The Instrumentation That Unlocked the Pipeline

Message 3079 — A Pivot from Guessing to Measuring

In the high-stakes world of GPU-accelerated zero-knowledge proof generation, memory is the invisible governor. The SUPRASEAL_C2 pipeline, part of Filecoin's PoRep (Proof-of-Replication) system, routinely consumes hundreds of gigabytes of RAM as it synthesizes circuits, transfers evaluation vectors to GPU memory, and computes Groth16 proofs across multiple partitions. When the Phase 12 split proving API was benchmarked with pw=12 (12 concurrent partition workers), the daemon was killed by the OOM killer at a peak RSS of 668 GiB — dangerously close to the 755 GiB system ceiling. The team had a mystery on their hands, and the solution would come not from a clever optimization, but from a humble instrumentation tool.

The Context: A Series of Near-Misses

The Phase 12 optimization (see [msg 3054]) had introduced a split GPU proving API that offloaded the b_g2_msm computation into a background thread, allowing GPU workers to immediately pick up new partitions instead of waiting for finalization. This yielded a 2.4% throughput improvement at pw=10 (37.1s/proof), but scaling to pw=12 caused catastrophic memory pressure. The assistant's first diagnostic step was to check whether the PendingProofHandle — the Rust-side struct that holds synthesized partition data until GPU finalization completes — was holding memory unnecessarily after the C++ prove_start function returned.

By tracing the C++ prep_msm_thread ([msg 3057]), the assistant confirmed that the massive a, b, c NTT evaluation vectors (~12 GiB per partition) were only needed by the GPU kernel region, which completed before the function returned. An early deallocation was implemented ([msg 3064]) to free these vectors immediately in prove_start. But when benchmarked ([msg 3073]), the peak RSS only dropped from 668 GiB to ~650 GiB — a savings of roughly 18 GiB, far short of what was needed. The OOM killer remained unimpressed.

The User's Prompt: A Call for Visibility

At this point, the assistant was considering abandoning the pw=12 pursuit entirely ([msg 3075]): "This isn't a Phase 12 problem per se. Let me kill the daemon and try something different." The user intervened with a crucial question ([msg 3076]):

Can we count and report number of each large buffer in flight and maybe the stage?

This was the turning point. Instead of guessing about memory pressure or chasing increasingly speculative optimizations, the user recognized that the team needed data — real-time visibility into exactly how many large buffers were alive at each stage of the pipeline. The assistant's response in [msg 3077] laid out a plan: track five buffer classes — ProvingAssignment a/b/c (~12 GiB each), aux_assignments (~4 GiB each), input_assignments (tiny), C++ split_vectors/tails, and C1 parsing buffers — using atomic counters.

The Subject Message: A Decision Made Concrete

Message [msg 3079] is the moment this plan became code:

[assistant] I'll add a global buffer tracker with atomic counters, and a log_buffers() helper that prints the current state. Let me put it right after the imports: [edit] /home/theuser/curio/extern/cuzk/cuzk-core/src/pipeline.rs Edit applied successfully.

The message is deceptively brief — just two sentences and an edit confirmation. But it represents a significant methodological pivot. The assistant had been operating in a "fix-and-test" mode: hypothesize a memory leak, apply a fix, benchmark, observe the RSS peak, repeat. The user's suggestion shifted the approach to "instrument-and-diagnose": build visibility into the system, run the benchmark, and read the counters to understand where memory was actually going.

The choice to place the buffer tracker "right after the imports" in pipeline.rs was deliberate. The pipeline module (cuzk-core/src/pipeline.rs) is the central orchestrator of the synthesis-to-GPU workflow — it defines the SynthesizedProof struct, manages the channel between CPU synthesis workers and GPU proving workers, and coordinates the lifecycle of every large buffer in the system. By placing the atomic counters and the log_buffers() helper here, the assistant ensured that every code path that creates or destroys a large buffer could be instrumented with a simple function call.

