| --- |
| license: apache-2.0 |
| tags: |
| - profiling |
| - ray |
| - gpu |
| - nsight-systems |
| - ray-data |
| pretty_name: Ray Data GPU Idle Profiles (B200) |
| --- |
| |
| # Ray Data GPU Idle Profiles (B200) |
|
|
| Nsight Systems profiles (exported to SQLite, readable by |
| [`nsys-ai`](https://github.com/GindaChen/nsys-ai)) from an experiment on how a |
| Ray Data pipeline keeps a GPU idle, and how the loss splits between *moving |
| data* and *waiting for data*. Captured on a single NVIDIA B200 with Ray 2.58.0 |
| / master, PyTorch 2.14.0+cu130, Nsight Systems 2026.1.3. |
|
|
| These profiles back the write-up in the iThome Ironman series |
| 「GPU 很忙？他真的有在做事嗎？」 (Days 27–29), and are shared so the numbers and |
| the before/after comparison are independently reproducible. |
|
|
| ## The workload |
|
|
| A deliberately small pipeline so scheduling shows through instead of the model: |
|
|
| ``` |
| ray.data.range → 32 blocks → 2048×8192 FP32 (64 MiB) per batch |
| → H2D → 9 matmuls (~39 ms) → D2H; CPU prep ~40 ms per batch |
| ``` |
|
|
| Two knobs move across the files: where data prep runs (inside the GPU UDF vs a |
| separate CPU operator), and whether pinned staging overlaps with compute. |
|
|
| ## Files |
|
|
| Analysis window is each profile's kernel span on `device 0` |
| (`CUDA_VISIBLE_DEVICES` exposes one physical card, renumbered to 0). |
|
|
| | File | Stage | device idle | copy_ms | note | |
| | --- | --- | --- | --- | --- | |
| | `inline.sqlite` | prep inside the GPU UDF | 59.0% | — | serial load→compute | |
| | `split.sqlite` | prep as a CPU operator | 29.1% | — | H2D 8.5 GB/s pageable | |
| | `pinned.sqlite` | naive `pin_memory` in UDF | 31.4% | — | worse: staging still serial | |
| | `overlap.sqlite` | `enable_true_multi_threading` + per-thread stream | 24.7% | — | 31.3% of H2D overlapped | |
| | `patched_split.sqlite` | clean profile after fixing Ray nsight bug | 31.3% | 339.6 | idle split by copy vs wait | |
| | `final_split.sqlite` | **baseline** for the prototype | 62.4% | 830.3 | before pinned-staging | |
| | `final_staged.sqlite` | **after** map_batches pinned-staging prototype | 54.8% | 155.1 | copy_ms −82% | |
|
|
| `patched_split.nsys-rep` is the raw report for opening in the Nsight Systems GUI. |
|
|
| Code: |
|
|
| - `workload.py` — the four modes (inline / split / pinned / overlap). |
| - `workload_staged.py` — the `ActorPoolStrategy(pinned_staging=True)` prototype run. |
| - `repro_flush.py` — minimal A/B for the nsys teardown-flush race |
| ([ray#60904](https://github.com/ray-project/ray/issues/60904)). |
| - `our_changes.patch` — the map_batches pinned-staging prototype (vs Ray master `c8466ab8`). |
| - `cpp_fix_v3.diff` — the C++ fix for the teardown-flush race |
| ([ray#66129](https://github.com/ray-project/ray/pull/66129)). |
| |
| ## Reproduce the analysis |
| |
| ```bash |
| pip install nsys-ai |
| # the copy_ms field (newer nsys-ai) splits "moving data" out of the idle |
| nsys-ai skill run gpu_idle_gaps final_split.sqlite -p device=0 --format json |
| nsys-ai skill run gpu_idle_gaps final_staged.sqlite -p device=0 --format json |
| ``` |
| |
| ## Two Ray bugs found while capturing these |
| |
| Getting a trustworthy profile out of Ray Data first meant fixing two Ray Core |
| bugs, both reduced to minimal repros and reported upstream: |
| |
| - **nsight runtime_env drops the selected Python command** (worker never starts): |
| [ray#66093](https://github.com/ray-project/ray/issues/66093) / |
| [PR #66094](https://github.com/ray-project/ray/pull/66094). |
| - **nsys report killed during teardown** (empty/missing `.nsys-rep`): |
| [ray#60904](https://github.com/ray-project/ray/issues/60904) / |
| [PR #66129](https://github.com/ray-project/ray/pull/66129). |
| |
| ## Caveats |
| |
| Numbers with the profiler on run larger than without it; the paired *difference* |
| is what matters, not the absolute idle. The box was shared, so wall-time |
| comparisons use the minimum across runs. See the series notes for the full |
| methodology and the "local metric vs mechanism vs end-to-end" distinction. |
| |
