The 28-Prover Pileup: Diagnosing Memory Pressure in a GPU Proving Pipeline

Introduction

In high-performance computing, the most valuable diagnostic tool is often the simplest: a counter. When the assistant in this opencode session added global atomic counters to track every large buffer class in a Groth16 proof generation pipeline, they were searching for the root cause of persistent out-of-memory (OOM) failures. What the counters revealed—and how the assistant interpreted that revelation—forms the subject of this article. Message 3108 is a turning point: a moment of diagnostic clarity where raw numbers from instrumentation are transformed into a structural understanding of a pipeline bottleneck, and where the assistant weighs competing design trade-offs under the harsh constraint of a 755 GiB memory ceiling.

Context: The Phase 12 Split API and Its Memory Mystery

The session leading to message 3108 is the culmination of a multi-week optimization effort for SUPRASEAL_C2, a Groth16 proof generation pipeline used in Filecoin's Proof-of-Replication (PoRep). The pipeline has two major phases: CPU-bound circuit synthesis (building constraint systems from vanilla proofs) and GPU-bound proving (executing multi-scalar multiplication and number-theoretic transform kernels). Phase 12 introduced a "split API" that decouples the GPU worker's critical path from CPU post-processing by offloading the b_g2_msm operation to a background thread.

But Phase 12 ran into a wall. Attempting to increase synthesis parallelism (pw=12 or pw=15 partition workers) triggered OOM crashes, with RSS peaking at 668 GiB on a 755 GiB system. The assistant suspected that memory was being held longer than necessary—perhaps the a, b, c NTT evaluation vectors (~12 GiB per partition) were not being freed promptly, or perhaps the pipeline was simply allowing too many synthesized partitions to queue up before the GPU could process them.

To resolve this, the assistant added a global buffer tracker with six atomic counters (buf_synth_start, buf_synth_done, buf_abc_freed, buf_finalize_done, buf_dealloc_done, buf_shell_done) and a log_buffers() helper that printed the state of every buffer class at each lifecycle event. This instrumentation was the key that unlocked the diagnosis in message 3108.

The Diagnostic Revelation

Message 3108 opens with the assistant staring at the buffer counter output from a pw=12 benchmark run:

aux hits 100 — which is 10 proofs × 10 partitions = all partitions ever synthesized. The buf_dealloc_done() is never called (bellperson can't reach it), so the counter just accumulates. That's a counter bug, not a memory bug. But the provers=28 peak IS real — 28 synthesis outputs waiting in the queue, holding ~336 GiB of a/b/c data.

This single sentence contains two distinct findings, and the assistant's ability to separate them is the first sign of analytical maturity.

Finding 1: The aux counter is a red herring. The aux counter tracks aux_assignment buffers (~4 GiB each). It hit 100, which sounds terrifying—400 GiB of aux data! But the assistant immediately recognizes this as a counter bug, not a memory bug. The buf_dealloc_done() decrement is called from the Rust dealloc thread in bellperson's finish_pending_proof, but bellperson is a separate crate that doesn't have access to the pipeline module's counter functions. The actual memory is being freed; the counter just never goes down. This is an important lesson in instrumentation: counters that only increment are worse than useless—they actively mislead unless you understand their coverage.

Finding 2: The provers counter reveals the real problem. The provers counter tracks ProvingAssignment sets (the a, b, c NTT evaluation vectors, ~12 GiB each). A peak of 28 means 28 synthesized partitions are sitting in memory, holding ~336 GiB of data. This is real memory that cannot be freed because the GPU hasn't consumed it yet. The assistant correctly identifies this as the root cause of the OOM at pw=12.

The Pipeline Dynamics: Why the Queue Grows

The assistant then reconstructs the pipeline dynamics that produce this pileup:

The channel between synthesis and GPU workers (synth_tx/synth_rx) has a lookahead capacity of 1, but synthesis tasks are spawned independently via tokio::spawn and enqueue onto it. The real backpressure comes from the partition semaphore (pw=12), but 12 partitions can complete synthesis concurrently, and if the GPU takes 3.5s per partition while synthesis takes ~5s per partition, the queue grows.

This is a subtle point that deserves unpacking. The pipeline has two independent concurrency controls:

  1. The partition semaphore (pw=12): Limits how many synthesis tasks can run simultaneously. This is the primary backpressure mechanism—it prevents the CPU from being overwhelmed by too many concurrent synthesis jobs.
  2. The GPU channel (lookahead=1): A bounded Tokio MPSC channel that carries synthesized jobs from CPU to GPU. Its capacity is 1, meaning only one job can be in transit. The critical insight is that these two controls are decoupled. The semaphore limits concurrent synthesis, but it doesn't limit total synthesized-but-not-yet-GPU-processed partitions. Once a synthesis task completes, it releases its semaphore permit, allowing another synthesis to start—even if the GPU channel is full. The completed job is sent to the channel, but if the channel is full, it queues in the sender's buffer (or, in this case, the tokio::spawn tasks pile up waiting to send). The assistant's arithmetic is telling: if synthesis takes ~5 seconds per partition and the GPU takes ~3.5 seconds, then with 12 concurrent synthesis workers, the system can produce partitions faster than the GPU can consume them. The imbalance is modest (5s vs 3.5s), but over the course of a 10-proof batch (100 partitions total), the queue grows inexorably, reaching 28 at peak.

The Solution Space: Trade-offs and Constraints

Having diagnosed the problem, the assistant immediately begins exploring solutions:

The real fix: limit the synthesis→GPU channel more aggressively, or free a/b/c as soon as synthesis completes (before sending to GPU). The provers' a/b/c are needed by the GPU kernels, so they can't be freed before prove_start. But we could restructure the pipeline to not synthesize more partitions than the GPU can consume.

