The 28 Provers: How a Single Grep Revealed the Root Cause of 732 GiB Memory Pressure

The Message

In a high-stakes optimization session for the SUPRASEAL_C2 Groth16 proof generation pipeline — part of Filecoin's Proof-of-Replication (PoRep) system — the assistant executed what appears at first glance to be a trivial data retrieval command:

grep "est=732\|est=727\|est=724" /home/theuser/cuzk-p12-buffers.log | head -5
BUFFERS[synth_done]: synth=3 provers=28 aux=97 shells=69 pending=0 est=727GiB
BUFFERS[synth_done]: synth=2 provers=28 aux=98 shells=70 pending=0 est=732GiB
BUFFERS[synth_done]: synth=1 provers=27 aux=99 shells=72 pending=0 est=724GiB

This is message <msg id=3105> in the conversation. It is a deceptively simple act — a grep for specific estimated memory values — but it represents the climax of a multi-hour debugging effort. Those three lines of log output contain the key to understanding why a 755 GiB machine was running out of memory during GPU proof generation, and they point directly to the fix that would eventually reduce peak RSS from 668 GiB to 294.7 GiB.

The Context: Phase 12 and the Memory Wall

The assistant had been working for many rounds on Phase 12 of a GPU proving optimization pipeline. The system under development was a CUDA-accelerated Groth16 prover for Filecoin, designed to handle 32 GiB sectors. The pipeline had two main phases: synthesis (CPU-bound circuit building, consuming ~13 GiB per partition) and GPU proving (CUDA kernel execution). A key architectural change in Phase 12 was the "split API" — decoupling the GPU worker's critical path from CPU post-processing by offloading the b_g2_msm computation to a background thread.

The system had 755 GiB of RAM, but the team was hitting Out-of-Memory (OOM) failures when attempting to increase synthesis parallelism beyond pw=10. At pw=12 (12 concurrent partition synthesis workers), RSS was peaking at 668 GiB — dangerously close to the system limit. The assistant had tried an early deallocation optimization (freeing the massive a, b, c NTT evaluation vectors — ~12 GiB per partition — immediately after prove_start returned), but this only saved ~18 GiB, barely making a dent.

The Instrumentation: Building Visibility

The user's suggestion at <msg id=3076> — "Can we count and report number of each large buffer in flight and maybe the stage?" — triggered a critical instrumentation effort. The assistant built a global buffer tracker with atomic counters (buf_synth_start, buf_abc_freed, buf_dealloc_done) and integrated it into the pipeline and engine modules. This gave real-time visibility into every large buffer class in flight:

The Discovery: 28 Provers

The grep command in <msg id=3105> targets the three highest est values from the buffer log — 727 GiB, 732 GiB, and 724 GiB. These represent the peak memory pressure points during a benchmark run with pw=12. The output reveals a stunning anomaly:

provers=28 — twenty-eight ProvingAssignment sets are alive simultaneously.

This is the smoking gun. With partition_workers=12 (pw=12), the theoretical maximum for in-flight provers should be 12. Each ProvingAssignment holds the a/b/c NTT evaluation vectors (~12 GiB) plus density trackers and witness assignments. Twenty-eight provers means ~336 GiB just for these structures — nearly half of the 732 GiB total estimate.

But how could there be 28 provers when only 12 synthesis workers are allowed? The answer lies in the pipeline architecture. The partition semaphore (set to pw=12) was designed to limit concurrent synthesis. However, the semaphore permit was being released immediately after synthesis completed, not after the synthesized job was consumed by the GPU worker. This meant that synthesis tasks could complete, release their permit, and allow a new synthesis to start — while the synthesized data (the ProvingAssignment) sat in the single-slot GPU channel waiting to be processed.

The GPU channel had a capacity of 1. With two GPU workers (gw=2), at most 2 partitions could be in the GPU pipeline at once. But the synthesis side could churn through partitions at a much higher rate, queuing up completed ProvingAssignments in the channel buffer. Since the semaphore released early, there was no limit on how many completed partitions could pile up waiting for the GPU.

