The Great Unwinding: How End-to-End Testing Revealed the True Value of a Pipelined Proving Architecture

Introduction

In any complex systems engineering effort, there comes a moment when months of careful optimization work collides with the messy reality of end-to-end measurement. For the cuzk SNARK proving engine — a GPU-accelerated Groth16 proof generator for Filecoin's Proof-of-Replication (PoRep) — that moment arrived in a single benchmark run that fundamentally reshaped the team's understanding of their own architecture.

This chunk of the opencode session (messages 1776–1813) documents the transition from component-level optimization to system-level validation. The assistant had just committed Phase 6's pipelined partition proving pipeline ([msg 1773]), a sophisticated redesign that broke the monolithic 10-partition PoRep proof into individually synthesized and proven partitions, achieving a 3.2× memory reduction (71 GiB vs 228 GiB peak) with only ~16% latency overhead in in-process benchmarks. The user's directive was clear: verify that all phases are properly wired into the daemon, then run end-to-end benchmarks through the gRPC interface to find the GPU saturation point.

What followed was a cascade of discoveries that redefined the value proposition of the entire Phase 6 effort. The standard pipeline path — the one that had been treated as the baseline to beat — turned out to be dramatically faster than the partitioned path in the daemon context (47.7s vs 72s per proof), because it leveraged an architectural feature the partitioned path inadvertently blocked: inter-proof overlap through the engine's two-stage synthesis→GPU pipeline.

The Context: A Pipeline Built on Strong Foundations

To understand the weight of this discovery, one must appreciate the journey that led to it. The cuzk proving engine had undergone five major phases of optimization before the partitioned pipeline was conceived. Phase 4 delivered synthesis hot-path optimizations achieving a 13.2% end-to-end improvement. Phase 5 introduced the Pre-Compiled Constraint Evaluator (PCE), a multi-threaded constraint evaluation system that dramatically accelerated synthesis. Phase 6 then introduced the slotted pipeline — the subject of this benchmark.

The partitioned pipeline's architecture was elegant. Instead of synthesizing all ten partitions of a PoRep proof into a single batch and submitting them to the GPU at once (which required ~228 GiB of peak memory), the pipeline would synthesize partitions individually using std::thread::scope, stream them through a bounded sync_channel to a GPU consumer thread, and have the GPU prove them one at a time. The channel's capacity provided backpressure: if the GPU fell behind, the synthesis threads would block when the channel filled, preventing unbounded memory growth. The in-process benchmarks had shown 71 GiB peak memory (a 3.2× reduction) with only ~16% latency overhead — numbers compelling enough to warrant full daemon integration.

The User's Challenge: Validate End-to-End

The user's request at [msg 1776] set the stage for the entire chunk: "Use explore agents to see the current state of the actual daemon, make sure all phases (pce, new concurrency/pipeline) are all implemented in it, then run full e2e tests with the daemon on various concurrencies (5/10/20/30/40) to find a threshold where the GPU is fed 100% of the time."

This request contained an implicit challenge: the in-process benchmarks, while promising, might not be representative of real daemon behavior. The daemon introduces gRPC request handling, a scheduler that queues and dispatches proof requests, asynchronous task management via Tokio, and the interaction between the synthesis task loop and the GPU worker pool. The assistant needed to verify that all the new code paths — PCE extraction, partitioned proving, slotted dispatch — were actually reachable through the daemon's production entry point.

The assistant responded by dispatching two parallel exploration subagents ([msg 1778]). The first traced the daemon's call chain from main.rs through the engine's process_batch method, confirming that PCE preloading and partitioned pipeline dispatch were correctly wired. The second explored the cuzk-bench tool's e2e capabilities, identifying the batch subcommand with its -j concurrency parameter as the right tool for the job.

The Discovery That Changed Everything

The exploration revealed a critical architectural constraint that would shape everything that followed. In [msg 1780]–[msg 1782], the assistant read the engine's synthesis task loop and discovered that it processes batches one at a time sequentially. When slot_size > 0, each PoRep proof goes through prove_porep_c2_partitioned, which uses spawn_blocking — meaning it holds the synthesis task thread for the entire duration of the proof (~72 seconds). Concurrent requests from the bench tool (-j N) would simply queue up in the scheduler; they would not execute in parallel.

This realization triggered a profound reinterpretation of the user's request. The user had asked for "concurrencies (5/10/20/30/40)" — but what did "concurrency" mean in this context? The assistant considered two interpretations:

The -j concurrency parameter of the bench tool, controlling how many proof requests are sent simultaneously.
The slot_size parameter of the partitioned pipeline, controlling how many synthesized partitions can be buffered before the GPU consumer. The assistant correctly noted that values 20/30/40 don't make sense for slot_size since PoRep has only 10 partitions — any value above 10 triggers a fallback to batch-all behavior. After wrestling with this ambiguity in [msg 1782], the assistant settled on a pragmatic test plan: vary slot_size across the meaningful range (0, 1, 2, 3, 5, 10) while keeping -j fixed at 2 (sufficient to keep the queue fed) and -c at 3 (three proofs for averaging, with the first serving as warmup).

