The Last Benchmark: Characterizing Memory-Concurrency Tradeoffs in a Groth16 Proving Pipeline

In the final stretch of a months-long optimization campaign targeting Filecoin's SUPRASEAL_C2 Groth16 proof generation pipeline, a single message marks the transition from raw engineering into systematic characterization for production deployment. Message 3307 captures a moment of discovery, decision, and closure: the assistant has just received results from a benchmark run that reveals a critical insight about the relationship between concurrency and memory footprint, and it acts on that insight by launching one final test to complete the picture.

Context: The Low-Memory Benchmark Sweep

The message sits within a broader effort—Segment 32 of the conversation—where the assistant shifted from the intense engineering of Phase 12's memory backpressure fixes into a phase of consolidation, documentation, and performance characterization. Phase 12 had been a grueling sequence of debugging sessions: fixing a use-after-free bug in the C++ prep_msm_thread, implementing early deallocation of NTT evaluation vectors, building a global buffer tracker with atomic counters, auto-scaling channel capacity, and holding partition semaphore permits through channel sends to prevent OOM. The result was a working split GPU proving API that could sustain 37.7 seconds per proof without exhausting the 755 GiB of DDR5 RAM on the test system.

But that was on a large system. The user's question—"Run cuzk with lower parallelsms to see how low we can get RSS requirements on smaller systems and the timings"—opened a new axis of investigation. The assistant had been optimizing for throughput on a memory-rich machine. Now it needed to characterize the engine's behavior on systems with 128 GiB, 256 GiB, or 384 GiB of RAM, where the 400 GiB peak of the reference configuration would be impossible.

The assistant designed a systematic sweep across nine configurations, varying two key parameters: partition_workers (pw), which controls how many CPU threads simultaneously synthesize circuit partitions, and gpu_workers_per_device (gw), which controls how many GPU worker threads process synthesized partitions. The sweep covered pw=1, 2, 5, 7, 10, and 12, each tested with gw=1 and sometimes gw=2, using a consistent benchmark methodology: 5 proofs at concurrency 5 (j=5), with peak RSS measured via /proc/PID/status VmHWM.

By message 3305, the assistant had completed pw=1 through pw=10 with gw=1, plus pw=5 and pw=7 with gw=2. The results were already telling a clear story: a baseline of ~69 GiB (44 GiB SRS + 25.7 GiB PCE), with each additional partition worker adding roughly 16–20 GiB of peak RSS. The throughput scaled inversely with pw, from 290 seconds per proof at pw=1 (serialized synthesis, GPU starved) down to 45.4 seconds at pw=10 with gw=1.

The Discovery: Concurrency Matters

Message 3306 launched the pw=10 gw=2 benchmark—a configuration that had previously been tested only with j=20 (20 concurrent proofs) during the Phase 12 optimization work. That earlier test had shown 321 GiB peak RSS. But message 3307 reveals the result of running the same configuration at j=5:

Interesting! pw=10 gw=2 with only j=5 (vs j=20 in previous tests): 271 GiB peak, 42.5s/proof. Lower RSS because only 5 proofs queued.

This is the moment of discovery. The assistant immediately recognizes the significance: peak RSS is not solely a function of partition_workers and gpu_workers. The number of in-flight proofs—the concurrency level j—directly affects memory pressure. With j=5, only five proofs compete for buffers simultaneously, so the engine never needs to hold as many intermediate synthesis results in memory. The 271 GiB peak represents a 50 GiB reduction from the j=20 case, purely from limiting concurrency.

This insight has immediate practical value. A system integrator deploying on a 384 GiB machine might have looked at the j=20 result of 321 GiB and concluded that pw=10 gw=2 was too risky. The j=5 result of 271 GiB opens up that configuration for smaller systems, provided the workload can tolerate lower concurrency (which for many Filecoin storage providers is entirely acceptable—proofs are generated one at a time as sectors are challenged).

The Decision: Completing the Picture

The assistant's next action is not to stop and compile results, but to extend the sweep:

Let me also run the reference config pw=12 gw=2 at j=5 to see its actual RSS with lower concurrency.

This decision reveals several things about the assistant's reasoning. First, it recognizes that the reference configuration (pw=12, gw=2, the Phase 12 optimal) has only been characterized at j=20, showing 400 GiB peak. If the concurrency effect scales similarly, pw=12 gw=2 at j=5 might drop to 330–350 GiB, potentially making it viable on 384 GiB systems. Second, the assistant is methodical: a sweep is only complete when all configurations have been tested under the same conditions. Having tested pw=10 gw=2 at j=5, the natural control is the same test for pw=12 gw=2. Third, the assistant is building a complete characterization matrix—pw × gw × j—that will allow deployment recommendations across a range of system sizes.

