The Pivot to Measurement: Diagnosing a 668 GiB Memory Leak in a GPU Proving Pipeline

In a high-stakes optimization session for a Groth16 proof generation pipeline targeting Filecoin's Proof-of-Replication (PoRep), a single short message marks a critical inflection point. The message, sent by an AI assistant in the middle of a debugging marathon, contains nothing more than a bash command to start monitoring the RSS (Resident Set Size) of a daemon process, along with its output showing a baseline of 70.0 GiB. But beneath this seemingly mundane technical action lies a carefully reasoned pivot from theoretical analysis to empirical measurement — a moment where the assistant abandons pure code tracing in favor of real-time data collection to diagnose a memory leak that had stymied both human and machine.

The Context: A Pipeline Pushed to Its Limits

To understand why this message matters, one must grasp the complexity of the system under development. The assistant and user had been iterating through "Phases" of optimization for cuzk, a CUDA-accelerated SNARK proving engine. Phase 12 had just introduced a "split GPU proving API" — an architectural change that decoupled the GPU worker's critical path from CPU post-processing by offloading the b_g2_msm (a multi-scalar multiplication on the G2 curve) into a background thread. This allowed the GPU to immediately pick up the next partition's work without waiting for the finalization step, theoretically improving throughput.

The Phase 12 benchmarks had been promising: with pw=10 (10 concurrent partition synthesis workers), the system achieved 37.1 seconds per proof, a ~2.4% improvement over the Phase 11 baseline of 38.0 seconds. But when the team tried to push further — increasing pw to 12 or 15 to saturate more CPU cores — the system crashed with Out Of Memory (OOM) errors. The Linux kernel's OOM killer would terminate the daemon, with RSS peaking at a staggering 668 GiB on a 755 GiB machine.

The user's frustration was palpable. In the messages preceding this one, they had written: "It's not 400GB tho" and "something is leaking somewhere." The assistant had been deep in code analysis — tracing the memory lifecycle of PendingProofHandle, checking whether the C++ prep_msm_thread (which runs b_g2_msm) captured dangling references to Rust-owned memory, and examining whether the dual async deallocation paths (one in C++, one in Rust) were creating contention that caused memory to pile up.

The Reasoning: Why This Message Was Written

The assistant's decision to write this particular message — to start RSS monitoring — represents a deliberate methodological choice. Up to this point, the debugging had been entirely static: reading source files, tracing ownership graphs, checking mutex contention patterns. The assistant had examined whether the PendingProofHandle held Rust-side synthesis data (provers, input assignments, auxiliary assignments) alive longer than necessary. It had verified that the C++ prep_msm_thread captured provers by reference and read inp_assignment_data and aux_assignment_data — raw pointers into Rust Vec<Arc<Vec<Fr>>> — meaning the Rust data must stay alive until the background thread completes. It had confirmed that both the C++ and Rust deallocation paths used their own mutexes (dealloc_mtx and DEALLOC_MTX respectively), and that malloc_trim was being called in the right places.

But all of this analysis had yielded no definitive answer. The theoretical calculations didn't add up: pw=12 added only ~26 GiB of extra memory (2 more partitions at ~13 GiB each) over pw=10, which should have been comfortably within the 755 GiB budget. Something was leaking — memory that should have been freed was not being returned to the operating system. The assistant needed to see the actual memory growth pattern in real time.

This message is the assistant saying, in effect: "I've exhausted what I can learn from reading code. Now I need to watch the system breathe."

How Decisions Were Made

The technical decisions embedded in this message reveal a methodical approach to diagnostics. First, the assistant retrieves the daemon's PID using pgrep -f "target/release/cuzk-daemon" — a robust pattern that matches the full command line rather than just the process name, avoiding false matches from other potential cuzk-daemon instances. Second, it captures a baseline RSS reading of 70.0 GiB, which is itself a significant data point: the daemon consumes 70 GiB just sitting idle, representing the SRS (Structured Reference String, ~44 GiB), the PCE (Prover Cache Engine, ~26 GiB), and other fixed overheads.

