[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/cudart.cu

@@ -0,0 +1,16 @@
+#include <cuda/std/memory>
+
+#include <cuda_runtime_api.h>
+
+#include <unittest/unittest.h>
+
+template <typename T>
+void TestCudaMallocResultAligned(const std::size_t n)
+{
+  T* ptr = nullptr;
+  cudaMalloc(&ptr, n * sizeof(T));
+  cudaFree(ptr);
+
+  ASSERT_EQUAL(true, ::cuda::std::is_sufficiently_aligned<alignof(T)>(ptr));
+}
+DECLARE_VARIABLE_UNITTEST(TestCudaMallocResultAligned);