The Dual Dealloc Paths: Tracing a Memory Leak in a GPU Proving Pipeline

In the high-stakes world of GPU-accelerated zero-knowledge proof generation, memory is the invisible governor. No matter how fast your CUDA kernels run, if the system runs out of RAM, it simply stops. Message 3000 captures a pivotal moment in a debugging session where an engineer, faced with mysterious out-of-memory (OOM) failures after increasing synthesis parallelism, realizes that the memory architecture of their newly implemented "split API" is more complex than anticipated — and begins the methodical work of tracing where the bytes are actually going.

The Context: Phase 12 and the Split API

The message sits within a larger optimization effort codenamed "Phase 12" for the cuzk GPU proving engine, a system that generates Groth16 proofs for Filecoin's Proof-of-Replication (PoRep) protocol. The core innovation of Phase 12 was a "split GPU proving API": instead of keeping the GPU worker thread busy until every last computation completed, the API was split into prove_start (which launches GPU kernels and returns immediately) and prove_finish (which waits for a background b_g2_msm computation to complete). This architectural change was designed to hide the latency of the b_g2_msm multi-scalar multiplication, allowing the GPU worker to pick up the next partition's work without waiting for the previous one's post-processing to finish.

The initial results were promising: with partition_workers=10 (the baseline), the Phase 12 implementation achieved 37.1 seconds per proof, a ~2.4% improvement over the Phase 11 baseline of 38.0 seconds. But when the engineer tried to increase synthesis parallelism — first to pw=15, then to pw=12 — the system crashed with out-of-memory errors. The user's observation was pointed: "It's not 400GB tho, something is leaking somewhere" ([msg 2993]). The 755 GiB machine should have been able to handle a modest increase from 10 to 12 concurrent partition syntheses, each consuming roughly 13 GiB. The ~26 GiB delta shouldn't have been fatal.

The Message: Counting Deallocs

Message 3000 begins with the assistant examining the async deallocation timings from the C++ side, which it had just retrieved in the previous message ([msg 2999]):

Those are the C++ side dealloc timings. But there are TWO async dealloc paths now: one in C++ (split_vectors, tails) and one in Rust (provers, input/aux assignments). The Rust one is in finish_pending_proof and also uses a static DEALLOC_MTX. Both dealloc mutexes serialize separately, but together they could create a bottleneck where memory accumulates.

This is the critical realization. The engineer had been thinking about async deallocation as a single mechanism, but the split API had inadvertently created two independent async dealloc pipelines, each with its own mutex-based serialization. On the C++ side, the GPU code deallocates split_vectors and tails buffers asynchronously. On the Rust side, the finish_pending_proof function spawns a thread to deallocate the provers, input_assignments, and aux_assignments data structures, gated by a static DEALLOC_MTX. Both mutexes serialize their respective deallocation work, but they serialize separately — meaning memory could accumulate on one side while waiting for the other side's mutex to release.

The engineer then runs a simple but revealing diagnostic:

echo "C++ deallocs: $(grep -c 'async_dealloc_ms' /home/theuser/cuzk-p12.log)"; echo "GPU completions: $(grep -c 'GPU prove complete (split)' /home/theuser/cuzk-p12.log)"
C++ deallocs: 200
GPU completions: 200

The counts match perfectly: 200 C++ deallocations for 200 GPU completions (20 proofs × 10 partitions each). This rules out a C++-side dealloc leak — every GPU completion has a corresponding C++ deallocation event. The C++ async dealloc path is being invoked correctly for every single partition.

The Reasoning: What the Counts Tell Us

The matching counts are both reassuring and frustrating. They reassure the engineer that the C++ dealloc path is not silently dropping deallocations — every partition that completes on the GPU also triggers its C++-side cleanup. But they also frustrate because the OOM at pw=12 remains unexplained. If the C++ side is cleaning up correctly, the leak must be elsewhere.

The engineer's reasoning reveals a sophisticated understanding of the system's memory architecture. The key insight is that the two async dealloc paths, while individually correct, could interact pathologically. Consider the timing: when a partition's GPU work completes, the C++ side spawns an async dealloc for its split vectors and tails. Simultaneously, the Rust finalizer task (running in a spawn_blocking closure) calls finish_pending_proof, which joins the b_g2_msm background thread, then spawns a Rust-side async dealloc for the synthesis data. Both dealloc paths are racing to free memory, but each is serialized behind its own mutex.

If the Rust-side dealloc mutex is contended — because multiple finalizers completed close together and their dealloc threads are queuing up — then the Rust-owned provers, input_assignments, and aux_assignments data structures remain alive longer than necessary. Meanwhile, the C++ side may have already freed its buffers, but the overall RSS doesn't drop because the Rust-side data is still pinned in memory waiting for its turn on the mutex.

This is the "bottleneck where memory accumulates" that the engineer hypothesizes. The two dealloc mutexes, each serializing independently, create a situation where memory can pile up on one side even as the other side completes its cleanup promptly.

