The Art of Instrumentation: Tracing Memory Pressure in a High-Performance GPU Proving Pipeline

Introduction

In the course of optimizing the cuzk SNARK proving engine—a high-performance system for generating Groth16 proofs for Filecoin's Proof-of-Replication (PoRep)—the development team encountered a stubborn memory capacity ceiling. Despite successfully implementing a split GPU proving API in Phase 12 that offloaded the b_g2_msm computation from the critical path and achieved a 37.1-second proof time, attempts to increase synthesis parallelism from 10 to 12 partition workers (pw=12) resulted in out-of-memory (OOM) crashes, with RSS peaking at 668 GiB on a 755 GiB system. The early deallocation of the massive NTT evaluation vectors (a, b, c—approximately 12 GiB per partition) after prove_start returned had only shaved ~18 GiB off the peak, leaving the fundamental question unanswered: where was all the memory going?

This article examines a single message in the conversation—message index 3082—in which the assistant takes the first concrete step toward answering that question by locating the synthesis function entry points in the pipeline code. While the message itself is brief—a single grep command searching for synthesis function names—it represents a critical juncture in the debugging process: the transition from planning an instrumentation strategy to executing it. Understanding this message requires situating it within the broader context of the memory pressure investigation, the design of the global buffer tracker, and the architectural decisions that shaped the proving pipeline.

[msg 3082]

[assistant] Now add hooks in the synthesis paths. Let me find where synthesis happens:
[bash] grep -n "fn synthesize_partition\|fn synthesize_porep\|fn synthesize_batch\|synthesize_circuits_batch" /home/theuser/curio/extern/cuzk/cuzk-core/src/pipeline.rs | head -15
6://!    `bellperson::synthesize_circuits_batch()` to produce intermediate state
104:    finish_pending_proof, prove_from_assignments, prove_start, synthesize_circuits_batch_with_hint,
200:/// Called after `synthesize_circuits_batch_with_hint` returns successfully.
262:        synthesize_circuits_batch_with_hint(circuits, hint)?;
720:/// This struct holds the output of `bellperson::synthesize_circuits_batch()`:
923:/// synthesizes them in a single `synthesize_circuits_batch()` call. The
959:pub ...

</msg>

The Context: A Memory Mystery

The story leading up to this message begins with the Phase 12 split GPU API implementation. The proving pipeline operates in two stages: synthesis (CPU-bound), which builds circuits from vanilla proofs and produces intermediate state including the massive a, b, c NTT evaluation vectors; and GPU prove, which consumes these vectors to generate the final Groth16 proofs. The split API allowed the GPU worker to offload the b_g2_msm computation to a background thread, hiding its latency from the critical path.

However, when the team attempted to scale synthesis parallelism from 10 to 12 workers, the system ran out of memory. The assistant had already implemented an early deallocation optimization—freeing the a, b, c vectors immediately after prove_start returned, since the C++ prep_msm_thread (which runs the background b_g2_msm) was confirmed not to need them. But this only saved ~18 GiB, far short of the ~300 GiB gap between the pw=10 and pw=12 memory profiles.

The user's question in [msg 3076]—"Can we count and report number of each large buffer in flight and maybe the stage?"—was the turning point. Rather than continuing to guess at the memory distribution, the user proposed building instrumentation to make the invisible visible. The assistant embraced this approach enthusiastically, designing a global buffer tracker with atomic counters for each buffer class: buf_synth_start, buf_synth_done, buf_abc_freed, buf_finalize_done, and buf_dealloc_done. These counters would be placed at key lifecycle events in the pipeline, and a log_buffers() helper would print the current state at each transition, providing real-time visibility into exactly how many large buffers were alive at every stage of processing.

The Message: First Steps Toward Instrumentation

Message 3082 is the moment when the assistant moves from design to implementation. Having already added the atomic counter definitions to pipeline.rs in the previous edit ([msg 3079]), the assistant now needs to integrate the counter hooks into the synthesis paths. But the synthesis code is complex, with multiple entry points: synthesize_partition, synthesize_porep_c2_partition, synthesize_batch, and the underlying synthesize_circuits_batch_with_hint. Before writing code, the assistant needs to understand the landscape.

The message consists of a single grep command that searches for all synthesis-related function definitions and references in pipeline.rs. The pattern is carefully constructed to catch multiple naming conventions:

The Reasoning: Why This Approach?

The assistant's decision to locate synthesis functions via grep rather than reading the file directly reflects a deliberate methodology. The synthesis paths in pipeline.rs are numerous and intertwined—the file is hundreds of lines long with multiple dispatch paths for different proof types (PoRep, etc.), partition counts, and concurrency levels. A naive read-through would be time-consuming and risk missing edge cases. By searching for function signatures, the assistant can quickly identify all the entry points that need instrumentation.

This approach also reveals the assistant's understanding of the codebase architecture. The assistant knows that:

  1. Synthesis happens through multiple paths, but they all converge on synthesize_circuits_batch_with_hint
  2. The SynthesizedProof struct (line 720) is the output type that carries the large buffers
  3. The engine dispatcher in engine.rs is where synthesized proofs are sent through channels to GPU workers The grep output serves as a roadmap for the instrumentation work. The assistant will need to place buf_synth_start() calls where synthesis begins (likely at the dispatch point in the engine), buf_synth_done() calls where synthesis completes (after synthesize_circuits_batch_with_hint returns), and buf_abc_freed() calls after prove_start returns.

