Probing the Memory Ceiling: A User's Optimization Suggestion in a High-Performance GPU Proving Pipeline

Subject Message: [user] Try higher synthesis partiton_workers in config, maybe 15/20?

In the middle of a deep optimization session targeting the SUPRASEAL_C2 Groth16 proof generation pipeline for Filecoin's Proof-of-Replication (PoRep), a brief user message cuts through the technical noise. The message is deceptively simple: "Try higher synthesis partiton_workers in config, maybe 15/20?" To an outsider, this looks like a casual suggestion. But within the context of the conversation — a months-long effort to squeeze every drop of performance from a GPU-accelerated proving system — this single line represents a critical decision point, a hypothesis about system behavior, and ultimately a probe that would reveal the hard memory ceiling of a 755 GiB machine.

The Context: What Came Before

To understand why this message was written, one must understand what had just been accomplished. The assistant had completed Phase 12 of the optimization campaign: the "split GPU proving API." This was a significant architectural change. In the previous Phase 11, the GPU worker's critical path included the b_g2_msm (a multi-scalar multiplication on the G2 curve) as a synchronous blocking step. The Phase 12 split API decoupled this work: gpu_prove_start would begin the GPU computation and return a PendingProofHandle, while a background thread (prep_msm_thread) handled the b_g2_msm asynchronously. The GPU worker could then pick up the next partition without waiting for the b_g2_msm to complete.

The benchmark results were encouraging. With the configuration gw=2 (GPU workers per device), pw=10 (partition workers), gt=32 (GPU threads), and j=15 (job concurrency), the system achieved 37.1 seconds per proof — a ~2.4% improvement over the Phase 11 baseline of 38.0 seconds. The split API was working, the concurrency bug (a use-after-free in the C++ CUDA code where the background thread captured a dangling reference to a stack-allocated provers array) had been fixed, and the code was committed to the feat/cuzk branch.

But 37.1 seconds was not the end goal. The optimization campaign had been iterating through phases, each targeting a different bottleneck: PCIe transfer optimization (Phase 9), two-lock GPU interlock (Phase 10 — abandoned), memory bandwidth contention (Phase 11), and now the split API (Phase 12). The user, seeing the Phase 12 results, recognized an opportunity. If the GPU worker was now faster because it no longer blocked on b_g2_msm, perhaps the bottleneck had shifted to synthesis throughput. More parallel synthesis workers could keep the GPU better fed, potentially improving overall throughput.

The Hypothesis: More Parallelism, More Throughput

The user's suggestion to try partition_workers = 15 or 20 was grounded in a straightforward intuition: the Phase 12 split API had reduced the GPU worker's per-partition latency, meaning the GPU could consume synthesized partitions faster. If synthesis could not keep up, the GPU would idle waiting for work. Increasing the number of concurrent synthesis workers from 10 to 15 or 20 would increase the rate at which synthesized partitions were produced, potentially allowing the GPU to run at full utilization.

This hypothesis made sense given the system's architecture. The pipeline had a clear producer-consumer relationship: the synthesis dispatcher (running on CPU cores) produced ProvingAssignment objects containing the circuit's a, b, and c NTT evaluation vectors, while the GPU worker consumed them to perform the final proof computation. The partition semaphore controlled how many synthesis tasks could run concurrently. If the GPU was the bottleneck, increasing synthesis parallelism would do nothing. But if synthesis was the bottleneck — and the Phase 12 speedup suggested the GPU was now faster — then more synthesis workers could help.

The specific values of 15 and 20 were educated guesses. The user knew the system had 755 GiB of RAM and that each partition synthesis consumed approximately 13 GiB. At pw=10, synthesis used ~130 GiB plus the SRS (44 GiB), PCE (26 GiB), and other overhead. At pw=15, the estimate was ~195 GiB for synthesis alone. The total should fit — but only just barely. The user was probing the boundary between "enough parallelism" and "too much memory pressure."

The Execution and the Result

The assistant eagerly picked up the suggestion. "Good idea — with the split API freeing up the GPU worker faster, the bottleneck may shift to synthesis throughput. More partition workers could keep the GPU fed better. Let me test pw=15 first, then pw=20." A todo list was updated, the daemon was killed, new configuration files were written (/tmp/cuzk-p12-pw15.toml and /tmp/cuzk-p12-pw20.toml), and the daemon was restarted with pw=15.

The benchmark was launched. Almost immediately, it failed:

Error: Prove RPC failed
Caused by:
    0: status: Unknown, message: "transport error", details: [], metadata: MetadataMap { headers: {} }
    1: transport error
    2: stream closed because of a broken pipe

The user confirmed with two terse messages: "oom." The system had run out of memory. The daemon was killed by the kernel's OOM killer. pw=15 was too aggressive.