The Benchmark Results: A Paradigm Shift

The assistant wrote a shell script (/tmp/cuzk-e2e-bench.sh) that would start the daemon with each configuration, wait for it to be ready (loading SRS parameters and PCE data), run the benchmark, kill the daemon, and repeat. After a brief detour debugging a CLI flag mismatch ([msg 1792]–[msg 1794]), the script executed successfully across all six configurations.

The results, analyzed in [msg 1801], were stunning:

| slot_size | Throughput | s/proof | GPU prove avg | Peak RSS | |-----------|-----------|---------|---------------|----------| | 0 (standard) | 1.257 proofs/min | 47.7s | 27.0s | 370.8 GiB | | 1 (partitioned) | 0.833 proofs/min | 72.1s | 37.7s | 264.9 GiB | | 2 (partitioned) | 0.842 proofs/min | 71.3s | 38.0s | 264.8 GiB | | 3 (partitioned) | 0.822 proofs/min | 73.0s | 39.1s | 264.8 GiB | | 5 (partitioned) | 0.841 proofs/min | 71.3s | 38.9s | 261.0 GiB | | 10 (fallback) | 0.838 proofs/min | 71.6s | 37.4s | 264.8 GiB |

The standard pipeline (slot_size=0) was 51% faster than the best partitioned mode — 47.7s per proof versus ~72s. Every single partitioned configuration, regardless of slot_size, clustered tightly around 71-73 seconds per proof. Even slot_size=10, which triggered a fallback to batch-all behavior within the partitioned function, was no better.

This was a complete reversal of expectations. The in-process benchmarks had shown only a 16% gap (62.3s vs 72s), but in the daemon context, the gap widened dramatically. The standard path was not just marginally better — it was decisively superior.

The Root Cause: Inter-Proof Overlap vs. Sequential Blocking

The assistant's analysis in [msg 1801] identified the root cause with surgical precision. The standard engine pipeline works as follows: the synthesis task runs in a spawn_blocking call, produces a SynthesizedJob, and sends it through a synth_tx channel to a GPU worker. While the GPU worker is proving proof N, the synthesis task can immediately start working on proof N+1. This is the synthesis_lookahead mechanism — with lookahead=1, the GPU channel always has at least one pre-synthesized proof waiting, so the GPU never stalls waiting for synthesis to begin.

The partitioned path destroys this overlap. The entire prove_porep_c2_partitioned function — including both the parallel partition synthesis and the sequential GPU partition proving — runs inside a single spawn_blocking call that blocks the synthesis task. The synthesis task cannot start on the next proof until the current proof's GPU work is completely finished. The daemon log analysis confirmed this starkly: proof 2 started only after proof 1 finished at t=70s, proof 3 started at t=141s. Zero overlap between proofs.

This explains why the gap widened from 16% in-process to 51% in the daemon. In the in-process benchmark, there was only one proof — no inter-proof overlap was possible in either path. But in the daemon with multiple proofs, the standard path's ability to pipeline proofs gave it a massive advantage. The partitioned path, by monopolizing the synthesis task, forfeited this advantage entirely.

GPU Utilization: The 57% Ceiling

The assistant's next step was to probe deeper. If the standard path was already winning on throughput, could it be pushed to 100% GPU utilization? A dedicated throughput benchmark ([msg 1807]) varied the -j concurrency parameter (1, 2, 3, 5) while keeping slot_size=0 and synthesis_lookahead=1.

The results, analyzed across [msg 1808]–[msg 1811], revealed a clear ceiling. With -j=1 (sequential proofs), GPU utilization was only 39% — the GPU spent 26s active out of 67s total per proof, with a ~42s idle gap waiting for the next proof to arrive and be synthesized. With -j >= 2, utilization jumped to 57% — the GPU was active 26s out of 46s per proof, with a persistent ~12s idle gap.

The 12s gap was structural, not a configuration issue. Synthesis took ~38s per proof while GPU took ~26s. With synthesis_lookahead=1, the GPU channel could hold at most one pre-synthesized proof. Since synthesis was 12s longer than GPU, the GPU would always finish its work 12s before the next synthesis completed. The channel would be empty at the moment the GPU finished, forcing it to wait.

The assistant computed this precisely in [msg 1811]: "GPU idle gap: ~12s (synth is 12s longer than GPU)." Increasing -j beyond 2 didn't help — the bottleneck was synthesis time, not queue depth. Even with infinite concurrency, throughput was bounded by the slower of synthesis and GPU, which was synthesis at ~38s per proof.

The Synthesis Lookahead Experiment

Could synthesis_lookahead=2 close the gap? The assistant tested this in [msg 1812], restarting the daemon with lookahead=2 and running 5 proofs at -j=3. The theory was straightforward: with two pre-synthesized proofs in the pipeline, the GPU would never have to wait — when it finished proof N, proof N+1 would already be waiting, and proof N+2 would be synthesizing. The steady-state throughput would become max(synth, gpu) = 38s/proof = 1.58 proofs/min, with GPU utilization rising to 26/38 = 68%.

