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

     CHECK: Output Representation
CHECK-NEXT:  vertices[0] = (0,0)
CHECK-NEXT:  vertices[1] = (0,1)
CHECK-NEXT:  vertices[2] = (1,0)
CHECK-NEXT:  vertices[3] = (1,1)
CHECK-NEXT:  vertices[4] = (2,0)
CHECK-NEXT:  indices[0] = 0
CHECK-NEXT:  indices[1] = 2
CHECK-NEXT:  indices[2] = 1
CHECK-NEXT:  indices[3] = 2
CHECK-NEXT:  indices[4] = 3
CHECK-NEXT:  indices[5] = 1
CHECK-NEXT:  indices[6] = 2
CHECK-NEXT:  indices[7] = 4
CHECK-NEXT:  indices[8] = 3
[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: Output Representation`
			`CHECK-NEXT: vertices[0] = (0,0)`
			`CHECK-NEXT: vertices[1] = (0,1)`
			`CHECK-NEXT: vertices[2] = (1,0)`
			`CHECK-NEXT: vertices[3] = (1,1)`
			`CHECK-NEXT: vertices[4] = (2,0)`
			`CHECK-NEXT: indices[0] = 0`
			`CHECK-NEXT: indices[1] = 2`
			`CHECK-NEXT: indices[2] = 1`
			`CHECK-NEXT: indices[3] = 2`
			`CHECK-NEXT: indices[4] = 3`
			`CHECK-NEXT: indices[5] = 1`
			`CHECK-NEXT: indices[6] = 2`
			`CHECK-NEXT: indices[7] = 4`
			`CHECK-NEXT: indices[8] = 3`