Third, the assistant constructs a background monitoring loop:

(while kill -0 $DAEMON_PID 2>/dev/null; do 
  echo "$(date +%T) RSS=$(ps -o rss= -p $DAEMON_PID | awk '{printf "%.1f", $1/1048576}') GiB"; 
  sleep 5; 
done) > /tmp/rss-monitor.log 2>&1 &

This is a carefully designed monitoring harness. The kill -0 check is a zero-overhead way to test whether the process is still alive — if the daemon crashes (as it had been doing with OOM), the monitoring loop terminates cleanly. The 5-second sampling interval is a pragmatic choice: frequent enough to capture the memory growth trajectory during a proof (~78 seconds per proof at pw=10), but not so frequent as to create significant I/O or CPU overhead from spawning ps and awk processes. The output is redirected to a log file (/tmp/rss-monitor.log) rather than printed to the terminal, ensuring the monitoring runs independently of the assistant's own command output and can be analyzed later.

The choice of RSS (Resident Set Size) rather than VSZ (Virtual Memory Size) or other metrics is also deliberate. RSS measures the actual physical memory committed to the process — pages that are in RAM. For a memory leak diagnosis, RSS is the most relevant metric because it directly reflects the memory pressure that triggers the OOM killer. VSZ would include mapped-but-not-touched pages (like the SRS file mapping), which could be misleading.

Assumptions Embedded in the Approach

This diagnostic approach rests on several assumptions, some explicit and some implicit. The assistant assumes that the daemon is still running from the previous invocation (message 3005-3006), where it was started with a configuration file. The pgrep command would fail if the daemon had been OOM-killed in the interim, but the assistant doesn't check for this failure mode — it proceeds directly to monitoring. In practice, this worked because the daemon was still alive, but a more robust script would have included error handling.

The assistant assumes that RSS sampling at 5-second intervals will reveal the leak pattern. This is reasonable for a system where proofs take ~78 seconds each and memory grows over minutes, but it could miss short-lived spikes or rapid allocation/deallocation cycles. A 5-second window might also alias with the GPU's periodic work patterns, creating misleading plateaus or drops.

More fundamentally, the assistant assumes that the leak is visible in aggregate RSS rather than being a fragmentation issue or a problem with a specific memory arena. RSS is a coarse metric — it doesn't distinguish between different allocators (glibc malloc, CUDA driver allocations, mmap'd regions) or different memory pools (SRS, PCE, partition synthesis data, GPU buffers). A leak that only affects one pool might be masked by normal fluctuations in others.

There's also an implicit assumption that the baseline of 70.0 GiB is "normal." In reality, 70 GiB for an idle daemon is itself remarkable — it means the fixed overheads consume nearly 10% of the system's total RAM before any work begins. This is a known property of the Groth16 proving system (the SRS alone is 44 GiB for 32 GiB sectors), but it means the effective memory budget for dynamic allocations is only ~685 GiB, not the full 755 GiB.

Input Knowledge Required

To fully appreciate this message, one needs substantial domain knowledge about the system. The reader must understand that each "partition" in the Groth16 proof represents a portion of the circuit evaluation, and that pw (partition workers) controls how many partitions are synthesized concurrently. Each partition synthesis consumes approximately 13 GiB of RAM for the ProvingAssignment data (the a, b, c NTT evaluation vectors plus input/auxiliary assignments). With pw=10, the system keeps ~130 GiB of partition data in flight; with pw=12, that rises to ~156 GiB.

One must also understand the Phase 12 split API architecture. Before Phase 12, the GPU worker would block until the entire proof (including b_g2_msm) was complete, then free all memory before picking up the next partition. Phase 12 split this into prove_start (which launches GPU kernels and returns immediately) and finish_pending_proof (which runs b_g2_msm in a background thread and finalizes). This means the Rust-side synthesis data (provers, input_assignments, aux_assignments) must remain alive in the PendingProofHandle until the background b_g2_msm thread completes — potentially creating a window where two generations of data are alive simultaneously.

