The Instrumentation That Unlocked a Memory Mystery

In the high-stakes world of GPU-accelerated proof generation for Filecoin's PoRep protocol, memory is the invisible enemy. A single Groth16 proof for a 32 GiB sector demands hundreds of gigabytes of working memory, spread across CPU synthesis buffers, GPU kernel regions, and C++/CUDA runtime structures. When the Phase 12 split GPU proving API delivered a promising 37.1 seconds per proof at pw=10 (partition workers = 10), the natural next step was to push to pw=12 for higher throughput. Instead, the system crashed with out-of-memory (OOM) errors, peaking at 668 GiB RSS on a 755 GiB machine. The assistant had already tried freeing the massive NTT evaluation vectors (a, b, c) early — a ~12 GiB per-partition saving — but the OOM persisted. Something else was consuming memory at a terrifying rate.

Then came the message that changed everything.

The Message: A Plan to See the Invisible

In message 3078, the assistant wrote:

I'll add global atomic counters for each buffer class and print a summary line every time a key event happens (synthesis complete, prove_start, prove_finish, dealloc done). Let me add the counters in the pipeline module where we have visibility into all stages:

This is followed by a read of the pipeline module source file. On the surface, it's a straightforward instrumentation task. But in context, this message represents a fundamental shift in debugging strategy — from reasoning about where memory might be going to measuring where it actually goes.

The Context That Made This Message Necessary

To understand why this message matters, we need to trace the debugging arc that led to it. The Phase 12 implementation ([msg 3054]) had refactored the monolithic generate_groth16_proofs_c C++ function into a split start + finalize API, allowing the GPU worker to offload the b_g2_msm computation (~1.7 seconds) to a background thread and immediately pick up new work. This delivered a 2.4% throughput improvement at pw=10. But pw=12 crashed.

The user asked a pointed question at [msg 3055]: "the pending proof handle has nothing that can be freed early?" The assistant investigated ([msg 3056]–[msg 3062]) and discovered that the ProvingAssignment's a, b, c vectors — each ~4 GiB, totaling ~12 GiB per partition — were only needed by the GPU kernel region, which completed before prove_start returned. The background prep_msm_thread (running b_g2_msm) only needed the density trackers and witness assignments, not the NTT evaluation vectors. So the assistant freed them early.

But the OOM persisted. At [msg 3073], the RSS still peaked at ~650 GiB — only ~18 GiB less than before. The early a/b/c free was a drop in the bucket.

The user then asked at [msg 3076]: "Can we count and report number of each large buffer in flight and maybe the stage?" This was the crucial pivot. Instead of guessing, the team would instrument.

The assistant responded at [msg 3077] with a plan: track five buffer classes — ProvingAssignment a/b/c (~12 GiB), aux_assignments (~4 GiB), input_assignments (tiny), C++ split_vectors/tails, and C1 parsing data. Then, at [msg 3078], the assistant begins executing that plan.

Why the Pipeline Module?

The decision to place the counters in the pipeline module (cuzk-core/src/pipeline.rs) rather than in bellperson or the engine is telling. The pipeline module is the central coordination point for the synthesis → GPU → finalization flow. It has visibility into all stages without needing to modify the lower-level proving library (bellperson) or the engine's worker dispatch logic. By putting global atomic counters there, the assistant could add buf_synth_start(), buf_synth_done(), buf_abc_freed(), buf_finalize_done(), and buf_dealloc_done() hooks that are called from various points in the codebase without requiring cross-crate dependencies or major refactoring.

This is a classic systems debugging technique: when you don't know where the memory is, instrument the boundaries between stages. Each counter represents a transition: synthesis begins (buffer allocated), synthesis completes (buffer ready for GPU), GPU starts processing (a/b/c freed), GPU finishes (aux freed), dealloc thread completes (all Rust-side memory released). The delta between these counters at any point in time tells you exactly how many buffers are alive at each stage.

The Assumptions Embedded in the Design

The assistant made several implicit assumptions in this message. First, that global atomic counters would be sufficient — that contention on these counters wouldn't distort the measurements or create performance artifacts. Given that these are fetch_add/fetch_sub operations on cache-line-bouncing atomics, this assumption was reasonable for a debugging instrument but worth noting.

Second, the assistant assumed that the pipeline module was the right abstraction boundary. The counters would need to be called from engine.rs (where synthesis tasks are spawned and where the GPU channel operates) and from bellperson (where the dealloc thread runs). The pipeline module sits between these two layers, making it a natural home.

Third, the assistant assumed that printing a summary line at each event would be sufficient for diagnosis. This turned out to be correct — the log lines would later reveal the exact peak counts.

What the Instrumentation Revealed

The counters, once built and deployed ([msg 3103]–[msg 3108]), told a stark story. At peak memory pressure, the buffer tracker showed:

• provers=28: 28 ProvingAssignment sets alive simultaneously, each holding ~12 GiB of a/b/c vectors = ~336 GiB
• aux=97–100: 97–100 aux_assignment buffers alive, each ~4 GiB = ~388–400 GiB
• shells=69–72: density-only shells (tiny, negligible) The total estimated memory was ~732 GiB — consistent with the observed OOM at ~668 GiB RSS (the difference being allocation overhead and fragmentation). The critical finding was provers=28. With pw=12 (12 concurrent synthesis workers) and a channel capacity of 1 (only one synthesized job can be queued for the GPU at a time), the system was generating synthesis outputs faster than the GPU could consume them. Each completed synthesis task released its semaphore permit immediately, allowing another task to start synthesizing — even while the completed task sat blocked on synth_tx.send(), holding its ~16 GiB of data. The backlog grew without bound. The aux counter hitting 100 was a measurement artifact — buf_dealloc_done() was never called from bellperson (which didn't have access to the pipeline module's counters), so the aux counter only ever incremented. But the provers counter was real, and it was devastating.

The Fix That Followed

Armed with this data, the assistant implemented a structural fix: hold the partition semaphore permit until the synthesized job is fully delivered to the GPU channel ([msg 3112]). This ensured that only pw partitions could be in the "synthesizing + waiting for GPU" state at any time. The peak RSS dropped from 668 GiB to 294.7 GiB, and pw=12 ran 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 reverted the semaphore change and instead increased the channel capacity from 1 to partition_workers, allowing a natural buffer of completed jobs without blocking the semaphore. This trade-off between memory pressure and pipeline throughput became the central theme of the chunk.

Why This Message Matters

The subject message at index 3078 is the hinge point of the entire debugging episode. Before it, the assistant was operating on intuition — freeing a/b/c early seemed like the right fix, but it barely moved the needle. After it, the team had hard data showing exactly where the memory was going. The instrumentation transformed an opaque "why is it OOMing?" question into a concrete "28 provers are queued because the semaphore releases too early" diagnosis.

This pattern — instrument first, optimize second — is a textbook lesson in performance engineering. The assistant's willingness to build real-time visibility into every buffer class, rather than continuing to guess, is what ultimately unlocked the solution. The message itself is brief — a plan and a file read — but it represents the decision to stop reasoning in the dark and start measuring in the light.

The broader lesson for the project is that the synthesis-to-GPU channel architecture needs careful memory accounting. The pipeline's throughput is GPU-bound (~3.5s per partition), but synthesis can produce partitions faster (~5s each, with 12 in parallel). Without backpressure that accounts for memory, the queue grows until the system collapses. The instrumentation built in this message didn't just solve the immediate OOM — it created the visibility needed to design the next generation of memory-efficient proving pipelines.