project_6/cccl_upstream/thrust/internal/test/thrust.example.discrete_voronoi.filecheck

     CHECK: [Initialize {{[0-9]+}}x{{[0-9]+}} Image]
CHECK-NEXT:   ( {{[0-9.]+}}ms )
CHECK-NEXT: [Copy to Device]
CHECK-NEXT:   ( {{[0-9.]+}}ms )
CHECK-NEXT: [JFA stepping]
CHECK-NEXT:   ( {{[0-9.]+}}ms )
CHECK-NEXT:   ( {{[0-9.]+}} MPixel/s )
CHECK-NEXT: [Device to Host Copy]
CHECK-NEXT:   ( {{[0-9.]+}}ms )
CHECK-NEXT: [PGM Export]
CHECK-NEXT:   ( {{[0-9.]+}}ms )
[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: [Initialize {{[0-9]+}}x{{[0-9]+}} Image]`
			`CHECK-NEXT: ( {{[0-9.]+}}ms )`
			`CHECK-NEXT: [Copy to Device]`
			`CHECK-NEXT: ( {{[0-9.]+}}ms )`
			`CHECK-NEXT: [JFA stepping]`
			`CHECK-NEXT: ( {{[0-9.]+}}ms )`
			`CHECK-NEXT: ( {{[0-9.]+}} MPixel/s )`
			`CHECK-NEXT: [Device to Host Copy]`
			`CHECK-NEXT: ( {{[0-9.]+}}ms )`
			`CHECK-NEXT: [PGM Export]`
			`CHECK-NEXT: ( {{[0-9.]+}}ms )`