A Pivot Point: Benchmarking Through the Ashes of Phase 10

In the middle of a methodical throughput sweep, a single message captures a moment of quiet progress after a dramatic design failure. The message at index 2674 is deceptively simple — a brief progress report followed by the launch of another benchmark run. But to understand its significance, one must trace the arc of events that led to it: a failed architectural gambit, a painful reversion, and the beginning of a data-driven investigation that would ultimately reshape the entire optimization strategy for the cuzk SNARK proving engine.

The Message

The assistant writes:

c=10 j=10: 38.7s/proof throughput, improving. Now c=15 j=15:

>

``bash FIL_PROOFS_PARAMETER_CACHE=/data/zk/params /home/theuser/curio/extern/cuzk/target/release/cuzk-bench batch --type porep --c1 /data/32gbench/c1.json --count 15 --concurrency 15 2>&1 | tee /home/theuser/cuzk-p9-c15j15.log ``

>

`` [1/15] COMPLETED — 61.0s (prove=45833 ms, queue=270 ms) [2/15] COMPLETED — 100.3s (prove=49424 ms, queue=704 ms) [3/15] COMPLETED — 137.6s (prove=53394 ms, queue=1121 ms) [4/15] COMPLETED — 174.9s (prove=50585 ms, queue=1551 ms) [5/15] COMPLETED — 212.5s (prove=5... ``

At first glance, this reads as a routine benchmark execution. The assistant reports that at concurrency 10 (c=10, j=10), throughput is 38.7 seconds per proof — an improvement over the 41.4 seconds observed at c=5. Then it launches the next data point at concurrency 15. But the real story lies in why this benchmark is happening at all, and what it represents in the broader narrative of the optimization campaign.

The Wreckage of Phase 10

To understand this message, one must understand what came immediately before it. The assistant had spent several messages designing, implementing, and debugging Phase 10 — a two-lock GPU interlock architecture intended to improve throughput by allowing multiple GPU workers to overlap their work. The core idea was to split a single mutex into a mem_mtx (for VRAM allocation and pre-staging) and a compute_mtx (for kernel execution), so that one worker could pre-stage its buffers while another was still computing.

The design was elegant on paper. In practice, it collapsed catastrophically.

The root cause was a fundamental mismatch between the design's assumptions and the hardware reality. The RTX A6000 (or similar 16 GB GPU) simply did not have enough VRAM to accommodate pre-staged buffers from multiple workers simultaneously. Each worker's pre-staging allocated approximately 12 GB, and when a second worker attempted to enter compute_mtx while the first worker's buffers were still live, cudaMalloc failed with an out-of-memory error deep inside the SPPARK kernel pipeline. The assistant's own analysis in [msg 2661] captured the despair: "This is a fundamental design flaw in the two-lock approach: pre-staged VRAM allocated under mem_mtx persists until consumed under compute_mtx, but another worker may enter compute_mtx first and find VRAM exhausted."

Further compounding the problem, CUDA memory management APIs like cudaDeviceSynchronize and cudaMemPoolTrimTo are device-global operations. They cannot be scoped to a single worker's context, which defeated the entire purpose of splitting the lock. The two-lock design implicitly assumed that VRAM operations could be partitioned per-worker — an assumption that CUDA's architecture does not support on a single device.

The assistant's response was decisive. Rather than attempting to patch the flawed design, it reverted to Phase 9's proven single-lock approach with a git checkout command ([msg 2664]), rebuilt the daemon, and pivoted to a completely different strategy: systematic benchmarking to understand the actual bottleneck under realistic workloads.

Why This Message Was Written

The subject message exists at a specific inflection point in that benchmarking campaign. The assistant had already run two data points:

• c=5 j=5: 41.4 seconds per proof (throughput)
• c=10 j=10: 38.7 seconds per proof (throughput) The trend was clear: throughput was improving with higher concurrency. This was a nontrivial finding. If the system were GPU-bound, higher concurrency would not improve throughput — the GPU would saturate and per-proof times would remain constant or even degrade. The fact that throughput improved from 41.4s to 38.7s when doubling concurrency suggested that the bottleneck was not the GPU itself, but something upstream — likely CPU-side contention or memory bandwidth. This observation is what motivated the message. The assistant was not merely running a benchmark; it was probing the system's scaling behavior to identify where the throughput curve would plateau. Each successive concurrency level was a deliberate experiment: does the system scale linearly? Does it saturate? At what point does contention overwhelm the gains from parallelism? The message's brevity belies its purpose. The assistant writes "improving" — a single word that encapsulates a significant insight. If throughput were flat or degrading, the optimization strategy would need to target GPU kernel time reduction. But because throughput was improving, the bottleneck lay elsewhere, and the optimization strategy needed to shift accordingly.

