[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/python/cuda_cccl/benchmarks/compute/host/test_host_pytest_benchmark.py

@@ -0,0 +1,95 @@
+# Copyright (c) 2026, NVIDIA CORPORATION & AFFILIATES. ALL RIGHTS RESERVED.
+#
+# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+
+from __future__ import annotations
+
+import pytest
+from host_benchmark_cases import (
+    CALL_CASES,
+    CASES,
+    HostBenchmarkCase,
+    patch_wrapper_to_skip_native_compute,
+    synchronize,
+)
+
+import cuda.compute as cc
+
+pytest.importorskip("pytest_benchmark")
+
+BUILD_TIME_ROUNDS = 10
+ONESHOT_ROUNDS = 20
+ONESHOT_ITERATIONS = 100
+TWOSHOT_ROUNDS = 20
+TWOSHOT_ITERATIONS = 1000
+
+
+def _case_params(cases: list[HostBenchmarkCase]) -> list[pytest.ParameterSet]:
+    params = []
+    for case in cases:
+        marks = []
+        if case.skip_reason is not None:
+            marks.append(pytest.mark.skip(reason=case.skip_reason))
+        params.append(pytest.param(case, id=case.name, marks=marks))
+    return params
+
+
+@pytest.mark.benchmark(group="cuda.compute.host.build_time")
+@pytest.mark.parametrize("case", _case_params(CASES))
+def test_build_time(benchmark, case: HostBenchmarkCase):
+    state = case.setup()
+    synchronize()
+
+    def setup() -> None:
+        cc.clear_all_caches()
+
+    def build():
+        return case.make_wrapper(state)
+
+    benchmark.pedantic(
+        build,
+        setup=setup,
+        rounds=BUILD_TIME_ROUNDS,
+        iterations=1,
+        warmup_rounds=0,
+    )
+
+
+@pytest.mark.benchmark(group="cuda.compute.host.oneshot_cached")
+@pytest.mark.parametrize("case", _case_params(CALL_CASES))
+def test_oneshot_cached_host_overhead(benchmark, case: HostBenchmarkCase):
+    cc.clear_all_caches()
+    state = case.setup()
+    wrapper = case.make_wrapper(state)
+    patch_wrapper_to_skip_native_compute(wrapper, case.noop_return_kind)
+    synchronize()
+
+    def call() -> None:
+        case.oneshot(state)
+
+    benchmark.pedantic(
+        call,
+        rounds=ONESHOT_ROUNDS,
+        iterations=ONESHOT_ITERATIONS,
+        warmup_rounds=0,
+    )
+
+
+@pytest.mark.benchmark(group="cuda.compute.host.twoshot_call")
+@pytest.mark.parametrize("case", _case_params(CALL_CASES))
+def test_twoshot_call_host_overhead(benchmark, case: HostBenchmarkCase):
+    cc.clear_all_caches()
+    state = case.setup()
+    wrapper = case.make_wrapper(state)
+    patch_wrapper_to_skip_native_compute(wrapper, case.noop_return_kind)
+    synchronize()
+
+    def call() -> None:
+        case.twoshot(state, wrapper)
+
+    benchmark.pedantic(
+        call,
+        rounds=TWOSHOT_ROUNDS,
+        iterations=TWOSHOT_ITERATIONS,
+        warmup_rounds=0,
+    )