project_6/cccl_upstream/cub/.clang-tidy

---
InheritParentConfig: true
CheckOptions:
 - key:             modernize-loop-convert.MaxCopySize
   value:           '16'
 - key:             modernize-loop-convert.MinConfidence
   value:           reasonable
 - key:             modernize-pass-by-value.IncludeStyle
   value:           llvm
 - key:             modernize-replace-auto-ptr.IncludeStyle
   value:           llvm
 - key:             modernize-use-nullptr.NullMacros
   value:           'NULL'
...
[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			`---`
			`InheritParentConfig: true`
			`CheckOptions:`
			`- key: modernize-loop-convert.MaxCopySize`
			`value: '16'`
			`- key: modernize-loop-convert.MinConfidence`
			`value: reasonable`
			`- key: modernize-pass-by-value.IncludeStyle`
			`value: llvm`
			`- key: modernize-replace-auto-ptr.IncludeStyle`
			`value: llvm`
			`- key: modernize-use-nullptr.NullMacros`
			`value: 'NULL'`
			`...`