[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/stf/cpp/reuse_computation.cu

@@ -0,0 +1,80 @@
+//===----------------------------------------------------------------------===//
+//
+// Part of CUDASTF 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) 2022-2024 NVIDIA CORPORATION & AFFILIATES.
+//
+//===----------------------------------------------------------------------===//
+
+#include <cuda/experimental/__stf/utility/run_once.cuh>
+#include <cuda/experimental/stf.cuh>
+
+using namespace cuda::experimental::stf;
+
+int main()
+{
+  context ctx;
+
+  const int N  = 16;
+  size_t niter = 12;
+
+  int A[N];
+
+  for (int i = 0; i < N; i++)
+  {
+    A[i] = 2 * i + 1;
+  }
+
+  auto lres = ctx.logical_data(A);
+
+  for (size_t k = 0; k < niter; k++)
+  {
+    auto ltmp = ctx.logical_data(lres.shape());
+    ctx.parallel_for(ltmp.shape(), ltmp.write())->*[] __device__(size_t i, auto tmp) {
+      tmp(i) = i;
+    };
+
+    ctx.parallel_for(lres.shape(), ltmp.read(), lres.rw())->*[] __device__(size_t i, auto tmp, auto res) {
+      res(i) += tmp(i);
+    };
+  }
+
+  for (size_t k = 0; k < niter; k++)
+  {
+    auto ltmp = run_once()->*[&]() {
+      // Ensure this is only done once !
+      static bool done = false;
+      EXPECT(!done);
+      done = true;
+
+      auto ltmp = ctx.logical_data(lres.shape());
+      ctx.parallel_for(ltmp.shape(), ltmp.write())->*[] __device__(size_t i, auto tmp) {
+        tmp(i) = i;
+      };
+      return ltmp;
+    };
+
+    auto ltmp2 = run_once(size_t(k % 4))->*[&](size_t val) {
+      // fprintf(stderr, "COMPUTE FOR %ld\n", val);
+
+      auto ltmp = ctx.logical_data(lres.shape());
+      ctx.parallel_for(ltmp.shape(), ltmp.write())->*[val] __device__(size_t i, auto tmp) {
+        tmp(i) = val;
+      };
+      return ltmp;
+    };
+
+    ctx.parallel_for(lres.shape(), ltmp.read(), lres.rw())->*[] __device__(size_t i, auto tmp, auto res) {
+      res(i) += tmp(i);
+    };
+  }
+
+  ctx.finalize();
+
+  for (int i = 0; i < N; i++)
+  {
+    EXPECT(A[i] == (2 * i + 1) + 2 * i * niter);
+  }
+}