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

     CHECK: SAXPY (functor method)
CHECK-NEXT: 2 * 1 + 1 = 3
CHECK-NEXT: 2 * 2 + 1 = 5
CHECK-NEXT: 2 * 3 + 1 = 7
CHECK-NEXT: 2 * 4 + 1 = 9
CHECK-NEXT: SAXPY (placeholder method)
CHECK-NEXT: 2 * 1 + 1 = 3
CHECK-NEXT: 2 * 2 + 1 = 5
CHECK-NEXT: 2 * 3 + 1 = 7
CHECK-NEXT: 2 * 4 + 1 = 9
[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: SAXPY (functor method)`
			`CHECK-NEXT: 2 * 1 + 1 = 3`
			`CHECK-NEXT: 2 * 2 + 1 = 5`
			`CHECK-NEXT: 2 * 3 + 1 = 7`
			`CHECK-NEXT: 2 * 4 + 1 = 9`
			`CHECK-NEXT: SAXPY (placeholder method)`
			`CHECK-NEXT: 2 * 1 + 1 = 3`
			`CHECK-NEXT: 2 * 2 + 1 = 5`
			`CHECK-NEXT: 2 * 3 + 1 = 7`
			`CHECK-NEXT: 2 * 4 + 1 = 9`