[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/reverse.cu

@@ -0,0 +1,119 @@
+#include <thrust/execution_policy.h>
+#include <thrust/reverse.h>
+
+#include <unittest/unittest.h>
+
+#ifdef THRUST_TEST_DEVICE_SIDE
+template <typename ExecutionPolicy, typename Iterator>
+__global__ void reverse_kernel(ExecutionPolicy exec, Iterator first, Iterator last)
+{
+  thrust::reverse(exec, first, last);
+}
+
+template <typename ExecutionPolicy>
+void TestReverseDevice(ExecutionPolicy exec)
+{
+  size_t n                          = 1000;
+  thrust::host_vector<int> h_data   = unittest::random_integers<int>(n);
+  thrust::device_vector<int> d_data = h_data;
+
+  thrust::reverse(h_data.begin(), h_data.end());
+
+  reverse_kernel<<<1, 1>>>(exec, raw_pointer_cast(d_data.data()), raw_pointer_cast(d_data.data() + d_data.size()));
+  cudaError_t const err = cudaDeviceSynchronize();
+  ASSERT_EQUAL(cudaSuccess, err);
+
+  ASSERT_EQUAL(h_data, d_data);
+};
+
+void TestReverseDeviceSeq()
+{
+  TestReverseDevice(thrust::seq);
+}
+DECLARE_UNITTEST(TestReverseDeviceSeq);
+
+void TestReverseDeviceDevice()
+{
+  TestReverseDevice(thrust::device);
+}
+DECLARE_UNITTEST(TestReverseDeviceDevice);
+
+template <typename ExecutionPolicy, typename Iterator1, typename Iterator2>
+__global__ void reverse_copy_kernel(ExecutionPolicy exec, Iterator1 first, Iterator1 last, Iterator2 result)
+{
+  thrust::reverse_copy(exec, first, last, result);
+}
+
+template <typename ExecutionPolicy>
+void TestReverseCopyDevice(ExecutionPolicy exec)
+{
+  size_t n                          = 1000;
+  thrust::host_vector<int> h_data   = unittest::random_integers<int>(n);
+  thrust::device_vector<int> d_data = h_data;
+
+  thrust::host_vector<int> h_result(n);
+  thrust::device_vector<int> d_result(n);
+
+  thrust::reverse_copy(h_data.begin(), h_data.end(), h_result.begin());
+
+  reverse_copy_kernel<<<1, 1>>>(exec, d_data.begin(), d_data.end(), d_result.begin());
+  cudaError_t const err = cudaDeviceSynchronize();
+  ASSERT_EQUAL(cudaSuccess, err);
+
+  ASSERT_EQUAL(h_result, d_result);
+};
+
+void TestReverseCopyDeviceSeq()
+{
+  TestReverseCopyDevice(thrust::seq);
+}
+DECLARE_UNITTEST(TestReverseCopyDeviceSeq);
+
+void TestReverseCopyDeviceDevice()
+{
+  TestReverseCopyDevice(thrust::device);
+}
+DECLARE_UNITTEST(TestReverseCopyDeviceDevice);
+#endif
+
+void TestReverseCudaStreams()
+{
+  using Vector = thrust::device_vector<int>;
+  Vector data{1, 2, 3, 4, 5};
+
+  cudaStream_t s;
+  cudaStreamCreate(&s);
+
+  thrust::reverse(thrust::cuda::par.on(s), data.begin(), data.end());
+
+  cudaStreamSynchronize(s);
+
+  Vector ref{5, 4, 3, 2, 1};
+
+  ASSERT_EQUAL(ref, data);
+
+  cudaStreamDestroy(s);
+}
+DECLARE_UNITTEST(TestReverseCudaStreams);
+
+void TestReverseCopyCudaStreams()
+{
+  using Vector = thrust::device_vector<int>;
+  Vector data{1, 2, 3, 4, 5};
+
+  Vector result(5);
+
+  cudaStream_t s;
+  cudaStreamCreate(&s);
+
+  thrust::reverse_copy(thrust::cuda::par.on(s), data.begin(), data.end(), result.begin());
+
+  cudaStreamSynchronize(s);
+
+  Vector ref{5, 4, 3, 2, 1};
+
+  ASSERT_EQUAL(ref, result);
+
+  cudaStreamDestroy(s);
+}
+DECLARE_UNITTEST(TestReverseCopyCudaStreams);