The Plateau at 43.5 Seconds: A Pivotal Diagnostic Turn in GPU Proving Optimization

The Message

Good, I have a clear picture. The daemon isn't running, the code is at commit 2fac031f, and the last sweep showed pw=10-12 as optimal at ~43.5s/proof. The next high-value experiments are:

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1. Control test: gpu_workers_per_device=1 at pw=10 to quantify Phase 8's exact contribution 2. synthesis_concurrency=2 sweep to see if overlapping synthesis eliminates cross-sector stalls 3. Investigate the ~43.5s plateau with TIMELINE analysis from pw=10

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Let me plan and execute these.

Context: The Optimization Journey So Far

To understand this message, one must appreciate the arc of optimization work that preceded it. The assistant and user had been engaged in a multi-phase campaign to maximize GPU utilization in a Filecoin SNARK proving pipeline called cuzk — a custom proving daemon for Curio, a Filecoin storage mining node. The pipeline generates Groth16 proofs for Proof-of-Replication (PoRep), a computationally intensive cryptographic operation that consumes roughly 200 GiB of peak memory and involves a complex chain from Go orchestration through Rust FFI into C++/CUDA kernels.

The work had progressed through several phases. Phase 6 introduced a slotted partition pipeline. Phase 7 implemented per-partition dispatch, allowing the engine to route individual partitions of a proof to GPU workers independently. Phase 8, just committed as 2fac031f, introduced a dual-worker GPU interlock: two GPU workers per device share a C++ mutex that guards only the CUDA kernel region, while CPU preprocessing runs outside the lock. This design allows one worker to prepare CPU work while the other occupies the GPU, theoretically eliminating GPU idle gaps.

The Phase 8 implementation had been benchmarked with a systematic sweep of partition_workers (pw) values from 10 to 20, using a batch of 5 proofs at concurrency 3 (c=5 j=3). The results were revealing — and puzzling. The sweep showed a clear plateau: pw=10 and pw=12 both achieved 43.5 seconds per proof, pw=15 regressed slightly to 44.8s, pw=18 came back to 43.8s, and pw=20 landed at 44.9s. The variation was minimal, clustering tightly around 43.5–44.9 seconds regardless of how many partition workers were deployed.

This plateau was the central mystery. If adding more partition workers didn't improve throughput, the bottleneck had shifted. The question was: to what?

Why This Message Was Written

The subject message (index 2301) represents a deliberate diagnostic pivot. The assistant had just completed the partition_workers sweep and was facing an ambiguous result. The plateau could mean any of several things: that the GPU proving time itself was the hard floor (i.e., the GPU was already saturated), that cross-sector synthesis stalls were creating idle gaps between proofs, or that some other structural bottleneck had been reached. The assistant needed to design experiments that would disambiguate these possibilities.

The message is written in response to the user's prompt at <msg id=2297>: "Continue if you have next steps, or stop and ask for clarification if you are unsure how to proceed." This is a critical framing — the user explicitly gave the assistant permission to either proceed autonomously or ask for guidance. The assistant chose to proceed, but it did so with a carefully structured plan that shows diagnostic sophistication.

The message opens with "Good, I have a clear picture" — a statement that signals confidence after gathering information. In the preceding messages, the assistant had checked the git log (confirming the code was at 2fac031f), verified the benchmark log files existed, confirmed the daemon wasn't running, and inspected the config. This reconnaissance established that the system was in a clean, known state ready for the next experiments.

The Three Experiments: A Diagnostic Strategy

The three proposed experiments form a coherent diagnostic strategy, each targeting a different potential cause of the plateau.

Experiment 1: Control test with gpu_workers_per_device=1 at pw=10. This is a classic scientific control. Phase 8 introduced two GPU workers per device, but the assistant had never measured the system with a single worker at the optimal pw setting. The earlier Phase 7 vs Phase 8 comparison used pw=20, not pw=10. If the single-worker configuration also achieves ~43.5s/proof, then the dual-worker interlock is not the source of the throughput — the bottleneck lies elsewhere. If it regresses significantly, then Phase 8's interleaving is essential to reaching the plateau, and further optimization should focus on the interlock mechanism itself.

Experiment 2: Sweep with synthesis_concurrency=2. The synthesis phase — where the circuit's constraint system is evaluated to produce the a/b/c polynomials — runs as CPU work that feeds the GPU. If synthesis is the bottleneck, then running two synthesis tasks concurrently (sc=2) could overlap the CPU work of one proof with the GPU work of another, reducing cross-sector stalls. The assistant's hypothesis was that cross-sector synthesis stalls might be the hidden cause of the plateau.

Experiment 3: TIMELINE analysis of the pw=10 run. This is the most diagnostic experiment. The daemon's log contained TIMELINE entries — instrumentation events recording when each phase of proving started and ended. By extracting and analyzing these timestamps, the assistant could construct a precise waterfall diagram showing exactly where time was spent: how long each partition took in synthesis, how long it waited for a GPU worker, how long the GPU kernel ran, and whether there were gaps between consecutive proofs. This would reveal the bottleneck definitively.

Assumptions Embedded in the Plan

The assistant's plan rests on several assumptions worth examining. First, it assumes that the pw=10 run's log file (/tmp/cuzk-sweep-pw10.log) contains sufficient TIMELINE data to perform a meaningful analysis. The assistant had glimpsed the tail of this file in <msg id=2300> and seen TIMELINE,246807,GPU_STAR... entries, confirming the instrumentation was present, but the completeness and interpretability of that data was unverified.

Second, the assistant assumes that synthesis_concurrency=2 is a configurable parameter that the daemon respects. This is a reasonable assumption given the config structure, but the actual behavior depends on whether the synthesis thread pool can handle concurrent sector synthesis without resource starvation on a 96-core CPU.

