13;
}
void TestInnerProductDispatchImplicit()
{
thrust::device_vector<int> vec;
int result = thrust::inner_product(
thrust::retag<my_tag>(vec.begin()), thrust::retag<my_tag>(vec.end()), thrust::retag<my_tag>(vec.begin()), 0);
ASSERT_EQUAL(13, result);
}
DECLARE_UNITTEST(TestInnerProductDispatchImplicit);
template <class Vector>
void TestInnerProductWithOperator()
{
using T = typename Vector::value_type;
Vector v1{1, -2, 3};
Vector v2{-1, 3, 6};
// compute (v1 - v2) and perform a multiplies reduction
T init = 3;
T result = thrust::inner_product(
v1.begin(), v1.end(), v2.begin(), init, ::cuda::std::multiplies<T>(), ::cuda::std::minus<T>());
ASSERT_EQUAL(result, 90);
}
DECLARE_VECTOR_UNITTEST(TestInnerProductWithOperator);
template <typename T>
struct TestInnerProduct
{
void operator()(const size_t n)
{
thrust::host_vector<T> h_v1 = unittest::random_integers<T>(n);
thrust::host_vector<T> h_v2 = unittest::random_integers<T>(n);
thrust::device_vector<T> d_v1 = h_v1;
thrust::device_vector<T> d_v2 = h_v2;
T init = 13;
T expected = thrust::inner_product(h_v1.begin(), h_v1.end(), h_v2.begin(), init);
T result = thrust::inner_product(d_v1.begin(), d_v1.end(), d_v2.begin(), init);
ASSERT_EQUAL(expected, result);
}
};
VariableUnitTest<TestInnerProduct, IntegralTypes> TestInnerProductInstance;
struct only_set_when_both_expected
{
long long expected;
bool* flag;
_CCCL_DEVICE long long operator()(long long x, long long y)
{
if (x == expected && y == expected)
{
*flag = true;
}
return x == y;
}
};
void TestInnerProductWithBigIndexesHelper(int magnitude)
{
thrust::counting_iterator<long long> begin(1);
thrust::counting_iterator<long long> end = begin + (1ll << magnitude);
ASSERT_EQUAL(::cuda::std::distance(begin, end), 1ll << magnitude);
thrust::device_ptr<bool> has_executed = thrust::device_malloc<bool>(1);
*has_executed = false;
only_set_when_both_expected fn = {(1ll << magnitude) - 1, thrust::raw_pointer_cast(has_executed)};
ASSERT_EQUAL(thrust::inner_product(thrust::device, begin, end, begin, 0ll, ::cuda::std::plus<long long>(), fn),
(1ll << magnitude));
bool has_executed_h = *has_executed;
thrust::device_free(has_executed);
ASSERT_EQUAL(has_executed_h, true);
}
void TestInnerProductWithBigIndexes()
{
TestInnerProductWithBigIndexesHelper(30);
#ifndef THRUST_FORCE_32_BIT_OFFSET_TYPE
TestInnerProductWithBigIndexesHelper(31);
TestInnerProductWithBigIndexesHelper(32);
TestInnerProductWithBigIndexesHelper(33);
#endif
}
DECLARE_UNITTEST(TestInnerProductWithBigIndexes);
void TestInnerProductPlaceholders()
{ // Regression test for NVIDIA/thrust#1178
using namespace thrust::placeholders;
thrust::device_vector<float> v1(100, 1.f);
thrust::device_vector<float> v2(100, 1.f);
auto result =
thrust::inner_product(v1.begin(), v1.end(), v2.begin(), 0.0f, ::cuda::std::plus<float>{}, _1 * _2 + 1.0f);
ASSERT_ALMOST_EQUAL(result, 200.f);
}
DECLARE_UNITTEST(TestInnerProductPlaceholders);