dedf08166a
Added 863 files from NVIDIA/cccl sparse checkout: - c2h/ (27 files): Catch2 test helpers — generators, validators, runner - nvbench_helper/ (10 files): Benchmark harness utilities - cmake/ (29 files): CMake presets and build helpers - cudax/ (794 files): Experimental CUDA extensions - AGENTS.md: NVIDIA's official AI agent instructions for CCCL - CMakePresets.json: Standardized build configurations - cccl-version.json: Version tracking Also added CCCL_ASSET_MAP.md mapping all 4295 CCCL files to competition value and PRD items. cccl_upstream now covers 100% of competition-critical assets: - 27 tuning headers (SM80/90/100 benchmark data) - 32 dispatch headers (algorithm implementations) - 60 Thrust examples (correctness verification) - 217 CUB Catch2 tests (regression matrix) - 153 CUB benchmarks (parameter space search) - 18 CUB examples (API verification) - 27 test helpers + benchmark harness - 794 cudax experimental extensions
91 lines
4.2 KiB
Markdown
91 lines
4.2 KiB
Markdown
# CCCL Asset → Competition Value Mapping
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## Executive Summary
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project_6 now contains **4,295 CCCL files** (42MB) — a strategic subset of NVIDIA's CCCL (135MB full).
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We have **100%** of the competition-critical assets and **0%** of the irrelevant CI/Python/docs bloat.
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## Asset Inventory
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### Tier 1: Direct Competition Impact (ALL PRESENT ✓)
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| CCCL Asset | Files | PRD Items | Competition Path |
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|-----------|-------|-----------|-----------------|
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| 26 tuning_*.cuh (SM80/90/100 benchmarks) | 26 | [muh] 语言规范, all标定items | The benchmark data we're adapting to BI-V100 |
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| 27 muh tuning_*.cuh (BI-V100 adapted) | 29 | [EPIC] 27/27 CCCL parity | Our kernel tuning injection layer |
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| 32 dispatch_*.cuh (algorithm impl) | 32 | gen_patch injection points | Where muh values get injected |
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| 153 CUB benchmarks (.cu) | 153 | [muh-bench] reduce/scan/topk/transform | The actual benchmark binaries |
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| 60 Thrust examples (.cu) | 60 | [CCCL-verify] all 22 items | Correctness verification suite |
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| 217 CUB Catch2 tests (.cu) | 217 | [CCCL-test] all 8 items | Regression test matrix |
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| 18 CUB examples (.cu) | 18 | [CCCL-verify] device_reduce/scan/topk | API-level verification |
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### Tier 2: Build & Test Infrastructure (NOW PRESENT ✓)
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| CCCL Asset | Files | Purpose |
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|-----------|-------|---------|
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| c2h/ (test helpers) | 27 | Catch2 test generators, validators, runner |
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| nvbench_helper/ | 10 | Benchmark harness utilities for CUB benches |
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| cmake/ | 29 | CMake presets, build helpers, target definitions |
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| CMakePresets.json | 1 | Standardized build configurations |
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| AGENTS.md / CLAUDE.md | 1 | NVIDIA's own AI agent instructions for CCCL |
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### Tier 3: Extended Library (NOW PRESENT ✓)
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| CCCL Asset | Files | Purpose |
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|-----------|-------|---------|
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| cudax/ | 794 | Experimental CUDA extensions (memory resources, launch, async) |
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| libcudacxx/ | 1463 | CUDA C++ Standard Library headers |
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### NOT Included (by design)
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| CCCL Asset | Why Excluded |
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|-----------|-------------|
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| .github/, ci/ (65 files) | GitHub Actions workflows — irrelevant |
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| python/ | Python bindings — we use C++ directly |
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| docs/ (25 files) | Markdown docs — we have the source code |
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| .git history | ~100MB of git objects — no value |
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## Competition Critical Path
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```
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竞赛门槛: Token吞吐加权值 ≥ 8000
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= Output TPS × 16.796 (83%) + Input TPS × 2.799 (14%) + Cache TPS × 0.56 (3%)
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CCCL → muh → vllm injection chain:
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cccl_upstream/cub/.../tuning_reduce.cuh (SM100 benchmark data: ipt_16.tpb_512 speedup=1.148)
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→ muh/include/muh/tuning/tuning_reduce.cuh (BI-V100 adapted: SMEM ≤ 48KB)
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→ muh/gen_patch.py (extract bi100_* structs → unified diff)
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→ vllm csrc/attention/paged_attention_v2.cu (NUM_THREADS=512, VEC_SIZE=2)
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→ Docker build → Phanthy Cloud 4×BI-V100 → 竞赛评测
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```
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## CCCL Examples → PRD Items Cross-Reference
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| Thrust Example | PRD [CCCL-verify] Item | vllm Kernel Path |
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|---------------|----------------------|-----------------|
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| summary_statistics.cu | summary_statistics (P1) | benchmark 统计分析 |
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| sort.cu | sort (P0) | top-k sampling radix sort |
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| scan_by_key.cu | scan_by_key (P0) | softmax denominator |
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| stream_compaction.cu | stream_compaction (P0) | token filtering |
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| histogram.cu | histogram (P1) | repetition_penalty |
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| norm.cu | norm (P0) | RMSNorm 精度基准 |
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| saxpy.cu | saxpy (P0) | SiLU/RoPE/bias_add |
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| run_length_encoding.cu | run_length_encoding (P1) | attention mask 压缩 |
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| sum.cu + sum_rows.cu | sum+sum_rows (P0) | attention score reduction |
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| dot_products_with_zip.cu | dot_products (P1) | multi-head attention score |
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| sparse_vector.cu | sparse_vector (P1) | sparse attention |
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| weld_vertices.cu | weld_vertices (P1) | KV cache deduplication |
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| max_abs_diff.cu | max_abs_diff (P2) | 效果测试精度对比 |
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| monte_carlo.cu | monte_carlo (P2) | temperature sampling |
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## File Count Summary
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| Component | Before | After | Delta |
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|-----------|--------|-------|-------|
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| cccl_upstream/ total | 3,432 | 4,295 | +863 |
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| + c2h (test helpers) | 0 | 27 | +27 |
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| + nvbench_helper | 0 | 10 | +10 |
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| + cmake (build system) | 0 | 29 | +29 |
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| + cudax (experimental) | 0 | 794 | +794 |
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| + metadata files | 0 | 3 | +3 |
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