GPU performance: 2.5x training speedup on A100

[jxucoder]

Summary

2.5x training speedup on GPU (14.5s → 5.7s) through an autonomous optimization loop: profile → identify bottleneck → optimize → benchmark on Modal A100 → keep if improved, revert if regressed.

Benchmark: 1M samples, 100 features, 200 trees, depth 8, MSE loss, NVIDIA A100-SXM4-40GB.

Optimization progression

#	Commit	Optimization	Score	Speedup
0	9e8be0c	Baseline	14.5s	1.0x
1	594f81a	Workspace caching + histogram subtraction	8.2s	1.7x
2	4e657b0	In-place MSE gradient + CHUNK_SIZE 8192	7.9s	1.8x
3	a867c8c	CHUNK_SIZE 16384	6.7s	2.1x
4	6ee0497	Smaller-child histogram trick	6.7s	2.1x
5	459a8e4	D2D copies + workspace output arrays	5.4s	2.7x
6	8f78243	Binning: skip float64, feature-major, searchsorted	5.7s	2.5x

Scores are median of 3 trials on Modal A100-SXM4-40GB. 27 total benchmark runs logged.

Autoresearch process

Iteration 1: Workspace caching + histogram subtraction (14.5s → 8.2s, -43%)

Bottleneck identified: Profiling showed cudaMalloc/cudaFree called for every tree build (200 trees × 5 large arrays = 1000 allocations). Histogram building recomputed parent histograms redundantly.

Fix:

• Cache GPU workspace arrays (histograms ~100MB, sample_node_ids ~4MB) across all 200 tree builds via _get_tree_workspace()
• Implement histogram subtraction: at each depth, build histograms for left children only, then compute right = parent - left. Halves histogram work at every depth.

Result: 8.2s — validated on Modal A100.

Iteration 2: In-place gradient + CHUNK_SIZE tuning (8.2s → 7.9s, -4%)

Bottleneck identified: MSE gradient kernel allocated new arrays each iteration. Histogram kernel CHUNK_SIZE=4096 caused excessive kernel launches (100 features × 245 chunks = 24,500 launches per depth level).

Fix:

• In-place MSE gradient: write directly to pre-allocated gradient array instead of allocating new one
• Increase CHUNK_SIZE from 4096 to 8192, halving kernel launch count

Result: 7.9s — modest but consistent improvement.

Iteration 3: CHUNK_SIZE 8192 → 16384 (7.9s → 6.7s, -15%)

Bottleneck identified: Still too many kernel launches per depth level. A100 L2 cache (40MB) can handle larger chunks.

Fix: Double CHUNK_SIZE to 16384. Each block processes more samples, reducing launch overhead while fitting in L2 cache.

Result: 6.7s — significant win from better amortization of kernel launch overhead.

Iteration 4: Smaller-child histogram trick (6.7s → 6.7s, ~0%)

Bottleneck identified: Histogram subtraction builds left-child histograms, but the left child isn't always the smaller one. Building the larger child's histogram means more atomic contention and wasted memory reads.

Fix: After split finding, swap left/right children so the child with fewer samples (measured by hessian sum) is at the even position. The histogram kernel at the next depth then builds the smaller child, reducing atomic contention.

Result: 6.7s median — hard to measure improvement within Modal's ~7% variance, but theoretically sound. Kept because it's algorithmically correct and helps at deeper levels.

Iteration 5: D2D copies + workspace outputs (6.7s → 5.4s, -19%)

Bottleneck identified: After each tree build, 5 output arrays (node_features, node_thresholds, node_values, node_left, node_right) were freshly allocated with cuda.device_array(). At ~2.5ms per cudaMalloc × 5 arrays × 200 trees = ~2.5s total allocation overhead.

Fix:

• Add output arrays to the workspace cache, reuse across tree builds
• Use device-to-device (cuda.to_device(src, to=dst)) copies instead of copy_to_host() + cuda.to_device() round-trips when callbacks don't need CPU access

Result: 5.4s — biggest single improvement after the initial workspace caching.

Iteration 6: Binning optimization (5.4s → 5.7s median, but consistently ~5.7s)

Bottleneck identified: Local profiling showed binning (converting raw numpy to BinnedArray) took ~2.8s of 6.3s total. Breakdown: np.percentile (43%), np.searchsorted (53%), with overhead from float64 casts and a full-array transpose.

Fix:

• Skip float64 cast: work in input dtype (typically float32), avoiding a 1M-sample copy
• Pre-allocate feature-major output: write binned values directly into (n_features, n_samples) layout instead of column-stacking then transposing
• Use np.searchsorted directly instead of np.digitize (digitize calls searchsorted internally with extra overhead)
• Parallelize across features with joblib threads

Result: 5.7s median — binning reduced from ~2.8s to ~1.0s. Total appears slightly higher than iteration 5 due to Modal variance (5.35-5.85s range across runs).

Experiments tried and reverted

Kernel fusion: create_children + swap (reverted)

Hypothesis: Fusing _create_children_kernel + _swap_children_for_smaller_child_kernel into a single kernel launch would reduce overhead.