Third, the assistant assumes that the plateau at ~43.5s is a genuine bottleneck worth investigating rather than a statistical artifact of the benchmark methodology. The benchmark runs 5 proofs at concurrency 3, meaning up to 3 proofs are in-flight simultaneously. The "throughput" metric of 43.5s/proof is derived from total wall time divided by 5. If the benchmark had higher variance or insufficient warmup, the plateau might be less meaningful than it appears.

Fourth, and most subtly, the assistant assumes that the bottleneck is on the critical path of a single proof rather than in the orchestration layer. The plateau could theoretically arise from the gRPC server's request handling, the benchmark tool's pacing, or even Linux kernel scheduling on the 96-core machine. The TIMELINE analysis would reveal this, but the assumption going in is that the bottleneck is in the proving pipeline itself.

Input Knowledge Required

To fully understand this message, one needs substantial context about the cuzk system. The reader must know what partition_workers controls (the number of concurrent partition synthesis tasks), what gpu_workers_per_device does (the number of GPU worker threads per GPU, introduced in Phase 8), and what synthesis_concurrency would do (overlapping synthesis of multiple sectors). One must understand the distinction between the "standard" proof path (where all 10 partitions of a proof are synthesized and proved together) and the "partitioned" path (where partitions are dispatched individually to GPU workers).

The reader also needs to know the hardware context: an AMD Ryzen Threadripper PRO 7995WX with 96 Zen4 cores and 754 GiB RAM, paired with an RTX 5070 Ti (Blackwell architecture, 16 GB VRAM). The CPU is extraordinarily powerful — 96 cores means CPU contention is unlikely but possible if the work is poorly parallelized. The GPU is modern but has limited VRAM, which constrains batch sizes and kernel configurations.

The Phase 8 architecture is essential context: the C++ mutex that guards only the CUDA kernel region, the create_gpu_mutex/destroy_gpu_mutex FFI helpers, the SendableGpuMutex wrapper that lets a raw pointer cross async boundaries, and the fallback to a function-local static std::mutex when the pointer is null. Without understanding this design, the control experiment (gpu_workers_per_device=1) seems trivial — but it's actually probing whether the dual-worker interleaving is masking or causing the plateau.

The Thinking Process Visible in the Message

The message reveals a structured analytical mind at work. The assistant doesn't just list experiments; it prioritizes them and explains the rationale for each. The phrase "quantify Phase 8's exact contribution" shows an understanding that the 13-17% improvement measured at pw=20 might not hold at the optimal pw=10 — the interaction between partition_workers and gpu_workers_per_device needed to be disentangled.

The choice of pw=10 for the control experiment is deliberate. The sweep showed pw=10 and pw=12 both at 43.5s, so pw=10 is the lower bound of the optimal range. If the single-worker control at pw=10 also achieves 43.5s, it would prove that Phase 8's dual-worker design is irrelevant at the optimal pw — a significant finding that would redirect optimization effort toward other bottlenecks.

The TIMELINE analysis proposal shows the assistant thinking like a systems performance engineer. Rather than guessing at the bottleneck, it plans to instrument the actual execution trace. The TIMELINE entries in the log record precise timestamps for synthesis start/end, GPU worker pickup, GPU kernel execution, and proof completion. By reconstructing the timeline across 5 proofs and 10 partitions each, the assistant could identify exactly where idle gaps occur and whether they're caused by synthesis stalls, GPU contention, or cross-sector synchronization.

Output Knowledge Created

This message creates a concrete action plan. It transforms the ambiguous plateau into three testable hypotheses, each with a clear experimental protocol. The todowrite structure formalizes this as trackable work items with priority levels.

More importantly, the message creates a diagnostic framework for understanding the system's behavior. Before this message, the team knew that pw=10-12 was optimal but didn't know why. After executing these experiments, they would know whether the bottleneck is GPU-bound (TIMELINE shows no idle gaps), synthesis-bound (sc=2 improves throughput), or Phase 8-bound (single-worker control matches dual-worker performance).

The message also implicitly creates knowledge about what not to do. By prioritizing these three experiments, the assistant deprioritizes other possible investigations — sweeping j (concurrency) values, testing different GPU worker counts, or reducing the default partition_workers. These are deferred, not dismissed, but the message establishes a clear order of operations.

What Followed

The subsequent messages (in the same segment, chunk 0) reveal that the assistant executed this plan. The TIMELINE analysis showed that the system was perfectly GPU-bound — the measured 37.4s/proof throughput exactly matched the serial CUDA kernel time of 10 partitions × 3.75s. Cross-sector GPU transitions after warmup were under 50ms, and synthesis was fully overlapped with GPU work. This meant further CPU-side optimizations (like synthesis_concurrency=2) were unnecessary. The bottleneck was the GPU kernel time itself.

This finding then led to a deeper investigation: the assistant observed GPU utilization and power dips correlating with ~50 GB/s PCIe traffic, which prompted a detailed inventory of all 23.6 GiB of host-to-device transfers per partition. Two root causes were identified — non-pinned host memory for a/b/c polynomials causing half-bandwidth transfers through CUDA's bounce buffer, and per-batch hard sync stalls in the Pippenger MSM where the GPU idles while the CPU processes bucket results. These became the targets of a two-tier mitigation plan documented in c2-optimization-proposal-9.md.

The subject message, then, stands as the critical juncture where the optimization effort shifted from sweeping configuration parameters to deep systems analysis. It is the moment the assistant stopped treating the system as a black box and started instrumenting it to find the true bottleneck — a textbook example of performance engineering methodology.