Three approaches are considered:

  1. More aggressive channel limiting: Reduce the effective capacity between synthesis and GPU so that synthesis cannot outpace GPU consumption. This would prevent the queue from growing but would also reduce throughput by starving the GPU.
  2. Early deallocation: Free the a/b/c vectors as soon as synthesis completes, before sending to the GPU. The assistant correctly notes this is infeasible—the GPU kernels need those vectors. They represent the circuit constraints that the GPU must evaluate.
  3. Pipeline restructuring: Change the architecture so that the number of synthesized-but-unprocessed partitions is bounded. This could mean using a semaphore that covers the entire lifecycle (synthesis + GPU wait), or making the GPU channel capacity equal to the partition worker count so that completed jobs have a place to go without blocking. The assistant doesn't commit to a solution in this message—instead, they pivot to check the configuration:
Actually wait — let me check what lookahead is set to: [bash] grep -n "lookahead" /home/theuser/curio/extern/cuzk/cuzk-core/src/engine.rs | head -10

This is a characteristic pattern in the assistant's reasoning: propose hypotheses, identify the information needed to evaluate them, and immediately gather that information. The lookahead value is a critical parameter—if it's already set to a large number, then the channel capacity isn't the bottleneck and the fix must lie elsewhere.

Assumptions, Knowledge, and the Thinking Process

Several assumptions underpin the assistant's analysis in this message:

Assumption 1: The a/b/c vectors are ~12 GiB per partition. This is based on earlier memory accounting work (Segment 0's analysis). It's a well-founded estimate, but it's still an estimate—the actual size depends on the circuit structure, which varies between proofs.

Assumption 2: The GPU takes ~3.5s per partition. This comes from earlier benchmarking (Phase 8 TIMELINE analysis). It's accurate for the current configuration but could change with different GPU models or driver versions.

Assumption 3: The buf_dealloc_done() counter bug doesn't affect actual memory. This is correct—the counter is a diagnostic artifact, not a memory leak. But it does mean the aux counter is useless for diagnosing memory pressure, which is a loss of signal.

Assumption 4: The partition semaphore (pw=12) is the primary backpressure mechanism. This is correct in the current design, but the assistant is questioning whether it should be. The semaphore limits concurrency but not backlog, which is the fundamental design flaw.

Input knowledge required to understand this message includes: the structure of the Groth16 proving pipeline (synthesis vs GPU phases), the buffer classes (ProvingAssignment, aux_assignment, shells), the role of the partition semaphore and GPU channel, and the memory characteristics of each buffer class (~12 GiB for a/b/c, ~4 GiB for aux). The reader also needs to understand the counter instrumentation that was added in the preceding messages.

Output knowledge created by this message includes: the diagnosis that the provers pileup (28 partitions, ~336 GiB) is the root cause of OOM at pw=12; the identification of the decoupled semaphore/channel architecture as the structural flaw; and the articulation of three potential fix strategies. This knowledge directly informs the next round of implementation (Chunk 1 of Segment 30), where the assistant will implement the chosen fix.

Mistakes and Incorrect Assumptions

The assistant makes one notable mistake in this message: the claim that "the channel between synthesis and GPU workers has a lookahead capacity of 1." This is stated as fact, but the assistant then immediately checks the configuration to verify. This is not an error in the final analysis—it's a hypothesis being tested. The assistant's willingness to check their own assumptions is a strength, not a weakness.

A more subtle issue is the assistant's framing of the problem. They say "if the GPU takes 3.5s per partition while synthesis takes ~5s per partition, the queue grows." This implies synthesis is slower than GPU, which would mean the queue should shrink, not grow. The assistant likely means that with 12 concurrent synthesis workers, the aggregate synthesis throughput (12 workers × 1 partition per 5s = 2.4 partitions/second) exceeds the GPU throughput (1 partition per 3.5s = 0.29 partitions/second). This is correct but the phrasing is ambiguous.

The Broader Significance

Message 3108 represents a classic moment in systems debugging: the transition from "we have a memory problem" to "we understand the memory problem." The buffer counters transformed an amorphous OOM failure into a precise, quantifiable diagnosis. The assistant identified that 28 synthesized partitions were queued, holding 336 GiB of a/b/c data, and traced this to the architectural decoupling of the synthesis semaphore from the GPU channel.

This diagnosis is the foundation for everything that follows. In the next chunk of Segment 30, the assistant will implement two fixes: first, holding the semaphore permit until the job is delivered to the GPU channel (which fixes the memory but hurts throughput), and then increasing the channel capacity from 1 to partition_workers (which balances memory and throughput). The trade-offs explored in message 3108—between memory pressure and pipeline throughput—are the central tension of the entire optimization effort.

The message also illustrates a deeper principle: in complex systems, you cannot optimize what you cannot measure. The buffer counters were a crude instrument—atomic integers and eprintln! statements—but they provided the visibility needed to make a correct diagnosis. Without them, the assistant would have been guessing at the cause of the OOM, potentially wasting hours on wrong fixes.

Conclusion

Message 3108 is a masterclass in diagnostic reasoning. The assistant receives a stream of counter values, separates signal from noise (aux=100 is a counter bug; provers=28 is real), reconstructs the pipeline dynamics that produce the observed behavior, and articulates a solution space with clear trade-offs. The message is concise—barely 200 words of analysis—but it contains the seed of every subsequent optimization in Segment 30. It is a reminder that the most important tool in a systems engineer's kit is not a profiler or a debugger, but the ability to ask the right question and interpret the answer.