[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
This commit is contained in:
EngineX CI
2026-07-30 09:35:51 +00:00
parent b4d01f481e
commit 56fd68e7dd
8871 changed files with 1454674 additions and 0 deletions

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file(
GLOB test_srcs
RELATIVE "${CMAKE_CURRENT_LIST_DIR}"
CONFIGURE_DEPENDS
*.cu
*.cpp
)
foreach (thrust_target IN LISTS THRUST_TARGETS)
thrust_get_target_property(config_device ${thrust_target} DEVICE)
if (NOT config_device STREQUAL "OMP")
continue()
endif()
foreach (test_src IN LISTS test_srcs)
get_filename_component(test_name "${test_src}" NAME_WLE)
string(PREPEND test_name "omp.")
thrust_add_test(test_target ${test_name} "${test_src}" ${thrust_target})
endforeach()
endforeach()

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#include <thrust/device_ptr.h>
#include <thrust/reduce.h>
#include <thrust/scan.h>
#include <thrust/sort.h>
#include <thrust/system_error.h>
#include <thrust/transform.h>
#include <unittest/unittest.h>
void TestNvccIndependenceTransform()
{
using T = int;
const int n = 10;
thrust::host_vector<T> h_input = unittest::random_integers<T>(n);
thrust::device_vector<T> d_input = h_input;
thrust::host_vector<T> h_output(n);
thrust::device_vector<T> d_output(n);
thrust::transform(h_input.begin(), h_input.end(), h_output.begin(), ::cuda::std::negate<T>());
thrust::transform(d_input.begin(), d_input.end(), d_output.begin(), ::cuda::std::negate<T>());
ASSERT_EQUAL(h_output, d_output);
}
DECLARE_UNITTEST(TestNvccIndependenceTransform);
void TestNvccIndependenceReduce()
{
using T = int;
const int n = 10;
thrust::host_vector<T> h_data = unittest::random_integers<T>(n);
thrust::device_vector<T> d_data = h_data;
T init = 13;
T h_result = thrust::reduce(h_data.begin(), h_data.end(), init);
T d_result = thrust::reduce(d_data.begin(), d_data.end(), init);
ASSERT_ALMOST_EQUAL(h_result, d_result);
}
DECLARE_UNITTEST(TestNvccIndependenceReduce);
void TestNvccIndependenceExclusiveScan()
{
using T = int;
const int n = 10;
thrust::host_vector<T> h_input = unittest::random_integers<T>(n);
thrust::device_vector<T> d_input = h_input;
thrust::host_vector<T> h_output(n);
thrust::device_vector<T> d_output(n);
thrust::inclusive_scan(h_input.begin(), h_input.end(), h_output.begin());
thrust::inclusive_scan(d_input.begin(), d_input.end(), d_output.begin());
ASSERT_EQUAL(d_output, h_output);
}
DECLARE_UNITTEST(TestNvccIndependenceExclusiveScan);
void TestNvccIndependenceSort()
{
using T = int;
const int n = 10;
thrust::host_vector<T> h_data = unittest::random_integers<T>(n);
thrust::device_vector<T> d_data = h_data;
thrust::sort(h_data.begin(), h_data.end(), ::cuda::std::less<T>());
thrust::sort(d_data.begin(), d_data.end(), ::cuda::std::less<T>());
ASSERT_EQUAL(h_data, d_data);
}
DECLARE_UNITTEST(TestNvccIndependenceSort);

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#include <thrust/functional.h>
#include <thrust/system/detail/internal/decompose.h>
#include <thrust/system/omp/detail/reduce_intervals.h>
#include <unittest/unittest.h>
// CPP reference implementation
template <typename InputIterator, typename OutputIterator, typename BinaryFunction, typename Decomposition>
void reduce_intervals(InputIterator input, OutputIterator output, BinaryFunction binary_op, Decomposition decomp)
{
using OutputType = thrust::detail::it_value_t<OutputIterator>;
using index_type = typename Decomposition::index_type;
// wrap binary_op
thrust::detail::wrapped_function<BinaryFunction, OutputType> wrapped_binary_op{binary_op};
for (index_type i = 0; i < decomp.size(); ++i, ++output)
{
InputIterator begin = input + decomp[i].begin();
InputIterator end = input + decomp[i].end();
if (begin != end)
{
OutputType sum = *begin;
++begin;
while (begin != end)
{
sum = wrapped_binary_op(sum, *begin);
++begin;
}
*output = sum;
}
}
}
void TestOmpReduceIntervalsSimple()
{
using T = int;
using Vector = thrust::device_vector<T>;
using thrust::system::detail::internal::uniform_decomposition;
using thrust::system::omp::detail::reduce_intervals;
Vector input(10, 1);
thrust::omp::tag omp_tag;
{
uniform_decomposition<int> decomp(10, 10, 1);
Vector output(decomp.size());
reduce_intervals(omp_tag, input.begin(), output.begin(), ::cuda::std::plus<T>(), decomp);
ASSERT_EQUAL(output[0], 10);
}
{
uniform_decomposition<int> decomp(10, 6, 2);
Vector output(decomp.size());
reduce_intervals(omp_tag, input.begin(), output.begin(), ::cuda::std::plus<T>(), decomp);
ASSERT_EQUAL(output[0], 6);
ASSERT_EQUAL(output[1], 4);
}
}
DECLARE_UNITTEST(TestOmpReduceIntervalsSimple);
template <typename T>
struct TestOmpReduceIntervals
{
void operator()(const size_t n)
{
using thrust::system::detail::internal::uniform_decomposition;
using thrust::system::omp::detail::reduce_intervals;
thrust::host_vector<T> h_input = unittest::random_integers<T>(n);
thrust::device_vector<T> d_input = h_input;
uniform_decomposition<size_t> decomp(n, 7, 100);
thrust::host_vector<T> h_output(decomp.size());
thrust::device_vector<T> d_output(decomp.size());
::reduce_intervals(h_input.begin(), h_output.begin(), ::cuda::std::plus<T>(), decomp);
thrust::system::omp::tag omp_tag;
reduce_intervals(omp_tag, d_input.begin(), d_output.begin(), ::cuda::std::plus<T>(), decomp);
ASSERT_EQUAL(h_output, d_output);
}
};
VariableUnitTest<TestOmpReduceIntervals, IntegralTypes> TestOmpReduceIntervalsInstance;