[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/test/coop/shuffle/threads_within_warp.cu

@@ -0,0 +1,141 @@
+//===----------------------------------------------------------------------===//
+//
+// Part of CUDA Experimental in CUDA C++ Core Libraries,
+// under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+// SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES.
+//
+//===----------------------------------------------------------------------===//
+
+#include <cuda/devices>
+#include <cuda/hierarchy>
+#include <cuda/launch>
+#include <cuda/stream>
+#include <cuda/type_traits>
+
+#include <cuda/experimental/coop.cuh>
+#include <cuda/experimental/group.cuh>
+
+#include "testing.cuh"
+
+template <class T>
+__device__ T make_instance_for(unsigned rank)
+{
+  if constexpr (cuda::is_vector_type_v<T>)
+  {
+    using U = cuda::scalar_type_t<T>;
+    return T{static_cast<U>(rank), static_cast<U>(rank), static_cast<U>(rank)};
+  }
+  else
+  {
+    return static_cast<T>(rank);
+  }
+}
+
+template <class T, class Group>
+__device__ void test_group(const Group& group)
+{
+  // Exit all threads that are not part of the group.
+  if (!cuda::gpu_thread.is_part_of(group))
+  {
+    return;
+  }
+
+  const auto my_rank  = cuda::gpu_thread.rank_as<unsigned>(group);
+  const auto my_value = make_instance_for<T>(my_rank);
+
+  // Test identity.
+  REQUIRE(cudax::coop::shuffle(group, my_value, my_rank) == my_value);
+
+  // Test broadcast from root rank.
+  REQUIRE(cudax::coop::shuffle(group, my_value, 0) == make_instance_for<T>(0));
+
+  // Test broadcast from last rank.
+  {
+    const auto other_rank = cuda::gpu_thread.count_as<unsigned>(group) - 1;
+    REQUIRE(cudax::coop::shuffle(group, my_value, other_rank) == make_instance_for<T>(other_rank));
+  }
+
+  // Test my_rank + 1.
+  {
+    const auto other_rank = (my_rank + 1) % cuda::gpu_thread.count_as<unsigned>(group);
+    REQUIRE(cudax::coop::shuffle(group, my_value, other_rank) == make_instance_for<T>(other_rank));
+  }
+
+  // Test my_rank + 6.
+  {
+    const auto other_rank = (my_rank + 6) % cuda::gpu_thread.count_as<unsigned>(group);
+    REQUIRE(cudax::coop::shuffle(group, my_value, other_rank) == make_instance_for<T>(other_rank));
+  }
+
+  // Test my_rank +- 1 based on whether my_rank is even or not.
+  {
+    const auto other_rank =
+      ((my_rank % 2 == 0) ? my_rank + 1 : my_rank - 1) % cuda::gpu_thread.count_as<unsigned>(group);
+    REQUIRE(cudax::coop::shuffle(group, my_value, other_rank) == make_instance_for<T>(other_rank));
+  }
+
+  // Test my_rank +- 3 based on whether my_rank is even or not.
+  {
+    const auto other_rank =
+      ((my_rank % 2 == 0) ? my_rank + 3 : my_rank - 3) % cuda::gpu_thread.count_as<unsigned>(group);
+    REQUIRE(cudax::coop::shuffle(group, my_value, other_rank) == make_instance_for<T>(other_rank));
+  }
+}
+
+struct CustomBinaryPartition
+{
+  template <class MappingResult>
+  __device__ bool operator()(MappingResult mapping_result)
+  {
+    switch (mapping_result.unit_rank())
+    {
+      case 1:
+      case 5:
+      case 14:
+      case 15:
+      case 31:
+        return true;
+      default:
+        return false;
+    }
+  }
+};
+
+template <class T, class Config>
+__device__ void test_type(const Config& config)
+{
+  const cudax::this_warp warp{config};
+  test_group<T>(cudax::group{cuda::gpu_thread, warp, cudax::identity_mapping{}, cudax::lane_synchronizer{}});
+  test_group<T>(cudax::group{cuda::gpu_thread, warp, cudax::group_by<4>{}, cudax::lane_synchronizer{}});
+  test_group<T>(cudax::group{cuda::gpu_thread, warp, cudax::group_by{1}, cudax::lane_synchronizer{}});
+  test_group<T>(
+    cudax::group{cuda::gpu_thread, warp, cudax::group_by{3, cudax::non_exhaustive}, cudax::lane_synchronizer{}});
+  test_group<T>(
+    cudax::group{cuda::gpu_thread, warp, cudax::binary_partition{CustomBinaryPartition{}}, cudax::lane_synchronizer{}});
+}
+
+struct TestKernel
+{
+  template <class Config>
+  __device__ void operator()(const Config& config)
+  {
+    test_type<signed char>(config);
+    test_type<unsigned>(config);
+    test_type<unsigned long long>(config);
+    test_type<longlong3>(config);
+  }
+};
+
+C2H_TEST("shuffle/threads_within_warp", "[shuffle][threads_within_warp]")
+{
+  const auto device = cuda::devices[0];
+
+  const cuda::stream stream{device};
+
+  const auto config = cuda::make_config(cuda::grid_dims<1>(), cuda::block_dims<32>());
+  cuda::launch(stream, config, TestKernel{});
+
+  stream.sync();
+}