[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/cudax/examples/vector_add.cu

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+/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *  * Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ *  * Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *  * Neither the name of NVIDIA CORPORATION nor the names of its
+ *    contributors may be used to endorse or promote products derived
+ *    from this software without specific prior written permission.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
+ * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+ * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+ * PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE COPYRIGHT OWNER OR
+ * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
+ * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+ * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+ * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
+ * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+ * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ */
+
+/**
+ * Vector addition: C = A + B.
+ *
+ * This sample is a very basic sample that implements element by element
+ * vector addition. It is the same as the sample illustrating Chapter 2
+ * of the programming guide with some additions like error checking.
+ */
+
+#include <cstdio>
+
+// For the CUDA runtime routines (prefixed with "cuda_")
+#include <cuda/std/span>
+
+#include <cuda/experimental/launch.cuh>
+#include <cuda/experimental/stream.cuh>
+
+#include <cuda_runtime.h>
+
+#include "vector.cuh"
+
+namespace cudax = cuda::experimental;
+using cudax::in;
+using cudax::out;
+
+/**
+ * CUDA Kernel Device code
+ *
+ * Computes the vector addition of A and B into C. The 3 vectors have the same
+ * number of elements numElements.
+ */
+__global__ void vectorAdd(cudax::span<const float> A, cudax::span<const float> B, cudax::span<float> C)
+{
+  int i = static_cast<int>(blockDim.x * blockIdx.x + threadIdx.x);
+
+  if (i < A.size())
+  {
+    C[i] = A[i] + B[i] + 0.0f;
+  }
+}
+
+/**
+ * Host main routine
+ */
+int main()
+try
+{
+  // A CUDA stream on which to execute the vector addition kernel
+  cudax::stream stream(cuda::devices[0]);
+
+  // Print the vector length to be used, and compute its size
+  int numElements = 50000;
+  printf("[Vector addition of %d elements]\n", numElements);
+
+  // Allocate the host vectors
+  cudax::vector<float> A(numElements); // input
+  cudax::vector<float> B(numElements); // input
+  cudax::vector<float> C(numElements); // output
+
+  // Initialize the host input vectors
+  for (int i = 0; i < numElements; ++i)
+  {
+    A[i] = static_cast<float>(rand()) / (float) RAND_MAX;
+    B[i] = static_cast<float>(rand()) / (float) RAND_MAX;
+  }
+
+  // Define the kernel launch parameters
+  constexpr int threadsPerBlock = 256;
+  auto config                   = cuda::distribute<threadsPerBlock>(numElements);
+
+  // Launch the vectorAdd kernel
+  printf("CUDA kernel launch with %zu blocks of %d threads\n", cuda::block.count(cuda::grid, config), threadsPerBlock);
+  cudax::launch(stream, config, vectorAdd, in(A), in(B), out(C));
+
+  printf("waiting for the stream to finish\n");
+  stream.sync();
+
+  printf("verifying the results\n");
+  // Verify that the result vector is correct
+  for (int i = 0; i < numElements; ++i)
+  {
+    if (fabs(A[i] + B[i] - C[i]) > 1e-5)
+    {
+      fprintf(stderr, "Result verification failed at element %d!\n", i);
+      exit(EXIT_FAILURE);
+    }
+  }
+
+  printf("Test PASSED\n");
+
+  printf("Done\n");
+  return 0;
+}
+catch (const std::exception& e)
+{
+  printf("caught an exception: \"%s\"\n", e.what());
+}
+catch (...)
+{
+  printf("caught an unknown exception\n");
+}