perf: cuBLAS SGEMM pipeline + dead code elimination — 37s → 86ms/epoch (430x)
Phase 2: Replace 1-warp/sample fused kernels with cuBLAS SGEMM batched forward/backward.
- batched_forward.rs: cuBLAS SGEMM forward (10 GEMM + bias/ReLU per pass)
- batched_backward.rs: cuBLAS SGEMM backward (chain rule via GEMM, no atomicAdd)
- c51_loss_kernel.cu: standalone C51 distributional loss (256 threads, 2KB shmem)
- c51_grad_kernel: dL/d_logits with dueling routing for cuBLAS backward
- BF16 alignment fix: pad offsets to even for short2 vectorized loads
- Training step: 10.7ms → 0.7ms (15x) on RTX 3050
Phase 3: Unified cuBLAS Q-forward + dead code elimination (-4,400 lines net).
- Rewrite experience collector: timestep loop + cuBLAS replaces monolithic 3,272-line kernel
- Delete dqn_training_kernel.cu (1,385 lines) — replaced by dqn_utility_kernels.cu (118 lines)
- Delete dqn_experience_kernel.cu (3,272 lines) — replaced by experience_kernels.cu (656 lines)
- Remove BF16 warp-matvec helpers from common_device_functions.cuh (-159 lines)
- Remove dead methods/fields from GpuDqnTrainer (-500 lines)
- Experience collection: 348ms → 12ms (29x) on RTX 3050
- No fallback paths — cuBLAS is the only Q-forward implementation
- All 1,514 tests pass, GPU smoke test verified with real data
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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