The Moment of Truth: Running the Diagnostic Benchmark After Instrumentation

In the high-stakes world of GPU-accelerated proof generation for Filecoin's PoRep (Proof-of-Replication), memory pressure is the silent killer. The SUPRASEAL_C2 pipeline, a complex chain spanning Go, Rust, C++, and CUDA, had been battling a persistent out-of-memory (OOM) condition at pw=12 (12 concurrent partition workers), with RSS peaking at 668 GiB on a 755 GiB system. After implementing the Phase 12 split GPU proving API, the assistant had made a promising fix—early deallocation of the massive a, b, c NTT evaluation vectors (~12 GiB per partition) in prove_start. But the OOM persisted. The user then asked a pivotal question in [msg 3076]: "Can we count and report number of each large buffer in flight and maybe the stage?"

This question triggered a focused 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, giving real-time visibility into every large buffer class in flight. But instrumentation is only useful if you run the experiment. Message [msg 3102] is that experiment: the moment the assistant fires the benchmark to collect data and finally understand where the memory is going.

The Message

The subject message is a single bash command:

/home/theuser/curio/extern/cuzk/target/release/cuzk-bench batch --type porep \
  --c1 /data/32gbench/c1.json --count 10 --concurrency 10 2>&1

The output shows the benchmark beginning its run:

[2026-02-20T10:51:34.568768Z INFO cuzk_bench] loading C1 output path=/data/32gbench/c1.json
=== Batch Benchmark ===
proof type:  porep
count:       10
concurrency: 10

  [1/10] COMPLETED — 80.0s (prove=81343 ms, queue=282 ms)
  [2/10] COMPLETED — 122.8s (prove=64921 ms, queue=742 ms)
  [3/10] COMPLETED — 166.4s (prove=69662 ms, queue=1189 ms)
  [4/10] COMPLETED — 199.8s (prove=76717 ms, queue=1619 ms)
  [5/10] COMPLETED — 223.9s (prove=8...

On the surface, this looks like a routine benchmark run. But in context, it is anything but routine. This is the first time the assistant is running pw=12 with the new instrumentation, and the results will reveal the true nature of the memory bottleneck that had been frustrating optimization efforts for multiple rounds.

Why This Message Matters

This message sits at a critical inflection point in the debugging process. To understand why, we need to trace the chain of reasoning that led here.

The Phase 12 split API (implemented in [msg 3077] through [msg 3097]) had introduced a structural change: offloading the b_g2_msm computation from the GPU worker's critical path to a background thread. This improved throughput from 38.0s to 37.1s per proof at pw=10, but the real prize was pw=12—more parallelism, higher throughput. Yet every attempt to run pw=12 had ended in OOM, with RSS hitting 668 GiB.

The assistant's initial hypothesis was that the PendingProofHandle—the Rust-side struct holding references to synthesized partition data—was retaining memory unnecessarily after prove_start returned. This led to the early deallocation of a, b, c vectors. But when the OOM persisted (RSS still peaking at ~650 GiB in [msg 3074]), the assistant realized something deeper was wrong.

The user's question about counting buffers in flight was the key insight. Instead of guessing where the memory was going, the assistant could measure it. The global buffer tracker was born: atomic counters for synth (synthesis tasks in flight), provers (ProvingAssignment sets with a/b/c data), aux (aux_assignment buffers), shells (density-only shells), and pending (pending proof handles). Each counter was incremented and decremented at precise points in the pipeline, and a BUFFERS[...] log line was printed at every transition.

But the tracker is useless without data. Message [msg 3102] is the data collection run. The assistant carefully chose --count 10 --concurrency 10—a smaller workload than the --count 20 --concurrency 15 that had previously OOM'd. This was a deliberate safety measure: test whether pw=12 could even complete a modest workload before attempting a full production run. The assistant was probing the system's behavior under controlled conditions.

The Architecture Under Test

To understand what the buffer counters would reveal, we need to understand the pipeline architecture. The proof generation pipeline has three stages connected by a bounded channel:

1. Synthesis (CPU-bound): Partition workers run synthesize_circuits_batch_with_hint to build circuits from vanilla proofs, producing a SynthesizedProof containing ~16 GiB of data per partition (12 GiB for a/b/c evaluation vectors + ~4 GiB for aux assignments).
2. Channel (bounded, capacity=1): A tokio::sync::mpsc::channel with synthesis_lookahead=1 connects synthesis to GPU processing. This is the backpressure mechanism.
3. GPU Prove (GPU-bound): Workers consume synthesized proofs, run prove_start (GPU kernels), then finish_pending_proof (CPU post-processing including b_g2_msm). The partition semaphore (pw=12) limits how many synthesis tasks can run concurrently. But crucially, in the original code, the semaphore permit was released as soon as synthesis completed, before the synthesized job was sent to the GPU channel. This meant a task could finish synthesis, release its permit (allowing another task to start), and then block on synth_tx.send() while holding ~16 GiB of data. With 12 workers all completing around the same time, and the GPU consuming partitions at ~3.5s each, the backlog of synthesized-but-undelivered partitions could grow unchecked.

