coorl-lost-cities/docs/research/julia_port_evaluation.md

Julia Port Evaluation

Tracks evidence for and against porting the Deep CFR training pipeline from Python/Cython to Julia. Plot/game/parity stays Python regardless; the candidate scope is the training stack (traversal, networks, inference).

Why we are even considering this

docs/performance.md "Option A Bench Result and Structural Ceiling" established that the current sync-blocking traversal in Python multiprocessing caps realized batch size at num_workers. Escaping that ceiling requires either restructuring traversal (Option B/C) or moving to a runtime where threads can carry many concurrent traversals in one process. Julia is the most credible candidate for the latter (Mojo too immature, free-threaded CPython requires nogil-cleaning our existing Cython — see docs/reports/cost_* triplet).

Decision criteria for going forward:

1. Single-thread compute parity with current Cython (or better).
2. GC behavior under tight CFR-shape recursion is acceptable (low pause time, low share).
3. Multi-thread scaling on the same CFR-shape pattern is near-linear, demonstrating the GIL-free promise actually holds.
4. ML stack (Flux.jl + CUDA.jl) covers our needs (MLP forward, AD, GPU). Our model is small and standard.
5. A real-game-state slice can be ported and compared head-to-head.

Evidence so far

2026-05-07 — Safe heuristic single-thread parity (criterion 1)

Path: experiments/julia_safe_heuristic/.

1,838 snapshots in 157.471 ms median (~85.6 μs/call). Action-sequence parity vs Python. Same order of magnitude as the Cython port of the same bot (Cython gives ~2.55× over original Python on a 200-game eval).

Verdict on criterion 1: Pass for isolated single-call work.

2026-05-07 — CFR-shape recursion toy (criteria 1, 2)

Path: experiments/julia_cfr_toy/. See that directory's README for the full table.

Headline: Julia ~1.9× faster than Cython, 0 MB allocation, 0% GC time on the hot path. Root regret parity ε ≤ 1e-9.

Verdict on criterion 1: Pass. Julia matches or beats Cython on the CFR-shape pattern.

Verdict on criterion 2: Strong pass. Type-stable code produces zero heap traffic. The GC concern that was the main argument against Julia adoption did not materialize here.

Caveats: Cython 21.3 MB alloc suggests room for tighter typing; best-effort Cython could narrow the gap. Toy is not a game.

2026-05-07 — CFR-shape multi-thread scaling on the same toy (criterion 3)

Path: experiments/julia_cfr_toy/ (bench_cfr_threaded.jl). Same 100 iters × 1000 traversals total work split across threads. Thread- local trees, root regrets reduced at the end.

threads	iter ms	speedup	efficiency
1	0.223	1.00×	100%
2	0.130	1.71×	85%
4	0.097	2.29×	57%
8	0.093	2.40×	30%

The light workload is too small to settle the question: 1T iter time is only ~0.2 ms, so thread dispatch overhead can dominate.

Heavy mode keeps the same tree and algorithm but increases traversals per iteration from 1000 to 50000 (50× work). This raises 1T iter time to 8.272 ms.

threads	iter ms	speedup	efficiency
1	8.272	1.00×	100%
2	5.058	1.64×	82%
4	2.602	3.18×	79%
8	1.739	4.76×	59%

Verdict on criterion 3: PARTIAL. Heavy 8T efficiency is 59%. Dispatch overhead was a significant part of the light-mode result, but the heavier workload still does not reach near-linear 8-thread scaling. Julia delivers useful throughput scaling (4.76× at 8T), but this is not the decisive PASS threshold for the threading criterion.

Open evidence (criteria 4, 5)

• Flux.jl + CUDA.jl MLP forward at bs={1, 64, 256}. Compare to PyTorch numbers in docs/performance.md. Criterion 4. Not started.
• Real-game-state slice port. Port play_card + scoring, run on a fixed corpus of game states, compare to current Cython. Criterion 5. Not started.

Decision posture

Promising but not enough to justify a port yet. The completed benchmarks remove the main risk (GC under recursion) and confirm compute parity. Heavy thread scaling upgrades criterion 3 from inconclusive to PARTIAL: 8T is 4.76× faster than 1T, but 59% efficiency is below the near-linear PASS threshold.

This means Julia remains a credible option, but not a slam dunk. ML-stack and game-state evidence (criteria 4, 5) must be positive before starting a serious port plan. If those are positive, criterion 3 should be revisited on a real traversal slice where each thread has substantially more work than this toy benchmark.

Do not commit to porting on the current evidence alone.