[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

cccl_upstream/thrust/testing/cuda/count.cu

@@ -0,0 +1,103 @@
+#include <thrust/count.h>
+#include <thrust/execution_policy.h>
+
+#include <unittest/unittest.h>
+
+#ifdef THRUST_TEST_DEVICE_SIDE
+template <typename ExecutionPolicy, typename Iterator, typename T, typename Iterator2>
+__global__ void count_kernel(ExecutionPolicy exec, Iterator first, Iterator last, T value, Iterator2 result)
+{
+  *result = thrust::count(exec, first, last, value);
+}
+
+template <typename T, typename ExecutionPolicy>
+void TestCountDevice(ExecutionPolicy exec, const size_t n)
+{
+  thrust::host_vector<T> h_data   = unittest::random_samples<T>(n);
+  thrust::device_vector<T> d_data = h_data;
+
+  thrust::device_vector<size_t> d_result(1);
+
+  size_t h_result = thrust::count(h_data.begin(), h_data.end(), T(5));
+
+  count_kernel<<<1, 1>>>(exec, d_data.begin(), d_data.end(), T(5), d_result.begin());
+  cudaError_t const err = cudaDeviceSynchronize();
+  ASSERT_EQUAL(cudaSuccess, err);
+
+  ASSERT_EQUAL(h_result, d_result[0]);
+}
+
+template <typename T>
+void TestCountDeviceSeq(const size_t n)
+{
+  TestCountDevice<T>(thrust::seq, n);
+}
+DECLARE_VARIABLE_UNITTEST(TestCountDeviceSeq);
+
+template <typename T>
+void TestCountDeviceDevice(const size_t n)
+{
+  TestCountDevice<T>(thrust::device, n);
+}
+DECLARE_VARIABLE_UNITTEST(TestCountDeviceDevice);
+
+template <typename ExecutionPolicy, typename Iterator, typename Predicate, typename Iterator2>
+__global__ void count_if_kernel(ExecutionPolicy exec, Iterator first, Iterator last, Predicate pred, Iterator2 result)
+{
+  *result = thrust::count_if(exec, first, last, pred);
+}
+
+template <typename T>
+struct greater_than_five
+{
+  _CCCL_HOST_DEVICE bool operator()(const T& x) const
+  {
+    return x > 5;
+  }
+};
+
+template <typename T, typename ExecutionPolicy>
+void TestCountIfDevice(ExecutionPolicy exec, const size_t n)
+{
+  thrust::host_vector<T> h_data   = unittest::random_samples<T>(n);
+  thrust::device_vector<T> d_data = h_data;
+
+  thrust::device_vector<size_t> d_result(1);
+
+  size_t h_result = thrust::count_if(h_data.begin(), h_data.end(), greater_than_five<T>());
+  count_if_kernel<<<1, 1>>>(exec, d_data.begin(), d_data.end(), greater_than_five<T>(), d_result.begin());
+  cudaError_t const err = cudaDeviceSynchronize();
+  ASSERT_EQUAL(cudaSuccess, err);
+
+  ASSERT_EQUAL(h_result, d_result[0]);
+}
+
+template <typename T>
+void TestCountIfDeviceSeq(const size_t n)
+{
+  TestCountIfDevice<T>(thrust::seq, n);
+}
+DECLARE_VARIABLE_UNITTEST(TestCountIfDeviceSeq);
+
+template <typename T>
+void TestCountIfDeviceDevice(const size_t n)
+{
+  TestCountIfDevice<T>(thrust::device, n);
+}
+DECLARE_VARIABLE_UNITTEST(TestCountIfDeviceDevice);
+#endif
+
+void TestCountCudaStreams()
+{
+  thrust::device_vector<int> data{1, 1, 0, 0, 1};
+
+  cudaStream_t s;
+  cudaStreamCreate(&s);
+
+  ASSERT_EQUAL(thrust::count(thrust::cuda::par.on(s), data.begin(), data.end(), 0), 2);
+  ASSERT_EQUAL(thrust::count(thrust::cuda::par.on(s), data.begin(), data.end(), 1), 3);
+  ASSERT_EQUAL(thrust::count(thrust::cuda::par.on(s), data.begin(), data.end(), 2), 0);
+
+  cudaStreamDestroy(s);
+}
+DECLARE_UNITTEST(TestCountCudaStreams);