Add an experiment-only explicit-continuation traversal prototype that batches policy requests, records CPU/CUDA parity results, and updates the Option B plan with Phase 0/1 evidence.
Co-Authored-By: Codex <codex@openai.com>
Add a microbench that separates game/encoding overhead, single-request policy boundary overhead, and batched PyTorch forward lower bounds. Record CPU/CUDA results and link the finding from the Julia port evaluation.
Co-Authored-By: Codex <codex@openai.com>
experiments/julia_cfr_toy/ ports a synthetic CFR external-sampling
traversal to both Julia and Cython for direct head-to-head
measurement of the actual project hot-path pattern (recursive
tree + mutable regret state + branch-heavy legal-action logic).
Headline result (2026-05-07, single run): Julia ~1.9× faster than
Cython on this pattern, 0 MB allocation, 0% GC time. Root regret
parity ε ≤ 1e-9. The GC-pause concern that was the main argument
against Julia adoption did not materialize. Cython's 21.3 MB
allocation suggests its implementation can be tightened, so the
honest gap window is roughly 1.3×–1.9×.
Multi-thread scaling (bench_cfr_threaded.jl): 2.44× wall-clock at 8T
but only 31% efficiency — inconclusive, likely a toy-size artifact
(per-thread workload too small to amortize dispatch). A heavier
per-thread workload sweep is the remaining decisive test.
docs/research/julia_port_evaluation.md captures this evidence
alongside the earlier safe-heuristic single-thread parity result
and lists the remaining decision criteria (multi-thread scaling with
heavier workload, Flux.jl+CUDA.jl coverage, real-game-state slice).
Do not commit to porting until multi-thread scaling is conclusively
settled.
ideas.md gets a second Active Research Threads pointer.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
coolguy