The benchmark launches with the familiar script output: daemon PID, 28-second SRS load, baseline RSS of 69 GiB, and the first proof completing at 82.6 seconds. The message ends mid-output, with the benchmark still running. The assistant will receive the full results in the next round and compile them into the comprehensive table that appears in message 3309.

Assumptions and Knowledge

The assistant operates on several assumptions in this message. It assumes that the benchmark script correctly measures peak RSS via VmHWM, which captures the kernel's high-water mark for physical memory. It assumes that j=5 is a representative concurrency level for low-memory deployment scenarios—a reasonable assumption given that many Filecoin storage providers run sequential proof generation. It assumes that the pw=12 gw=2 result at j=5 will follow the same pattern as pw=10 gw=2, with proportionally lower RSS.

There is a subtle assumption worth examining: that the relationship between concurrency and RSS is roughly linear. The assistant implicitly expects that cutting j from 20 to 5 (a 4× reduction) produces a meaningful RSS reduction, but not a 4× reduction—because much of the memory (the 69 GiB baseline, plus per-partition-worker buffers) is independent of concurrency. The pw=10 gw=2 result confirms this: 271 GiB at j=5 vs 321 GiB at j=20, a 50 GiB reduction (roughly 16%). This is consistent with a model where concurrency adds a per-proof overhead that is small relative to the baseline and per-worker costs.

The input knowledge required to understand this message is substantial. One must know that pw (partition_workers) controls CPU-side synthesis parallelism, that gw (gpu_workers_per_device) controls GPU-side processing parallelism, that j (concurrency) controls how many proofs are in flight simultaneously, and that the engine's memory footprint is dominated by the SRS (44 GiB), PCE (25.7 GiB), and per-partition synthesis buffers. One must also understand the benchmark methodology: the daemon preloads the SRS at startup, the bench tool submits proofs via HTTP, and RSS is measured from /proc filesystem. Without this knowledge, the message reads as a cryptic sequence of numbers and acronyms.

The Thinking Process

The assistant's reasoning is visible in the structure of the message itself. It begins with a reaction—"Interesting!"—signaling that the result is noteworthy and unexpected. It immediately contextualizes the result against prior knowledge: "pw=10 gw=2 with only j=5 (vs j=20 in previous tests)". This comparison shows that the assistant is holding the j=20 result in working memory and recognizing the discrepancy. The explanation follows: "Lower RSS because only 5 proofs queued." This is a causal inference, connecting the observed difference to the mechanism of in-flight proof buffers.

The decision to run pw=12 gw=2 at j=5 is then made explicit: "Let me also run the reference config pw=12 gw=2 at j=5 to see its actual RSS with lower concurrency." The phrase "reference config" identifies pw=12 gw=2 as the canonical optimal configuration from Phase 12. The phrase "to see its actual RSS" indicates that the assistant considers the j=20 measurement incomplete or misleading for the low-memory use case. The decision is rational and methodical: to characterize a system, you must test it under the conditions you intend to recommend.

Significance and Impact

This message, while brief, is pivotal. It is the last benchmark launched before the assistant compiles all results into a comprehensive deployment guide. The discovery that concurrency significantly affects RSS—independent of partition_workers and gpu_workers—adds a third dimension to the characterization matrix. The final compiled table (message 3309) will include nine configurations with concrete deployment guidance for 128 GiB through 768 GiB systems, and the concurrency insight will inform the recommendation that lower j is acceptable for sequential proof workloads.

The message also demonstrates a key characteristic of the assistant's working style: it does not wait for all results to accumulate before acting. Upon receiving the pw=10 gw=2 result, it immediately recognizes the gap in the sweep and launches the pw=12 gw=2 test. This parallelism—interleaving analysis with execution—is what enables the assistant to complete a nine-configuration benchmark sweep in a single session, producing a coherent characterization that would take a human engineer hours of manual coordination.

In the broader arc of the conversation, this message represents the final data point before synthesis. The months of optimization—from the initial call chain analysis through nine identified bottlenecks, through Phases 10, 11, and 12's memory backpressure fixes—culminate in this systematic sweep. The assistant is no longer debugging or optimizing; it is characterizing and documenting, preparing the engine for production deployment by system integrators who need to know exactly how much memory each configuration requires.