The Benchmark That Revealed the Bottleneck Had Shifted

In the long arc of optimizing the SUPRASEAL_C2 Groth16 proof generation pipeline for Filecoin's Proof-of-Replication (PoRep), there are moments when the entire trajectory of the optimization effort pivots on a single measurement. Message [msg 2489] is one such moment. It appears, at first glance, to be a routine benchmark execution — the assistant runs a batch of 15 proofs with 10 concurrent syntheses, collecting throughput data after committing the Phase 9 PCIe transfer optimization. But this message is the fulcrum on which the entire subsequent investigation turns. It is the message where the assistant, following the user's guidance, runs the diagnostic that reveals the bottleneck has moved from the GPU and PCIe bus to a far more subtle constraint: CPU memory bandwidth contention.

The Message Itself

The message is concise. The assistant writes:

Got it — it's batch --type porep --c1 ... -c ... -j .... Let me run the sweep now.

Then it executes a bash command:

echo "=== Phase 9 gw=1 c=15 j=10 ===" && time /home/theuser/curio/extern/cuzk/target/release/cuzk-bench batch \
  --type porep \
  --c1 /data/32gbench/c1.json \
  -c 15 -j 10 2>&1 | tee /tmp/cuzk-p9-c15-j10-bench.log

The output begins to stream in:

=== Batch Benchmark ===
proof type:  porep
count:       15
concurrency: 10

  [1/15] COMPLETED — 79.0s (prove=45008 ms, queue=295 ms)
  [2/15] COMPLETED — 121.0s (prove=37359 ms, queue=781 ms)
  [3/15] COMPLETED — 164.2s (prove=38217 ms, queue=1245 ms)
  [4/15] COMPLETED — 207.7s (prove=36890 ms, queue=1721 ms)
  [5/15...

The message is cut off mid-output, but the pattern is already visible: prove times around 37–45 seconds, queue times growing as the pipeline fills.

The Context That Led Here

To understand why this message matters, we must understand what came before it. The optimization journey had progressed through nine phases. Phase 8 had achieved a dual-worker GPU interlock that improved throughput by 13–17%. Phase 9 then tackled PCIe transfer optimization — pre-staging polynomial uploads using cudaHostRegister and async DMA, double-buffering Pippenger MSM synchronization, and freeing GPU memory earlier. The result was a dramatic 14.2% throughput improvement in single-worker mode, with GPU kernel time per partition dropping from ~3.7s to ~1.5s.

But the user noticed something concerning in the Phase 9 results: "jumpy and inconsistent gpu use." The GPU utilization was no longer smooth. The user hypothesized that the GPU had become so fast that it was starving for work, and suggested running with higher concurrency (15–30 syntheses) and more proofs to let the pipeline stabilize. The assistant committed the Phase 9 code and began preparing the diagnostic benchmarks.

There was a small but telling stumble: the assistant initially tried --c1 as a top-level argument to cuzk-bench, which failed with an "unexpected argument" error. It then discovered the correct syntax — batch --type porep --c1 ... — by reading the help text. This minor correction, visible in the message's opening line, reflects the exploratory nature of working with a newly built toolchain where CLI conventions are still being internalized.

The Reasoning Behind the Benchmark Parameters

The choice of c=15 (15 concurrent proofs) and j=10 (10 syntheses) was deliberate. The user had suggested a range of 15–30 syntheses. The assistant started at the low end of that range to avoid overwhelming the system. With partition_workers=10 (the number of CPU threads dedicated to GPU-side work), the pipeline would have 10 synthesis workers feeding a single GPU worker. The theory was that higher concurrency would keep the synthesis pipeline full, eliminating the gaps where the GPU sat idle waiting for the next partition to be synthesized.

The benchmark also used j=10 — 10 proofs total — which was significantly more than the single-proof runs used in earlier phases. This gave the pipeline time to reach steady state, where the initial burst of synthesis work would spread out and the herding behavior (where all syntheses finish around the same time, creating a wave of partitions followed by a lull) would stabilize.

What the Results Revealed

The output shows prove times of 37–45 seconds per proof. This was worse than the small-run Phase 9 result of 32.1 seconds. The queue times were growing — 295ms for the first proof, 781ms for the second, 1.2s for the third, 1.7s for the fourth — indicating that proofs were piling up in the queue faster than they could be processed.

This was the critical signal. The assistant had expected higher concurrency to improve throughput by keeping the GPU fed. Instead, throughput degraded. Something was fundamentally different about the system's behavior under load.

In the messages immediately following ([msg 2492]), the assistant would analyze the TIMELINE data and discover the truth: GPU utilization was still 90.1%, but the average GPU idle gap was 406ms, with some gaps exceeding 3.8 seconds. The synthesis times averaged 36.2 seconds per partition — nearly identical to the GPU compute time per proof. The bottleneck had shifted from GPU kernel execution and PCIe transfers to the CPU-side operations: prep_msm (1.9s) and b_g2_msm (0.48s) per partition, totaling ~2.4s of CPU work that left the GPU idle for ~600ms per partition.

The Deeper Significance

This message represents the moment when the optimization team realized they had crossed a critical threshold. The GPU was no longer the bottleneck. The PCIe transfers were no longer the bottleneck. The new bottleneck was CPU memory bandwidth contention — the 10 synthesis workers competing with the CPU MSM operations for 8-channel DDR5 memory bandwidth, inflating CPU times by 2–12× under load.

This discovery would directly lead to the design of Phase 10: a two-lock architecture (mem_mtx and compute_mtx) that would allow CPU-side memory management to overlap with GPU kernel execution, hiding the CPU overhead and potentially recovering 30–38% throughput. And when Phase 10 failed due to CUDA device-global synchronization conflicts, it would force an even deeper understanding of the hardware constraints.

Assumptions and Knowledge Required

To fully understand this message, one needs knowledge of: the Groth16 proving pipeline structure (synthesis → partition → GPU kernels), the Filecoin PoRep circuit with its 10 partitions, CUDA memory management (VRAM allocation, pool trimming, device synchronization), PCIe transfer patterns (host-pinned memory, async DMA), and the concept of bottleneck analysis in throughput-optimized systems.

The key assumption being tested was that higher concurrency would improve GPU utilization by smoothing out synthesis herding. This assumption proved partially correct — the GPU was indeed starving for work — but the root cause was not insufficient synthesis throughput but rather CPU-side memory bandwidth saturation that delayed the critical path operations feeding the GPU.

Output Knowledge Created

This message produced the first concrete evidence that the Phase 9 optimization had succeeded in making the GPU so fast that the bottleneck had migrated to the CPU. The benchmark data — prove times, queue times, and the raw TIMELINE events captured in the daemon log — would fuel the next several phases of optimization. It also validated the user's intuition about "jumpy" GPU utilization and confirmed that the optimization strategy needed to shift from GPU-side improvements to CPU-side overlap and memory bandwidth management.

In the broader narrative of this optimization journey, message [msg 2489] is the diagnostic that reoriented the entire effort. It is a reminder that in performance engineering, the most valuable measurements are often the ones that disprove your assumptions.