2a7ca101d7
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
69 lines
1.4 KiB
Plaintext
69 lines
1.4 KiB
Plaintext
digraph {
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compound=true;
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subgraph cluster_00 {
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label="";
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AA [label="Allocate A"];
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}
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subgraph cluster_01 {
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label="";
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CA [label="Copy A H->D"];
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}
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subgraph cluster_10 {
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label="";
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AB [label="Allocate B"];
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}
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subgraph cluster_11 {
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label="";
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CB [label="Copy B H->D"];
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}
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subgraph cluster_0 {
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label="T1";
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K1 [label="K1"];
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K2 [label="K2"];
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}
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CA -> K1 [ltail=cluster_01,lhead=cluster_0,minlen=2];
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AA -> CA [ltail=cluster_00,lhead=cluster_01,minlen=2];
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CB -> K1 [ltail=cluster_11,lhead=cluster_0,minlen=2];
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AB -> CB [ltail=cluster_10,lhead=cluster_11,minlen=2];
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subgraph cluster_1 {
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label="T2";
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K3 [label="K3"];
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}
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subgraph cluster_2 {
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label="T3";
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K4 [label="K4"];
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}
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K1 -> K2;
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K2 -> K3 [ltail=cluster_0,lhead=cluster_1,minlen=2];
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K2 -> K4 [ltail=cluster_0,lhead=cluster_2,minlen=2];
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subgraph cluster_02 {
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label="";
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CA2 [label="Copy A D->A"];
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}
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subgraph cluster_12 {
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label="";
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CB2 [label="Copy B D->A"];
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}
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subgraph cluster_3 {
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label="T4";
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cb [label="callback"];
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}
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K3 -> CA2 [ltail=cluster_1,lhead=cluster_02,minlen=2];
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K4 -> CB2 [ltail=cluster_2,lhead=cluster_12,minlen=2];
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CA2 -> cb [ltail=cluster_02,lhead=cluster_3,minlen=2]
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CB2 -> cb [ltail=cluster_12,lhead=cluster_3,minlen=2]
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}
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