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project_6/cccl_upstream/python/cuda_cccl/benchmarks/compute/AGENTS.md
EngineX CI 56fd68e7dd [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

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CCCL Python cuda.compute benchmarks

This directory contains the code for the Python cuda.compute benchmarks. They are migrated from the original C++ benchmarks and they should match the C++ implementations as closely as possible.

The original C++ benchmarks are available in this repository in: ../../../../cub/benchmarks/bench/ We follow the same directory structure and naming conventions converting to Python were appropriate.

The code for cuda.compute is in this repository under: ../../../../python/cuda_cccl/cuda/compute/. Look into this directory when searching for existing APIs in Python.

The benchmarks use nvbench to run the benchmarks and report the results.