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

     CHECK: initial data
CHECK-NEXT: 0 0 6 8 9 1 5 3 2 7 0 5 5 8 5 5 8 9 7 9 2 4 8 6 9 9 1 8 9 2
CHECK-NEXT: sorted data
CHECK-NEXT: 0 0 0 1 1 2 2 2 3 4 5 5 5 5 5 6 6 7 7 8 8 8 8 8 9 9 9 9 9 9
CHECK-NEXT: values
CHECK-NEXT: 0 1 2 3 4 5 6 7 8 9
CHECK-NEXT: counts
CHECK-NEXT: 3 2 3 1 1 5 2 2 5 6
CHECK-NEXT: Modal value 9 occurs 6 times
[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: initial data`
			`CHECK-NEXT: 0 0 6 8 9 1 5 3 2 7 0 5 5 8 5 5 8 9 7 9 2 4 8 6 9 9 1 8 9 2`
			`CHECK-NEXT: sorted data`
			`CHECK-NEXT: 0 0 0 1 1 2 2 2 3 4 5 5 5 5 5 6 6 7 7 8 8 8 8 8 9 9 9 9 9 9`
			`CHECK-NEXT: values`
			`CHECK-NEXT: 0 1 2 3 4 5 6 7 8 9`
			`CHECK-NEXT: counts`
			`CHECK-NEXT: 3 2 3 1 1 5 2 2 5 6`
			`CHECK-NEXT: Modal value 9 occurs 6 times`