[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/graph/concurrency_test.cu

@@ -0,0 +1,86 @@
+//===----------------------------------------------------------------------===//
+//
+// 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/graph/graph_ctx.cuh>
+
+#include <iostream>
+
+/*
+ * The goal of this test is to ensure that using read access modes actually
+ * results in concurrent tasks
+ */
+
+using namespace cuda::experimental::stf;
+
+static __global__ void cuda_sleep_kernel(long long int clock_cnt)
+{
+  long long int start_clock  = clock64();
+  long long int clock_offset = 0;
+  while (clock_offset < clock_cnt)
+  {
+    clock_offset = clock64() - start_clock;
+  }
+}
+
+int main(int argc, char** argv)
+{
+  int NTASKS = 256;
+  int ms     = 40;
+
+  if (argc > 1)
+  {
+    NTASKS = atoi(argv[1]);
+  }
+
+  if (argc > 2)
+  {
+    ms = atoi(argv[2]);
+  }
+
+  // cudaDevAttrClockRate: Peak clock frequency in kilohertz;
+  int clock_rate;
+  cuda_safe_call(cudaDeviceGetAttribute(&clock_rate, cudaDevAttrClockRate, 0));
+  long long int clock_cnt = (long long int) (ms * clock_rate);
+
+  graph_ctx ctx;
+
+  int dummy[1];
+  auto handle = ctx.logical_data(dummy);
+
+  ctx.task(handle.rw())->*[](cudaGraph_t graph, auto /*unused*/) {
+    cudaGraphNode_t n;
+    cuda_safe_call(cudaGraphAddEmptyNode(&n, graph, nullptr, 0));
+  };
+
+  for (int iter = 0; iter < 10; iter++)
+  {
+    for (int k = 0; k < NTASKS; k++)
+    {
+      ctx.task(handle.read())->*[&](cudaStream_t stream, auto /*unused*/) {
+        cuda_sleep_kernel<<<1, 1, 0, stream>>>(clock_cnt);
+      };
+    }
+
+    ctx.task(handle.rw())->*[&](cudaGraph_t graph, auto /*unused*/) {
+      cudaGraphNode_t n;
+      cuda_safe_call(cudaGraphAddEmptyNode(&n, graph, nullptr, 0));
+    };
+  }
+
+  ctx.submit();
+
+  if (argc > 3)
+  {
+    std::cout << "Generating DOT output in " << argv[3] << '\n';
+    ctx.print_to_dot(argv[3]);
+  }
+
+  ctx.finalize();
+}