Assumptions and Their Limits

The engineer makes several assumptions in this message, most of which are sound but worth examining:

Assumption 1: The grep counts are accurate. The engineer assumes that grep -c 'async_dealloc_ms' correctly counts every C++ deallocation event. This is reasonable — the log format CUZK_TIMING: async_dealloc_ms=N is emitted once per dealloc call. But it assumes no log lines were dropped, no races in log emission, and no edge cases where dealloc is called without logging.

Assumption 2: Matching counts imply correct dealloc behavior. This is the shakiest assumption. The counts matching means every GPU completion has one corresponding C++ dealloc event, but it doesn't tell us how much memory was freed, or whether the dealloc actually succeeded. A dealloc call could be made but the underlying memory might not be returned to the OS (e.g., if free() doesn't release to the kernel, or if the allocator holds onto cached memory). The async_dealloc_ms timing values from the previous message ([msg 2999]) show wide variation — from 153 ms to 876 ms — which could indicate varying amounts of work or contention on the dealloc mutex.

Assumption 3: The Rust-side dealloc is the likely culprit. The engineer's hypothesis that "both dealloc mutexes serialize separately, but together they could create a bottleneck" is a plausible theory but remains unverified at this point. The subsequent investigation (in chunk 1 of segment 30) would reveal that the actual bottleneck was different: the partition semaphore released immediately after synthesis, allowing tasks to pile up while blocking on the single-slot GPU channel. The Rust-side dealloc mutex turned out to be a secondary concern.

Input Knowledge Required

To fully understand this message, a reader needs to grasp several layers of the system architecture:

The Phase 12 split API: The fundamental change was splitting GPU proof generation into prove_start (launches kernels, returns immediately) and prove_finish (waits for background b_g2_msm). This means the GPU worker can start processing the next partition before the previous one's post-processing completes.
The two dealloc paths: C++ dealloc handles GPU-side buffers (split vectors, tails from NTT evaluations). Rust dealloc handles CPU-side synthesis data (provers, input assignments, auxiliary assignments). Each path uses a static DEALLOC_MTX to serialize deallocation onto a background thread.
The memory footprint: Each partition synthesis allocates ~13 GiB of data. With partition_workers=12, up to 12 syntheses can be in flight simultaneously, plus the SRS (44 GiB) and PCE (26 GiB) and other overhead.
The log format: The daemon emits structured log lines like CUZK_TIMING: async_dealloc_ms=169 and GPU prove complete (split) proof_count=1. The engineer uses grep -c to count occurrences.
The concurrency model: The system uses j=15 (15 concurrent jobs), gw=2 (2 GPU workers), and pw=N (N partition workers for synthesis). These interact to determine how much memory is in flight at any given moment.

Output Knowledge Created

This message produces a concrete diagnostic result: the C++ async dealloc path is functioning correctly, with exactly one dealloc event per GPU completion. This negative result is valuable — it rules out one class of bugs (C++ dealloc leaks) and forces the investigation to focus elsewhere.

The message also introduces a new conceptual framework for understanding the memory pressure: the idea of two independent dealloc pipelines that can become desynchronized. This reframes the problem from "is there a leak?" to "is there a timing mismatch between the two dealloc paths?" — a more subtle and interesting question.

The Thinking Process

The engineer's thinking in this message is a textbook example of systematic debugging under pressure. The sequence across messages 2994-3000 shows:

Hypothesis formation ([msg 2994]): "Something is leaking" — the user and engineer agree the OOM shouldn't happen from just 2 extra partition workers.
Code tracing (<msg id=2995-2996>): The engineer traces the memory lifecycle by reading source code, confirming that the C++ prep_msm_thread reads Rust-owned memory (so the Rust data must stay alive during b_g2_msm).
Flow verification ([msg 2997]): The engineer checks that PendingProofHandle is properly consumed by gpu_prove_finish, confirming the ownership chain is correct.
Quick checks ([msg 2998]): The engineer checks malloc_trim placement, finding it only in process_partition_result.
Data gathering ([msg 2999]): The engineer extracts async_dealloc_ms timing values from logs, seeing wide variation (153-876 ms).
Pattern recognition ([msg 3000]): The engineer realizes there are two async dealloc paths and runs the count check. This progression shows a disciplined approach: form hypotheses, trace code to verify assumptions, gather data, and let the data guide the next hypothesis. The engineer doesn't jump to conclusions — each step narrows the search space.

The Broader Significance

Message 3000 is interesting not because it solves the OOM problem, but because it represents the moment when the engineer's mental model of the system's memory architecture is refined. The realization about dual dealloc paths is a genuine insight — the split API, designed to improve throughput by decoupling GPU work from post-processing, had an unintended consequence: it split the deallocation path into two independently-serialized pipelines. This kind of emergent complexity is common in high-performance systems where small architectural changes ripple through the memory management subsystem.

The message also illustrates the importance of counting things in debugging. The simple grep -c command — counting log lines — provides an immediate, unambiguous answer to a specific question. In a system handling gigabytes of data across C++ and Rust FFI boundaries, sometimes the most powerful tool is a well-placed wc -l.