56fd68e7dd
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
21 lines
557 B
CMake
21 lines
557 B
CMake
file(
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GLOB example_srcs
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RELATIVE "${CMAKE_CURRENT_LIST_DIR}"
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CONFIGURE_DEPENDS
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*.cu
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*.cpp
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)
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foreach (thrust_target IN LISTS THRUST_TARGETS)
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thrust_get_target_property(config_device ${thrust_target} DEVICE)
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if (NOT config_device STREQUAL "CUDA")
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continue()
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endif()
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foreach (example_src IN LISTS example_srcs)
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get_filename_component(example_name "${example_src}" NAME_WLE)
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string(PREPEND example_name "cuda.")
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thrust_add_example(example_target ${example_name} "${example_src}" ${thrust_target})
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endforeach()
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endforeach()
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