project_6/cccl_upstream/cub/test/cmake/CMakeLists.txt

# Check source code for issues that can be found by pattern matching:
add_test(
  NAME cub.test.cmake.check_source_files
  # gersemi: off
  COMMAND
    "${CMAKE_COMMAND}"
      -D "CUB_SOURCE_DIR=${CUB_SOURCE_DIR}"
      -P "${CMAKE_CURRENT_LIST_DIR}/check_source_files.cmake"
  # gersemi: on
)
[INFRA] Import NVIDIA/CCCL upstream as optimization reference library CCCL (CUDA C++ Core Libraries) provides: - CUB: device/block/warp-level GPU primitives (reduce, scan, sort, topk) - Thrust: high-level parallel algorithms (transform_reduce, sort, scan) - libcudacxx: CUDA C++ standard library (atomics, barriers, memory) - cudax: experimental features (memory resources, allocators) - Tuning policies: per-SM hardware-specific algorithm parameters Competition optimization vectors mapped to CCCL: - Output TPS (83% weight): warp_reduce, block_reduce, device_topk - Input TPS (14% weight): device_scan, block_load, prefetch - Cache TPS (3% weight): prefix caching strategy patterns - Memory (0.9 util): pooled/cached/buddy allocators Source: https://github.com/NVIDIA/cccl (shallow clone, HEAD only) License: Apache-2.0 2026-07-30 09:35:51 +00:00			`# Check source code for issues that can be found by pattern matching:`
			`add_test(`
			`NAME cub.test.cmake.check_source_files`
			`# gersemi: off`
			`COMMAND`
			`"${CMAKE_COMMAND}"`
			`-D "CUB_SOURCE_DIR=${CUB_SOURCE_DIR}"`
			`-P "${CMAKE_CURRENT_LIST_DIR}/check_source_files.cmake"`
			`# gersemi: on`
			`)`