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project_6/cccl_upstream/thrust/testing/cuda/CMakeLists.txt
EngineX CI 56fd68e7dd [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

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990 B
CMake

file(
GLOB test_srcs
RELATIVE "${CMAKE_CURRENT_LIST_DIR}"
CONFIGURE_DEPENDS
*.cu
*.cpp
)
foreach (thrust_target IN LISTS THRUST_TARGETS)
thrust_get_target_property(config_device ${thrust_target} DEVICE)
if (NOT config_device STREQUAL "CUDA")
continue()
endif()
foreach (test_src IN LISTS test_srcs)
get_filename_component(test_name "${test_src}" NAME_WLE)
string(PREPEND test_name "cuda.")
# Create two targets, one with RDC enabled, the other without. This tests
# both device-side behaviors -- the CDP kernel launch with RDC, and the
# serial fallback path without RDC.
thrust_add_test(seq_test_target ${test_name}.cdp_0 "${test_src}" ${thrust_target})
thrust_configure_cuda_target(${seq_test_target} RDC OFF)
if (THRUST_ENABLE_RDC_TESTS)
thrust_add_test(cdp_test_target ${test_name}.cdp_1 "${test_src}" ${thrust_target})
thrust_configure_cuda_target(${cdp_test_target} RDC ON)
endif()
endforeach()
endforeach()