The reader should also know about the dual deallocation architecture: both the C++ side (which frees GPU-side split vectors and tails) and the Rust side (which frees CPU-side provers and assignments) use static mutexes to serialize deallocation, preventing munmap TLB shootdown storms. This means deallocation is inherently serialized — if completions arrive faster than the dealloc mutex can be acquired and released, threads pile up holding their memory.

Output Knowledge Created

This message produces a concrete artifact: /tmp/rss-monitor.log, a time-series log of RSS readings sampled every 5 seconds. This log becomes the empirical foundation for the next phase of debugging. When the assistant later analyzes this log (in subsequent messages), it will reveal the memory growth pattern — how quickly RSS rises during a batch run, whether it plateaus or grows linearly, and whether it ever decreases (indicating successful deallocation) or only increases until OOM.

The baseline reading of 70.0 GiB is itself valuable knowledge. It confirms that the fixed overheads (SRS + PCE + daemon infrastructure) consume ~70 GiB, leaving ~685 GiB for dynamic allocations. When the assistant later sees RSS peaking at 668 GiB during a pw=12 run, this baseline allows it to calculate that the dynamic memory footprint reached ~598 GiB — far more than the theoretical ~156 GiB for 12 concurrent partitions, confirming a leak.

The Thinking Process: A Methodological Pivot

The thinking visible in this message — and in the surrounding context — reveals a structured debugging methodology. The assistant had been working through a hierarchy of hypotheses:

  1. Hypothesis 1: The C++ b_g2_msm thread captures dangling references to Rust memory. This was tested by reading the C++ source (groth16_cuda.cu) and confirming that prep_msm_thread captures provers by reference. The conclusion: yes, it does, but this is correct behavior — the Rust data must stay alive, and the PendingProofHandle ensures this.
  2. Hypothesis 2: The Rust dealloc thread (DEALLOC_MTX) creates a bottleneck where memory piles up. This was tested by tracing the finish_pending_proof code and comparing it to the pre-Phase 12 prove_from_assignments path. The conclusion: both use the same pattern, so this isn't new to Phase 12.
  3. Hypothesis 3: malloc_trim is not being called on the right thread. This was tested by checking which thread calls malloc_trim and whether the dealloc happens on a different thread. The conclusion: potentially an issue, but glibc's malloc_trim(0) is supposed to be global.
  4. Hypothesis 4: The dual deallocation mutexes (C++ and Rust) create serialization that causes memory to accumulate. This was tested by counting deallocs vs completions. The conclusion: 200 C++ deallocs for 200 partitions — the counts match, so the C++ side is keeping up. Having exhausted these static analyses without a definitive answer, the assistant pivots to dynamic analysis. The RSS monitoring command is the manifestation of this pivot. It's a recognition that the code is too complex to reason about purely through reading — the interactions between the Rust async runtime, the C++ thread pool, the CUDA driver's memory management, and glibc's allocator create emergent behaviors that can only be observed empirically. This is a mature engineering judgment. Many less experienced engineers would continue tracing code paths indefinitely, looking for the "bug" in the source. The assistant recognizes that the bug might not be in any single line of code but in the interaction of multiple subsystems — a timing issue, a fragmentation pattern, or a subtle allocator behavior that only manifests under real workload.

The Broader Significance

This message, for all its brevity, captures a universal debugging pattern: the transition from hypothesis formation to data collection. In the context of the larger optimization project, it represents the moment when the team stopped guessing and started measuring. The RSS monitoring log that this message produces will become the key evidence that leads to the root cause: the partition semaphore releasing its permit immediately after synthesis, allowing tasks to pile up while blocking on the single-slot GPU channel. The fix — holding the semaphore permit until the synthesized job is delivered to the GPU channel — will reduce peak RSS from 668 GiB to 294.7 GiB, enabling pw=12 to run without OOM for the first time.

But that fix is still many messages away. In this moment, all the assistant has is a 70 GiB baseline and a monitoring loop ticking away every 5 seconds. It's a small step, but it's the step that makes all subsequent diagnosis possible. Without this empirical foundation, the team would still be tracing code paths, wondering why 12 partitions consume more memory than 10.