Assumptions and Implicit Knowledge

The message rests on several assumptions that are worth examining:

Assumption 1: The synthesis functions are the right place to instrument. The assistant assumes that tracking buffer counts at synthesis boundaries will reveal the memory bottleneck. This is a reasonable assumption—the OOM occurs during synthesis-heavy phases, and the buffers in question (the a, b, c vectors, aux assignments, etc.) are created during synthesis and consumed during GPU prove. However, it's possible that the bottleneck lies elsewhere—in C1 JSON parsing, in the C++ CUDA runtime allocations, or in glibc arena fragmentation. The assistant implicitly acknowledges this possibility in [msg 3074] by noting that "glibc arena fragmentation" might be a factor, but proceeds with the Rust-side instrumentation as the most tractable first step.

Assumption 2: Atomic counters are sufficient for diagnosis. The assistant assumes that simple increment/decrement counters at lifecycle events will provide enough information to identify the bottleneck. This is a pragmatic choice—full heap profiling would be more precise but also more complex and invasive. The counters trade precision for simplicity, providing aggregate counts rather than per-allocation tracking. The risk is that the counters might not capture the full picture—for example, if memory is held by C++ code that doesn't go through the Rust allocation paths.

Assumption 3: The synthesis paths are discoverable via function name patterns. The grep pattern assumes that all relevant synthesis functions follow one of the named conventions. If there are synthesis paths with different naming (e.g., generate_partition or build_circuit), they would be missed. The assistant mitigates this by also searching for synthesize_circuits_batch which is the core bellperson function that all paths must call.

The Thinking Process: A Window into Debugging Methodology

The assistant's thinking process, visible in the surrounding messages, reveals a systematic debugging methodology:

  1. Hypothesis formation: The assistant hypothesizes that the PendingProofHandle holds memory unnecessarily ([msg 3055]). This is tested by tracing the C++ prep_msm_thread to determine which data it actually needs (<msg id=3057-3062>).
  2. Targeted intervention: Based on the analysis, the assistant implements early deallocation of a, b, c vectors (<msg id=3064-3066>). This is a surgical fix—freeing exactly the data that is no longer needed, nothing more.
  3. Empirical testing: The fix is benchmarked (<msg id=3068-3071>), and the results show only ~18 GiB improvement—far less than expected.
  4. Hypothesis revision: The assistant realizes the bottleneck is not the pending proof handle but something else entirely. The RSS data shows that even with pw=10, memory peaks at 367 GiB, far above the estimated 223 GiB. The assistant hypothesizes glibc fragmentation ([msg 3074]).
  5. Instrumentation design: The user proposes counting buffers in flight ([msg 3076]), and the assistant designs a comprehensive instrumentation system (<msg id=3077-3081>).
  6. Execution: Message 3082 begins the execution phase, locating the synthesis entry points to place the hooks. This progression from hypothesis → intervention → measurement → revised hypothesis → instrumentation is a textbook example of the scientific method applied to systems debugging. The assistant resists the temptation to make further guesses or apply more bandaids, instead choosing to instrument the system to get definitive answers.

Input and Output Knowledge

Input knowledge required to understand this message includes:

The Broader Significance

While message 3082 appears mundane—a developer searching for function definitions—it represents a critical methodological shift in the debugging process. Before this message, the assistant was applying fixes based on reasoning and hypothesis. After this message, the assistant will be guided by data from the instrumentation. This shift from a priori reasoning to empirical investigation is what ultimately solves the memory mystery.

The buffer counters, once integrated, will reveal the true bottleneck: the provers counter peaking at 28, meaning 28 synthesized partitions are queued holding their full ~16 GiB datasets. This occurs because the partition semaphore releases immediately after synthesis, allowing tasks to pile up while blocking on the single-slot GPU channel. The fix—holding the semaphore permit until the job is delivered to the GPU channel—dramatically reduces peak RSS from 668 GiB to 294.7 GiB, enabling pw=12 to run without OOM for the first time.

But that fix introduces a throughput regression (39.9s vs 37.1s) by serializing synthesis and channel delivery. The assistant then reverts the semaphore change and instead increases the channel capacity from 1 to partition_workers, allowing a natural buffer of completed jobs without blocking the semaphore. This final configuration achieves both memory stability and performance.

None of this would have been possible without the instrumentation that message 3082 begins to implement. The grep command is the first step on a path that leads from guesswork to understanding, from symptom to root cause, from OOM crashes to a stable, high-throughput proving pipeline.

Conclusion

Message 3082 is a deceptively simple message that marks the transition from hypothesis-driven debugging to data-driven investigation. The assistant's decision to locate synthesis functions via grep reflects a systematic methodology: understand the codebase structure before modifying it, place instrumentation at natural lifecycle boundaries, and let the data guide the next intervention. The message embodies the principle that in complex systems, the most valuable tool is not intuition but visibility—the ability to see what the system is actually doing with its resources.

The buffer tracker that this message begins to build will ultimately reveal the root cause of the OOM crashes and lead to a structural fix that balances memory pressure against pipeline throughput. It is a testament to the power of targeted instrumentation in high-performance computing: sometimes the most important code you write is the code that tells you what the other code is doing.