[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/stf/03-temporary-data.cu

@@ -0,0 +1,69 @@
+//===----------------------------------------------------------------------===//
+//
+// 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.
+//
+//===----------------------------------------------------------------------===//
+
+/**
+ * @file
+ *
+ * @brief This example illustrates how we can create temporary data from shapes, and use them in tasks
+ */
+
+#include <cuda/experimental/stf.cuh>
+
+using namespace cuda::experimental::stf;
+
+int main()
+{
+  const int n = 4096;
+  int X[n];
+  int Y[n];
+
+  for (size_t i = 0; i < n; i++)
+  {
+    X[i] = 3 * i;
+    Y[i] = 2 * i - 3;
+  }
+
+  context ctx;
+
+  auto lX = ctx.logical_data(X);
+  auto lY = ctx.logical_data(Y);
+
+  // Select an odd number
+  int niter = 19;
+  assert(niter % 2 == 1);
+
+  for (int iter = 0; iter < niter; iter++)
+  {
+    // We here define a temporary vector with the same shape as X, for which there is no existing copy
+    // This data handle has a limited scope, so that it is automatically destroyed at each iteration of the loop
+    auto tmp = ctx.logical_data(lX.shape());
+
+    ctx.task(lY.rw(), lX.rw(), tmp.write())->*[](cudaStream_t s, auto sY, auto sX, auto sTMP) {
+      // We swap X and Y using TMP as temporary buffer
+      // TMP = X
+      cuda_safe_call(
+        cudaMemcpyAsync(sTMP.data_handle(), sX.data_handle(), n * sizeof(int), cudaMemcpyDeviceToDevice, s));
+      // X = Y
+      cuda_safe_call(cudaMemcpyAsync(sX.data_handle(), sY.data_handle(), n * sizeof(int), cudaMemcpyDeviceToDevice, s));
+      // Y = TMP
+      cuda_safe_call(
+        cudaMemcpyAsync(sY.data_handle(), sTMP.data_handle(), n * sizeof(int), cudaMemcpyDeviceToDevice, s));
+    };
+  }
+
+  ctx.finalize();
+
+  // We have exchanged an odd number of times, so they must be inverted
+  for (size_t i = 0; i < n; i++)
+  {
+    assert(X[i] == 2 * i - 3);
+    assert(Y[i] == 3 * i);
+  }
+}