The results confirmed the expected behavior. The 12s gap was partially closed, but not eliminated — synthesis remained the bottleneck. The theoretical maximum throughput of 2.31 proofs/min (one proof per 26s GPU cycle) remained out of reach because synthesis simply couldn't keep up.

Memory: The Partitioned Path's True Value Proposition

The e2e benchmarks also revealed a troubling discrepancy in memory measurements. The in-process benchmarks had shown the partitioned path achieving 71 GiB peak RSS, but the daemon benchmarks showed ~265 GiB for all partitioned modes and 370 GiB for the standard path. The assistant noted in [msg 1801]: "Peak RSS of ~265 GiB for partitioned modes is way too high — the in-process bench showed 71 GiB. Something in the daemon is not releasing memory between proofs, or the -j 2 is causing two proofs' worth of data to be live."

This discrepancy highlighted an important lesson: in-process benchmarks and daemon benchmarks measure different things. The daemon's gRPC layer, scheduler queue, and concurrent request handling all contribute to memory pressure that doesn't appear in isolated in-process tests. The partitioned path's memory advantage — while real — is less dramatic in the daemon context than the in-process numbers suggested.

Nevertheless, the fundamental trade-off remained clear. The standard path offered superior throughput (47.7s vs 72s per proof) at the cost of higher peak memory (370 GiB vs ~265 GiB). For memory-constrained deployments — machines with, say, 256 GiB of RAM — the partitioned path's lower peak memory could be the difference between running and not running at all. For throughput-optimized deployments with abundant memory, the standard path was the clear winner.

The Strategic Reorientation

Perhaps the most important outcome of this chunk was the strategic reorientation it triggered. The partitioned pipeline had been the focus of Phase 6 development, consuming significant engineering effort. The benchmark results could have been demoralizing — a sophisticated optimization that turned out to be a regression in the primary metric (throughput).

Instead, the assistant treated the data as information, not judgment. The partitioned path was not abandoned; its value proposition was clarified. It became a tool for memory-constrained deployments rather than a universal improvement. The standard path was recognized as already having a form of pipeline — just one that operates at a different level.

This reorientation is visible in the assistant's reflection at the end of [msg 1801]: "But actually, there's a simpler insight: the standard path (slot_size=0) already achieves better throughput. The partitioned path's value is memory reduction for memory-constrained machines. For throughput, the standard path wins."

This is the voice of an engineer who has learned something fundamental about the system they are optimizing. The partitioned pipeline was designed to solve a problem (memory) but was evaluated on a metric (throughput) where the existing solution was already superior. The clarification of which metric each path optimizes is the chunk's most lasting contribution.

Conclusion: Lessons from the Great Unwinding

This chunk of the opencode session offers several enduring lessons for systems engineering:

Lesson 1: End-to-end testing reveals what component testing cannot. The in-process benchmarks showed the partitioned path as a clear win (3.2× memory reduction, 16% latency overhead). Only daemon-integrated testing exposed the inter-proof overlap advantage of the standard path, which widened the gap from 16% to 51%. Optimizations must be validated at the system level, not just the component level.

Lesson 2: The architecture you have is often better than the one you're building. The standard engine pipeline, which had been treated as the baseline to beat, already achieved near-optimal GPU utilization through its two-stage synthesis→GPU architecture. The partitioned path's elegant intra-proof pipelining was defeated by the simple fact that it blocked the synthesis task, preventing inter-proof overlap.

Lesson 3: Memory and throughput are different optimization axes. The partitioned path's 3.2× memory reduction remains valuable — just not for throughput. Recognizing which metric each path optimizes is the key to making correct deployment decisions.

Lesson 4: GPU fixed costs are everywhere. The 12s GPU idle gap — caused by synthesis time exceeding GPU time — is a structural bottleneck that no amount of concurrency tuning can eliminate. Further gains would require either reducing synthesis time (already near-minimum with PCE) or running multiple synthesis tasks in parallel — a significant engine architecture change.

In the end, this chunk is not about failure. It is about learning. The partitioned pipeline was not wrong; it was solving a real problem (memory) that the standard path does not address. The insight is that throughput and memory are different axes, and optimizing for one can harm the other. The partitioned path's 3.2× memory reduction remains valuable — just not for throughput. And the standard path's inter-proof overlap, once recognized, becomes a feature to preserve and enhance rather than a baseline to surpass.

The assistant's relentless curiosity — the refusal to accept even a clear finding as final, the willingness to probe deeper into GPU utilization logs, the methodical testing of synthesis_lookahead=2 as a mitigation — is what makes this chunk a model of disciplined systems engineering. The great unwinding of assumptions was not a defeat; it was a deeper understanding of the system's true nature.