[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/benchmarks/bench/shuffle/basic.cu

@@ -0,0 +1,72 @@
+// SPDX-FileCopyrightText: Copyright (c) 2011-2023, NVIDIA CORPORATION. All rights reserved.
+// SPDX-License-Identifier: BSD-3
+
+#include <thrust/device_vector.h>
+#include <thrust/execution_policy.h>
+#include <thrust/random.h>
+#include <thrust/shuffle.h>
+
+#include "nvbench_helper.cuh"
+
+template <typename T>
+static void basic(nvbench::state& state, nvbench::type_list<T>)
+{
+  const auto elements = static_cast<std::size_t>(state.get_int64("Elements"));
+
+  thrust::device_vector<T> data(elements);
+
+  state.add_element_count(elements);
+  state.add_global_memory_reads<T>(elements);
+  state.add_global_memory_writes<T>(elements);
+
+  auto do_engine = [&](auto&& engine_constructor) {
+    caching_allocator_t alloc;
+    state.exec(nvbench::exec_tag::gpu | nvbench::exec_tag::no_batch | nvbench::exec_tag::sync,
+               [&](nvbench::launch& launch) {
+                 thrust::shuffle(policy(alloc, launch), data.begin(), data.end(), engine_constructor());
+               });
+  };
+
+  const auto rng_engine = state.get_string("Engine");
+  if (rng_engine == "minstd")
+  {
+    do_engine([] {
+      return thrust::random::minstd_rand{};
+    });
+  }
+  else if (rng_engine == "ranlux24")
+  {
+    do_engine([] {
+      return thrust::random::ranlux24{};
+    });
+  }
+  else if (rng_engine == "ranlux48")
+  {
+    do_engine([] {
+      return thrust::random::ranlux48{};
+    });
+  }
+  else if (rng_engine == "taus88")
+  {
+    do_engine([] {
+      return thrust::random::taus88{};
+    });
+  }
+}
+
+using types =
+  nvbench::type_list<int8_t,
+                     int16_t,
+                     int32_t,
+                     int64_t
+#if _CCCL_HAS_INT128()
+                     ,
+                     int128_t
+#endif
+                     >;
+
+NVBENCH_BENCH_TYPES(basic, NVBENCH_TYPE_AXES(types))
+  .set_name("base")
+  .set_type_axes_names({"T{ct}"})
+  .add_int64_power_of_two_axis("Elements", nvbench::range(16, 28, 4))
+  .add_string_axis("Engine", {"minstd", "ranlux24", "ranlux48", "taus88"});