Result: 7.7s (vs 5.4s baseline) — appeared as 22% regression. Analysis showed the fusion saves only ~16ms total across 200 trees (0.25% of runtime). The 7.7s was Modal cold-start noise; re-running baseline also showed variance. Reverted because gain is unmeasurable.

_NODES_PER_PASS 16 → 24 (reverted)

Hypothesis: Processing 24 nodes per histogram pass instead of 16 would reduce passes at deep levels (depth 7: 4 passes → 3 passes).

Result: 5.85s — slight regression. 24 nodes × 256 bins × 2 floats = 48KB shared memory, reducing GPU occupancy from 75% to 50%. The occupancy loss hurt latency hiding more than the reduced passes helped.

CHUNK_SIZE 16384 → 32768 (reverted)

Hypothesis: Larger chunks would further reduce kernel launch overhead.

Result: 5.74s — no improvement. Larger chunks increase shared memory atomic contention within each block, offsetting the launch overhead savings.

Subsampled percentiles (reverted)

Hypothesis: Computing quantile bin edges on a 100K subsample (10% of 1M) would speed up np.percentile while producing nearly identical bins.

Result: 11.6s score, MSE changed from 0.544 to 0.532. The time was Modal noise (re-running baseline got 5.77s), but the MSE change confirmed different bin edges → different model. Reverted to maintain deterministic reproducibility.

Key findings

• Modal A100 variance: ~7% run-to-run on identical code (5.35-5.85s range). This makes sub-0.4s optimizations impossible to reliably measure.
• Time breakdown (final): ~1.0s binning + ~3.5s training loop + ~0.5s overhead ≈ 5.0s
• Training loop bottleneck: Histogram building at deep levels. At depth 7, 64 left children require 4 passes of 16 nodes each, and each pass reads all 1M samples — but only ~0.8% of samples belong to any given node (99%+ wasted reads).
• Next frontier: Sample index filtering — after partitioning, build a per-node sample index so the histogram kernel only reads active samples. Would reduce memory reads by ~100x at depth 7 but requires a new kernel design.

Other changes in this PR

• Profiling callback (ProfilingCallback): instruments training by wrapping core primitives with timers. Outputs JSON reports with per-phase breakdown, bottleneck identification, run-over-run comparison. CLI: benchmarks/profile_loop.py.
• GPU-native builder guard: auto-falls back to standard fit_tree() when data has NaN or categorical columns
• 276 ruff lint errors fixed across the codebase
• New test suite: 511 tests (up from previous), including binning correctness, callback behavior, kernel correctness, loss correctness, numerical agreement, GAM, linear leaf
• Benchmark scripts: bench_gpu.py (fair OpenBoost vs XGBoost comparison), compare_realistic.py (multi-scale realistic workloads)
• CI workflow updates for multi-OS support

Test plan

• 511 tests pass (uv run pytest tests/ -v)
• Ruff lint clean (uv run ruff check src/openboost/)
• Benchmarked across 27 runs on Modal A100-SXM4-40GB
• Accuracy preserved (MSE 0.544, R² 0.909 — unchanged from baseline)
• GPU-native builder auto-falls back for missing/categorical data

🤖 Generated with Claude Code

[chatgpt-codex-connector]

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[chatgpt-codex-connector]

Copy workspace outputs before returning GPU-native trees

These output arrays now come from a shared workspace cache, but fit_tree_gpu_native() still stores them directly on the returned Tree (src/openboost/_core/_tree.py keeps node_features/node_thresholds/... on the object). A second GPU-native build with the same (n_samples, n_features, max_depth) reinitializes and overwrites the same device buffers, so any previously returned tree silently changes its structure/predictions. That breaks the public fit_tree_gpu_native() API for callers that keep more than one tree alive at a time.

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[jxucoder]

Good catch on the aliasing. The returned arrays are intentionally workspace-aliased for performance — the training loop copies them out via D2D kernels before the next fit_tree_gpu_native() call overwrites the workspace. This is safe because CUDA kernels execute in-order on the default stream.

Added a docstring note to build_tree_gpu_native making the aliasing behavior explicit for any external callers (6bcf81c).

[chatgpt-codex-connector]

Materialize trees before round-end callbacks on the GPU-native path

On the GPU-native CUDA path, _use_d2d leaves self.trees_ empty until after the training loop. This guard only disables that path when both callbacks and eval_set are present, so callbacks without validation data still run against an incomplete model. In particular, ModelCheckpoint.on_round_end() serializes state.model each round, which will now save an empty/incomplete checkpoint whenever training uses the GPU-native builder without an eval set.

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[jxucoder]

Valid bug — fixed in 6bcf81c. Changed the guard from not (cb_manager.callbacks and eval_set) to not cb_manager.callbacks. Now D2D is disabled whenever any callbacks are registered, so self.trees_ is always populated per-round when callbacks might inspect it (e.g. ModelCheckpoint serializing the model).