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

     CHECK: ******Summary Statistics Example*****
CHECK-NEXT: The data: 4 7 13 16
CHECK-NEXT: Count              : 4
CHECK-NEXT: Minimum            : 4
CHECK-NEXT: Maximum            : 16
CHECK-NEXT: Mean               : 10
CHECK-NEXT: Variance           : 30
CHECK-NEXT: Standard Deviation : 4.74342
CHECK-NEXT: Skewness           : 0
CHECK-NEXT: Kurtosis           : 1.36
[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: ****Summary Statistics Example***`
			`CHECK-NEXT: The data: 4 7 13 16`
			`CHECK-NEXT: Count : 4`
			`CHECK-NEXT: Minimum : 4`
			`CHECK-NEXT: Maximum : 16`
			`CHECK-NEXT: Mean : 10`
			`CHECK-NEXT: Variance : 30`
			`CHECK-NEXT: Standard Deviation : 4.74342`
			`CHECK-NEXT: Skewness : 0`
			`CHECK-NEXT: Kurtosis : 1.36`