Chunk 26.0

In this chunk, the assistant implemented **Phase 9: PCIe Transfer Optimization** for the cuzk SNARK proving engine, targeting two root causes of GPU idle gaps identified in the Phase 8 baseline. **Change 1 (Tier 1)** moved the 6 GiB of non-pinned a/b/c polynomial uploads out of the GPU mutex by pinning host memory with `cudaHostRegister`, allocating device buffers, and issuing async `cudaMemcpyAsync` transfers on a dedicated stream with event-based synchronization. **Change 2 (Tier 3)** eliminated per-batch hard sync stalls in the Pippenger MSM by introducing double-buffered host result buffers (`res_buf[2]`, `ones_buf[2]`) and deferring the `sync()` call to the next iteration, allowing GPU compute to overlap with DtoH transfers. Initial testing revealed OOM failures caused by two issues: (1) with `gpu_workers_per_device=2`, both workers tried to pre-stage simultaneously, allocating 12 GiB each and exceeding the 16 GiB VRAM; (2) CUDA's `cudaMallocAsync`/`cudaFreeAsync` memory pools did not release freed memory back to the synchronous `cudaMalloc` pool, causing subsequent allocations to fail even after cleanup. The assistant fixed both by moving the pre‑staging allocation inside the GPU mutex (serializing it across workers), freeing `d_bc` immediately after the NTT phase (before the mutex release), and adding a memory‑aware allocator that queries `cudaMemGetInfo`, subtracts a 512 MiB safety margin, and falls back if insufficient VRAM is available. Benchmark results with `gpu_workers_per_device=1` showed dramatic improvements: NTT+MSM time dropped from ~2430 ms to ~690 ms (**‑71.6%**), tail MSM from ~125 ms to ~82 ms (**‑34.4%**), and overall GPU time per partition from ~3746 ms to ~1450–1900 ms (**‑50–61%**). Throughput improved from 37.4 s/proof to ~32.1 s/proof (**+14.2%**). The assistant then started the full production benchmark with `gpu_workers_per_device=2` and concurrency=3 to validate the gains under the intended dual‑worker configuration.

Phase 9 PCIe Transfer Optimization: A Deep Dive into GPU Memory Management, CUDA Pool Asymmetry, and the 14.2% Breakthrough 2966 words

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