Three optimizations targeting GPU allocation churn and lock contention:
1. In-place polyak update: Var::set() reuses existing GPU buffer instead
of Var::from_tensor() which allocates a new one per call. Eliminates
~10,000 cudaMalloc/cudaFree per epoch (20 params × 500 steps).
2. Fused affine ops: Replace 13 Tensor::full()/Tensor::ones() constant
tensor allocations per step with tensor.affine(mul, add) — a single
fused kernel. Patterns: 1-x → x.affine(-1,1), γ*x → x.affine(γ,0),
0.5*x² → (x*x).affine(0.5,0). Applied across all 4 loss paths
(branching Bellman/Huber, standard Bellman/Huber, IQN). Eliminates
~6,500 GPU allocs/epoch.
3. Batch pre-sampling (K=8): Sample 8 batches under one READ lock, train
all 8 under one WRITE lock. Reduces async lock acquisitions from
2×N to 2×ceil(N/8). Priority staleness across 8 steps is negligible.
Combined estimated impact: 20-35% H100 throughput improvement.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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