[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/freeze/task_fence.cu

@@ -0,0 +1,98 @@
+//===----------------------------------------------------------------------===//
+//
+// 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 Freeze data in read-only fashion
+ *
+ */
+
+#include <cuda/experimental/__stf/graph/graph_ctx.cuh>
+#include <cuda/experimental/__stf/stream/stream_ctx.cuh>
+
+using namespace cuda::experimental::stf;
+
+int X0(int i)
+{
+  return 17 * i + 45;
+}
+
+__global__ void print(slice<int> s)
+{
+  int tid      = blockIdx.x * blockDim.x + threadIdx.x;
+  int nthreads = gridDim.x * blockDim.x;
+
+  for (int i = tid; i < s.size(); i += nthreads)
+  {
+    printf("%d %d\n", i, s(i));
+  }
+}
+
+int main()
+{
+  stream_ctx ctx;
+
+  const int N = 16;
+  int X[N];
+
+  for (int i = 0; i < N; i++)
+  {
+    X[i] = X0(i);
+  }
+
+  auto lX = ctx.logical_data(X).set_symbol("X");
+  auto lY = ctx.logical_data(lX.shape()).set_symbol("Y");
+
+  ctx.parallel_for(lX.shape(), lX.rw()).set_symbol("X=2X")->*[] __device__(size_t i, auto x) {
+    x(i) *= 2;
+  };
+
+  // test 1 : implicit sync of gets
+  {
+    auto fx      = ctx.freeze(lX);
+    auto [dX, _] = fx.get(data_place::current_device());
+
+    // the stream returned by fence should depend on the get operation
+    auto stream2 = ctx.fence();
+    print<<<8, 4, 0, stream2>>>(dX);
+
+    ctx.parallel_for(lX.shape(), lX.read(), lY.write()).set_symbol("Y=X")->*[] __device__(size_t i, auto x, auto y) {
+      y(i) = x(i);
+    };
+
+    fx.unfreeze(stream2);
+  }
+
+  // test 2 : unfreeze with no events due to user sync
+  {
+    auto fx      = ctx.freeze(lX);
+    auto [dX, _] = fx.get(data_place::current_device());
+
+    // the stream returned by fence should depend on the get operation
+    auto stream2 = ctx.fence();
+    print<<<8, 4, 0, stream2>>>(dX);
+
+    // We synchronize so there is nothing to depend on anymore
+    cudaStreamSynchronize(stream2);
+    fx.unfreeze(event_list());
+  }
+
+  ctx.parallel_for(lX.shape(), lX.rw()).set_symbol("X+=1")->*[] __device__(size_t i, auto x) {
+    x(i) += 1;
+  };
+
+  ctx.finalize();
+
+  for (int i = 0; i < N; i++)
+  {
+    EXPECT(X[i] == 2 * X0(i) + 1);
+  }
+}