What the Data Revealed

The benchmark in [msg 3102] completed successfully—no OOM. But the buffer counts told a stark story. In the subsequent analysis ([msg 3105] through [msg 3107]), the assistant examined the logs and found:

• provers=28: 28 ProvingAssignment sets alive simultaneously, each holding ~12 GiB of a/b/c data = ~336 GiB
• aux=97-99: 97-99 aux_assignment buffers alive, each ~4 GiB = ~388 GiB
• synth=12-14: Up to 14 synthesis tasks launched (the semaphore cap was 12, but the counter was incremented before acquisition) The aux counter was never decremented because buf_dealloc_done() was never called from bellperson (a cross-crate visibility issue), so the aux number was an artifact. But the provers=28 number was real and damning. With the channel capacity at 1, only one synthesized proof could be in the channel at a time, and one being GPU-processed. The remaining 26 were blocked on synth_tx.send(), each holding ~16 GiB of data. The smoking gun was clear: the semaphore released too early. The permit was dropped inside the spawn_blocking closure (line 1163 of engine.rs), which meant it was released as soon as synthesis completed, not after the job was delivered to the GPU channel. This allowed up to pw tasks to synthesize concurrently, and then all of them could pile up waiting for the single-slot channel, each holding their full dataset.

Assumptions and Their Consequences

The assistant made several assumptions in this message and the surrounding work:

Assumption 1: The early a/b/c deallocation would be sufficient. The Phase 12 changes freed the NTT evaluation vectors immediately after prove_start returned, but this only saved ~12 GiB per partition. The real problem was the number of partitions in flight, not the per-partition footprint of any single buffer class.

Assumption 2: The channel capacity of 1 would provide adequate backpressure. It did, but only for the channel itself—not for the synthesis tasks that had already completed and were blocking on send(). The semaphore was the actual backpressure mechanism, and it was releasing too early.

Assumption 3: A smaller workload (count=10, concurrency=10) would be safe. This was correct—the benchmark completed without OOM—but it masked the severity of the problem. The buffer counters showed that even with this modest workload, the system was operating at the edge of its memory capacity.

Assumption 4: The buffer counters would reveal the bottleneck. This was correct and was the key insight of the entire debugging session. Without the counters, the assistant would have continued guessing about fragmentation or other hard-to-measure phenomena.

The Thinking Process Visible in This Message

While the message itself is just a command execution, the reasoning behind it is visible in the surrounding context. The assistant had just finished implementing the buffer tracker across multiple files:

• Adding atomic counters in pipeline.rs ([msg 3079])
• Adding hook calls at synthesis completion and GPU pickup points in engine.rs ([msg 3087], [msg 3089], [msg 3092])
• Adding buf_dealloc_done() calls in bellperson's supraseal.rs ([msg 3096]) The build succeeded with only warnings ([msg 3097]). Then the assistant started the daemon with the pw=12 config, waited for it to be ready, and launched the RSS monitor. Message [msg 3102] is the final step in this setup: running the actual workload. The choice of --count 10 --concurrency 10 is deliberate. Earlier benchmarks used --count 20 --concurrency 15 and OOM'd. The assistant is being conservative—enough proofs to generate meaningful buffer data, but not so many that an OOM would crash the daemon before the counters could log useful information.

The Aftermath

The benchmark completed successfully. In the next message ([msg 3103]), the assistant immediately checked the buffer counts and found the smoking gun: provers=28. This led to a redesign of the semaphore holding strategy ([msg 3111] through [msg 3115]), where the permit was moved out of the spawn_blocking closure and held until after synth_tx.send() completed. This fix dramatically reduced peak RSS from 668 GiB to 294.7 GiB ([msg 3122]), finally enabling pw=12 to run without OOM.

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—a classic example of the memory-throughput trade-off in systems engineering.

Conclusion

Message [msg 3102] appears, at first glance, to be a routine benchmark command. But in the context of the debugging narrative, it is the moment of truth—the experiment that finally revealed the true nature of the memory bottleneck. The assistant had instrumented the system with atomic counters, and this benchmark was the data collection run that turned guesswork into measurement.

The lesson is a powerful one about performance debugging: you cannot optimize what you cannot measure. The user's simple question—"Can we count and report number of each large buffer in flight?"—was the catalyst that transformed the debugging process from hypothesis-driven guesswork into data-driven engineering. The buffer counters didn't just reveal the problem; they pointed directly to the fix: hold the semaphore permit until the synthesized job is delivered to the GPU channel.

This message also illustrates the iterative nature of systems optimization. Each round of changes—Phase 12 split API, early a/b/c deallocation, buffer counters, semaphore fix, channel capacity tuning—built on the insights of the previous round. The 37.1s/proof throughput at pw=10 and the eventual stable pw=12 operation were not achieved through a single brilliant insight, but through systematic measurement, hypothesis testing, and incremental refinement. Message [msg 3102] is the hinge point where measurement replaced speculation.