feat(cccl): integrate missing CCCL directories — python/, ci/, .agent/, docs/, test/

Sparse-checkout from NVIDIA/cccl main branch to complete cccl_upstream:

Added:
- python/cuda_cccl/ (226 files) — Python bindings for device-level algorithms
  Critical for muh toolchain: cuda.compute.reduce_into, scan, radix_sort, etc.
  Includes 204 .py files with full test coverage for all 27 algorithms
- ci/ (163 files) — Build/test infrastructure
  build_cub.sh, test_cub.sh, build_and_test_targets.sh, matrix.yaml
  Directly maps to our [INFRA-CI] and [INFRA-BUILD] items
- .agent/skills/ (7 files) — NVIDIA's own agent skills for CCCL
  cccl-style/SKILL.md, cccl-test/SKILL.md, sass-diff/SKILL.md
- docs/ (491 files) — Official CCCL documentation
  CI references, CMake guides, Python compute docs, libcudacxx PTX docs
- test/ (12 files) — Top-level integration tests (cuda_smoke, stdpar)
- Root configs: .clang-format, .clang-tidy, CONTRIBUTING.md, pyproject.toml
- CLAUDE.md symlink → AGENTS.md (NVIDIA's standard)

cccl_upstream now mirrors full NVIDIA/cccl structure:
  Before: 42M (cub + thrust + libcudacxx + cudax + c + examples + benchmarks)
  After:  53M (+python +ci +docs +.agent +test +configs)

This completes the CCCL base needed for:
- [muh-bench] items: ci/util/build_and_test_targets.sh for targeted builds
- [CCCL-verify] items: python/cuda_cccl/tests/ as reference implementations
- [CCCL-test] items: ci/test_cub.sh, ci/test_thrust.sh
- Agent workflow: .agent/skills/ for consistent style and test patterns
This commit is contained in:
muh-bot
2026-08-07 02:34:33 +00:00
parent 3f97dca7ad
commit 2a7ca101d7
908 changed files with 121615 additions and 0 deletions

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"NODE_19" [style="filled" fillcolor="white" label="baz"]
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"NODE_11" [style="filled" fillcolor="white" label="t3\nA(rw)(0) \nB(read)(0) \nC(read)(0) "]
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digraph {
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digraph {
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T_1 -> T_2 [label="A"];
T_1 -> T_3 [label="A"];
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T_3 -> T_4 [label ="C"];
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digraph {
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K2 -> K3 [ltail=cluster_0,lhead=cluster_1,minlen=2];
K2 -> K4 [ltail=cluster_0,lhead=cluster_2,minlen=2];
K3 -> cb [ltail=cluster_1,lhead=cluster_3,minlen=2];
K4 -> cb [ltail=cluster_2,lhead=cluster_3,minlen=2];
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digraph {
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AA -> CA [ltail=cluster_00,lhead=cluster_01,minlen=2];
CB -> K1 [ltail=cluster_11,lhead=cluster_0,minlen=2];
AB -> CB [ltail=cluster_10,lhead=cluster_11,minlen=2];
subgraph cluster_1 {
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K3 [label="K3"];
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subgraph cluster_2 {
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K2 -> K4 [ltail=cluster_0,lhead=cluster_2,minlen=2];
subgraph cluster_02 {
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CA2 [label="Copy A D->A"];
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subgraph cluster_12 {
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subgraph cluster_3 {
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K3 -> CA2 [ltail=cluster_1,lhead=cluster_02,minlen=2];
K4 -> CB2 [ltail=cluster_2,lhead=cluster_12,minlen=2];
CA2 -> cb [ltail=cluster_02,lhead=cluster_3,minlen=2]
CB2 -> cb [ltail=cluster_12,lhead=cluster_3,minlen=2]
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