The aux=97-99 and shells=69-72 numbers tell a similar story. The aux_assignments (~4 GiB each) and C++ shell structures were also accumulating because they weren't being consumed fast enough. The GPU worker was the bottleneck, and the synthesis side was overproducing.

The Assumptions That Were Wrong

This discovery exposed several incorrect assumptions:

  1. "The semaphore limits memory": The team assumed that setting pw=12 would cap memory usage by limiting concurrent synthesis. But the semaphore only limited the rate of synthesis starts, not the lifetime of synthesized data. Completed partitions could accumulate indefinitely.
  2. "Early a/b/c deallocation would solve it": The early free of NTT evaluation vectors in prove_start was expected to save ~12 GiB per partition. But with 28 provers in flight, even after freeing a/b/c, each ProvingAssignment still held aux_assignments (~4 GiB) and other structures. The early free helped at the margin but couldn't address the fundamental accumulation problem.
  3. "Memory estimates from first principles are reliable": Earlier estimates predicted ~223 GiB for pw=10 based on theoretical calculations. Actual RSS was 367 GiB. The discrepancy was attributed to "glibc fragmentation," but the real culprit was buffer accumulation that the theoretical model didn't account for.
  4. "The GPU channel capacity of 1 is sufficient backpressure": The single-slot GPU channel was intended to serialize GPU work, but it was never designed to act as a memory throttle. The synthesis side could outrun the GPU by a factor of 10x or more, creating a massive buffer backlog.

The Fix: Holding the Semaphore

The insight from <msg id=3105> directly led to the solution. The assistant realized that the semaphore permit must be held until the synthesized job is fully delivered to the GPU channel, not released immediately after synthesis. This would create a true end-to-end limit: at most pw partitions could be in any stage of the pipeline (synthesis + waiting for GPU) at once.

The fix was implemented by moving the semaphore release from the synthesis completion point to the channel delivery point. This immediately reduced peak RSS from 668 GiB to 294.7 GiB and allowed pw=12 to run without OOM for the first time.

However, this fix introduced a throughput regression (39.9s vs 37.1s) by serializing synthesis and channel delivery. The assistant then iterated further, increasing the channel capacity from 1 to partition_workers to allow a natural buffer of completed jobs without blocking the semaphore. This restored throughput while keeping memory under control.

The Broader Lesson: Instrumentation-Driven Debugging

What makes <msg id=3105> significant is not the command itself but what it represents: the moment when instrumentation turned guesswork into certainty. For dozens of prior messages, the team had been reasoning about memory based on RSS curves, theoretical calculations, and speculation about "glibc fragmentation." The buffer counters transformed the problem from a black box into a transparent system where every buffer class was visible.

The three log lines in this message tell a complete story. synth=3 means only 3 syntheses are currently active — the semaphore is doing its job. But provers=28 means 25 completed partitions are sitting in the pipeline, fully allocated, waiting for the GPU. The est=732GiB is not a mystery — it's the sum of 28 provers × ~12 GiB, 98 aux × ~4 GiB, 70 shells, plus the SRS and runtime overhead.

This is the power of targeted instrumentation. A single grep, run at the right moment, can reveal a root cause that hours of theoretical analysis could not. The message is a testament to the principle that in complex systems, you cannot optimize what you cannot measure — and that the right measurement, at the right granularity, is worth more than a thousand educated guesses.

Conclusion

Message <msg id=3105> is a turning point in the Phase 12 optimization effort. It is a short message — a single bash command and its output — but it carries the weight of a multi-hour debugging journey. The three lines of buffer counters it reveals are the key that unlocked the memory pressure mystery, leading directly to a structural fix that cut peak memory by more than half. In the broader narrative of the SUPRASEAL_C2 optimization, this message represents the transition from blind optimization to data-driven engineering — a moment when the system's behavior became legible, and the path forward became clear.