The Thinking Process Visible in the Message

Though the message is short, the thinking process is visible in its structure. The assistant does three things:

1. Reports the previous result ("c=10 j=10: 38.7s/proof throughput, improving") — this is a checkpoint, confirming that the previous benchmark completed and the trend is positive.
2. States the next step ("Now c=15 j=15") — this signals the experimental design: a sweep across concurrency levels to map the throughput curve.
3. Executes the next benchmark — the bash command and its output. The phrase "improving" is particularly telling. It reveals that the assistant is actively interpreting the data as it arrives, not just mechanically running benchmarks. The assistant is looking for the shape of the throughput curve — where it rises, where it plateaus, where it falls. This is a scientist's approach to performance optimization: form a hypothesis, design an experiment, run it, interpret the results, and iterate. The choice of concurrency levels (5, 10, 15, and later 20) is also revealing. These are not arbitrary numbers. The assistant is probing the system's scaling behavior at doubling intervals (5→10) and then at 1.5× (10→15) to find the inflection point. This is classic benchmarking methodology: start low, double until you see diminishing returns, then narrow in on the optimal region.

Assumptions Embedded in the Message

Several assumptions underpin this message, some explicit and some implicit:

The system is CPU-bottlenecked, not GPU-bottlenecked. This is the working hypothesis that the sweep is designed to test. If throughput continues to improve with concurrency, the bottleneck is upstream of the GPU. If it plateaus, the GPU is saturated.

Phase 9's single-lock approach is the correct baseline. The assistant has implicitly accepted that Phase 10's two-lock design is not salvageable for this hardware configuration. The revert to Phase 9 is a statement: "this is our best known configuration, and we need to understand its behavior before attempting further optimizations."

gw=2 is the optimal worker count. The assistant configured gpu_workers_per_device = 2 based on earlier Phase 8/9 findings. This assumption would later be validated by the sweep results.

The benchmark is representative of production workloads. The assistant uses a real C1 output file (/data/32gbench/c1.json) and real parameters, ensuring the results are meaningful.

Input Knowledge Required

To fully understand this message, one needs knowledge of:

• The cuzk proving pipeline architecture: How synthesis, GPU kernels, prep_msm, and b_g2_msm fit together, and how the pipeline processes 10 partitions per proof.
• The Phase 8/9 optimization history: The dual-worker GPU interlock, the TIMELINE analysis, and the PCIe transfer optimization that preceded this work.
• The Phase 10 failure analysis: Why the two-lock design was attempted and why it failed — specifically the VRAM capacity constraint and device-global CUDA API limitations.
• Benchmarking methodology: What c (concurrency) and j (jobs) mean in the cuzk-bench tool, and how throughput is calculated from batch completion times.
• The hardware configuration: A single GPU with 16 GB VRAM, DDR5 system memory, and the implications for memory bandwidth contention.

Output Knowledge Created

This message, combined with the broader sweep, produces several critical insights:

1. Throughput scales with concurrency — at least up to c=10, where it reaches 38.7s/proof. This rules out GPU compute as the primary bottleneck.
2. The throughput curve is not yet flat — the improvement from 41.4s to 38.7s suggests that further gains may be possible at higher concurrency.
3. Phase 9 is a stable, well-understood baseline — the assistant now has a repeatable benchmark methodology and a known configuration that produces consistent results.
4. The optimization target is shifting — from GPU kernel time reduction toward CPU-side contention, memory bandwidth, and thread scheduling. These insights would prove decisive. After completing the sweep through c=20, the assistant would perform a waterfall timing analysis ([msg 2675]) revealing 90.8% GPU utilization at high concurrency, with throughput plateauing at ~38s/proof due to DDR5 memory bandwidth contention. This would lead directly to Phase 11's three-intervention design targeting TLB shootdown storms, thread pool oversubscription, and memory-phase overlap — a fundamentally different optimization strategy than anything attempted in Phases 1-10.

Mistakes and Incorrect Assumptions

The message itself contains no errors — it is a straightforward benchmark execution. However, the broader context reveals an implicit assumption that would later prove incomplete: that the throughput curve would continue to improve or at least plateau gracefully. In reality, the c=15 and c=20 results would show throughput plateauing at ~38s/proof, with individual proof times becoming more variable due to memory bandwidth contention. The assistant's note "improving" at c=10 was correct in isolation, but the improvement was already decelerating — the low-hanging fruit of concurrency scaling was nearly exhausted.

More significantly, the entire benchmarking sweep was a response to the Phase 10 failure, and that failure itself stemmed from an incorrect assumption: that CUDA memory management could be partitioned per-worker on a single GPU. The two-lock design assumed that cudaDeviceSynchronize and cudaMemPoolTrimTo operated on a per-context basis, when in fact they are device-global operations. This is a subtle but critical CUDA programming pitfall that the assistant discovered the hard way.

Conclusion

Message 2674 is a quiet pivot point in a larger optimization campaign. It captures the moment when the assistant, having abandoned a flawed architectural redesign, returns to first principles: measure the system, understand its behavior, and let the data guide the next step. The benchmark sweep that begins here will reveal that the true bottleneck is not GPU compute or lock contention, but DDR5 memory bandwidth — a finding that will redirect the entire optimization effort toward CPU-side interventions like bounding async deallocation, reducing thread pool sizes, and adding lightweight semaphore interlocks.

The message is brief — barely a sentence of analysis followed by a command invocation — but it represents a crucial methodological commitment. In the face of a failed design, the assistant chose not to guess at the next optimization, but to measure. That commitment to data-driven decision-making is what makes this message, for all its brevity, a turning point in the story.