[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/examples/01-axpy-places.cu

@@ -0,0 +1,119 @@
+//===----------------------------------------------------------------------===//
+//
+// 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 to use the task construct with grids of
+ *        places and composite data places
+ */
+
+#include <cuda/experimental/__places/partitions/tiled_partition.cuh>
+#include <cuda/experimental/__stf/graph/graph_ctx.cuh>
+#include <cuda/experimental/__stf/stream/stream_ctx.cuh>
+
+using namespace cuda::experimental::stf;
+
+template <typename T>
+__global__ void axpy(size_t start, size_t cnt, T a, const T* x, T* y)
+{
+  int tid      = blockIdx.x * blockDim.x + threadIdx.x;
+  int nthreads = gridDim.x * blockDim.x;
+
+  for (int ind = tid; ind < cnt; ind += nthreads)
+  {
+    y[ind + start] += a * x[ind + start];
+  }
+}
+
+double X0(size_t i)
+{
+  return sin((double) i);
+}
+
+double Y0(size_t i)
+{
+  return cos((double) i);
+}
+
+template <typename Ctx>
+void run()
+{
+  Ctx ctx;
+
+  const int N = 1024 * 1024 * 32;
+  double *X, *Y;
+
+  X = new double[N];
+  Y = new double[N];
+  SCOPE(exit)
+  {
+    delete[] X;
+    delete[] Y;
+  };
+
+  for (size_t ind = 0; ind < N; ind++)
+  {
+    X[ind] = X0(ind);
+    Y[ind] = Y0(ind);
+  }
+
+  //    std::shared_ptr<execution_grid> all_devs = exec_place::all_devices();
+  // use grid [ 0 0 0 0 ] for debugging purpose
+  auto all_devs = exec_place::repeat(exec_place::device(0), 4);
+
+  // 512k doubles = 4MB (2 pages)
+  // A 1D blocking strategy over all devices with a block size of 32 and a round robin distribution of blocks across
+  // devices
+  //    data_place cdp = data_place(exec_place::all_devices().as_grid().get_grid(),
+  //            [](dim4 grid_dim, pos4 index_pos) { return pos4((index_pos.x / (512 * 1024ULL)) % grid_dim.x); });
+
+  data_place cdp = data_place::composite(tiled_partition<512 * 1024ULL>(), all_devs);
+
+  auto handle_X = ctx.logical_data(X, {N});
+  auto handle_Y = ctx.logical_data(Y, {N});
+
+  double alpha = 3.14;
+
+  /* Compute Y = Y + alpha X */
+  auto t = ctx.task(all_devs, handle_X.read(cdp), handle_Y.rw(cdp));
+  t->*[&](auto, auto sX, auto sY) {
+    size_t grid_size = t.grid_dims().size();
+
+    assert(N % grid_size == 0);
+
+    for (size_t i = 0; i < grid_size; i++)
+    {
+      auto active = t.activate_place(i);
+      axpy<<<16, 128, 0, t.get_stream(i)>>>(i * N / grid_size, N / grid_size, alpha, sX.data_handle(), sY.data_handle());
+    }
+  };
+
+  /* Check the result on the host */
+  ctx.host_launch(handle_X.read(), handle_Y.read())->*[&](auto sX, auto sY) {
+    for (size_t ind = 0; ind < N; ind++)
+    {
+      // Y should be Y0 + alpha X0
+      EXPECT(fabs(sY(ind) - (Y0(ind) + alpha * X0(ind))) < 0.0001);
+
+      // X should be X0
+      EXPECT(fabs(sX(ind) - X0(ind)) < 0.0001);
+    }
+  };
+
+  ctx.finalize();
+}
+
+int main()
+{
+  run<stream_ctx>();
+  // Disabled until composite data places are implemented with graphs
+  // run<graph_ctx>();
+}