The Discovery: A Memory Ceiling

The OOM at pw=15 revealed a hard constraint. The assistant calculated: "Each partition synthesis uses ~13 GiB, so 15 × 13 = ~195 GiB just for synthesis, plus the SRS (44 GiB) + PCE (26 GiB) + other overhead exceeds the 755 GiB." But the numbers didn't quite add up — 195 + 44 + 26 = 265 GiB, well within 755 GiB. Something else was consuming memory.

This discrepancy would drive the next phase of investigation. The assistant would eventually discover that the PendingProofHandle held onto the massive a, b, c NTT evaluation vectors (~12 GiB per partition) even after they were no longer needed by the GPU kernel. More critically, the partition semaphore released its permit immediately after synthesis completed, allowing tasks to pile up while blocking on a single-slot GPU channel. The buffer counters built later would reveal that the provers counter peaked at 28 — meaning 28 synthesized partitions were queued, each holding ~16 GiB of data, totaling ~448 GiB of queued data alone. This was the true cause of the OOM, not the raw synthesis memory.

The user's suggestion to try pw=15/20, while unsuccessful in its immediate goal, served as the probe that revealed this deeper issue. Without the OOM, the memory buildup from the semaphore/channel mismatch might have remained hidden, masked by the comfortable margin at pw=10.

Assumptions and Their Limits

The user's message rested on several assumptions, some explicit and some implicit:

  1. That the GPU was now the bottleneck's consumer side. The Phase 12 split API did make the GPU worker faster, but the assumption that synthesis throughput was the new bottleneck was not yet validated. In fact, the system was still GPU-bound in the critical path — the bottleneck had shifted only slightly.
  2. That memory would scale linearly with partition workers. The user assumed 15 × 13 GiB = ~195 GiB for synthesis. But they did not account for the memory held by completed-but-unconsumed partitions queued in the channel, nor for the memory held by the PendingProofHandle after prove_start returned. These hidden memory consumers multiplied the effective per-partition footprint.
  3. That the system had headroom. With 755 GiB of RAM, pw=10 consumed perhaps 300-400 GiB. The user assumed there was room for more. The OOM proved otherwise.
  4. That the daemon would gracefully handle OOM conditions. The daemon did not — it was killed by the kernel, requiring manual restart and investigation.

The Broader Significance

This message is a perfect example of the iterative, hypothesis-driven optimization process that characterizes high-performance systems engineering. The user did not command or demand; they suggested a probe. The probe failed, but the failure was informative. The OOM at pw=15 was not a dead end — it was a signal that led directly to the next phase of investigation: memory instrumentation, buffer tracking, and the discovery of the semaphore/channel mismatch that was causing memory to balloon.

In the subsequent chunk (Chunk 1 of Segment 30), the assistant would build a global buffer tracker with atomic counters (buf_synth_start, buf_abc_freed, buf_dealloc_done) to gain real-time visibility into every large buffer class in flight. This instrumentation revealed that the provers counter peaked at 28, meaning 28 synthesized partitions were queued holding their full ~16 GiB datasets. The fix — holding the semaphore permit until the synthesized job was fully delivered to the GPU channel — reduced peak RSS from 668 GiB to 294.7 GiB and enabled pw=12 to run 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.

All of this was set in motion by the user's simple suggestion. The message "Try higher synthesis partiton_workers in config, maybe 15/20?" was the catalyst that transformed a working optimization into a deeper understanding of the system's memory dynamics. It is a reminder that in systems engineering, the most valuable experiments are often the ones that fail — because they reveal the constraints that success would have kept hidden.

Input and Output Knowledge

To understand this message, the reader needs knowledge of: the Phase 12 split GPU proving API and its purpose; the concept of partition workers as CPU-side synthesis parallelism; the memory characteristics of partition synthesis (~13 GiB per partition); the system's total RAM (755 GiB); the current baseline configuration (pw=10, gw=2, gt=32, j=15); and the iterative phase structure of the optimization campaign.

The message created new knowledge: the confirmation that pw=15 causes OOM on this hardware; the discovery that memory usage does not scale linearly with partition workers due to queued partitions; and the impetus to build memory instrumentation that would reveal the semaphore/channel mismatch. This knowledge directly led to the next round of optimization, ultimately enabling pw=12 to run stably with a peak RSS of 294.7 GiB.

Conclusion

The user's six-word message — "Try higher synthesis partiton_workers in config, maybe 15/20?" — is a masterclass in concise, hypothesis-driven optimization. It was not a command but a probe, grounded in an understanding of the system's architecture and the recent Phase 12 changes. It failed, but it failed informatively, revealing a memory ceiling that no amount of theoretical analysis would have uncovered. In doing so, it set the stage for the most important diagnostic work of the entire optimization campaign: the discovery that the pipeline's memory pressure was not a simple function of partition workers, but a complex emergent behavior of the semaphore, channel, and buffer lifecycle interactions. The message is a testament to the value of empirical probing in systems engineering — and to the insight that sometimes the most productive thing you can do is try something that might not work.