The Design of the Buffer Tracker

The buffer tracker, as elaborated in subsequent messages ([msg 3080], [msg 3087], [msg 3089], [msg 3092]), used six atomic counters:

• buf_synth_start() — incremented when a synthesis task begins
• buf_synth_done() — incremented when synthesis completes (producing a SynthesizedProof)
• buf_abc_freed() — incremented after prove_start frees the a/b/c evaluation vectors
• buf_finalize_done() — incremented after finish_pending_proof completes
• buf_dealloc_done() — incremented when the Rust deallocation thread finishes cleaning up
• log_buffers() — prints the current state of all counters with an estimated GiB total The counters were designed to track the flow of buffers through the pipeline, not just the instantaneous count. By logging at every transition point, the team could reconstruct exactly how many partitions were in synthesis, how many were queued waiting for the GPU, how many were being processed by the GPU, and how many were in finalization. The estimated GiB total multiplied each counter by the known per-buffer size (12 GiB for provers, 4 GiB for aux, negligible for shells), providing a real-time memory pressure gauge.

What the Instrumentation Revealed

When the instrumented daemon was run with pw=12 and j=10 (10 concurrent proofs, 10 partitions each), the buffer logs told a stark story ([msg 3105], [msg 3106]):

• provers=28: 28 ProvingAssignment sets were alive simultaneously, each holding ~12 GiB of a/b/c vectors = ~336 GiB
• aux=97-100: 97-100 aux_assignment buffers were alive, each ~4 GiB = ~388 GiB
• shells=69-72: Density-only shells (tiny bitvectors) were accumulating The provers counter peaking at 28 was the key finding. With pw=12, only 12 synthesis workers should have been active at once. But the synthesis→GPU channel had a lookahead capacity of 1 (the default), and synthesis tasks were spawned independently via tokio::spawn. The partition semaphore (pw=12) released its permit immediately after synthesis completed, allowing the next task to start synthesizing while the completed partition sat in the channel waiting for the GPU. Since the GPU took ~3.5s per partition and synthesis took ~5s, the queue grew faster than the GPU could drain it — reaching 28 queued partitions at peak. The aux counter reaching 100 was partly a counter bug (the buf_dealloc_done() hook was never called from bellperson's deallocation thread because bellperson is a separate crate without access to the pipeline module's counters), but the actual memory was being freed — the RSS returned to 71 GiB after completion. The provers=28 peak, however, was entirely real and represented ~336 GiB of a/b/c data sitting in the queue.

The Downstream Impact

The buffer tracker data directly drove the next phase of optimization. The assistant identified that the root cause was the partition semaphore releasing too early — it freed the permit as soon as synthesis finished, allowing more tasks to pile up while blocking on the single-slot GPU channel. Two fixes were attempted:

1. Hold the semaphore permit until the job is delivered to the GPU channel — this reduced peak RSS from 668 GiB to 294.7 GiB and enabled pw=12 to run without OOM for the first time, but introduced a throughput regression (39.9s vs 37.1s) by serializing synthesis and channel delivery.
2. Increase the channel capacity from 1 to partition_workers — this allowed a natural buffer of completed jobs without blocking the semaphore, balancing memory pressure and throughput. These fixes, documented in subsequent messages ([msg 3115] onward), were only possible because the buffer tracker had revealed the true bottleneck. Without the instrumentation, the team might have continued chasing phantom leaks or prematurely abandoned the pw=12 configuration.

Lessons in Debugging Methodology

Message [msg 3079] exemplifies a critical principle in systems debugging: when you can't explain a performance or memory problem, stop optimizing and start measuring. The assistant's initial approach — hypothesize a cause, apply a fix, benchmark — was rational but inefficient when the system's behavior was poorly understood. The buffer tracker transformed the problem from "why is memory usage so high?" to "why are 28 partitions queued?" — a question with a clear, actionable answer.

The message also highlights the value of user collaboration in AI-assisted development. The assistant, operating autonomously, was ready to abandon the pw=12 pursuit. The user's simple question — "Can we count and report number of each large buffer in flight?" — redirected the effort toward instrumentation, ultimately yielding a deeper understanding of the pipeline's memory dynamics and a more effective fix.

Conclusion

Message [msg 3079] is, on its surface, a mundane edit: adding atomic counters and a logging helper to a Rust module. But in the context of the SUPRASEAL_C2 optimization effort, it represents a methodological turning point — the moment the team stopped guessing and started measuring. The buffer tracker it introduced revealed that the OOM killer was not the result of a memory leak or an oversized data structure, but of a scheduling mismatch between the CPU synthesis pipeline and the GPU consumption rate. This diagnosis, invisible without instrumentation, directly enabled the fixes that followed and deepened the team's understanding of their own system. Sometimes the most powerful optimization tool is not a faster algorithm or a leaner data structure — it's a well-placed counter.