Pushing the Boundary: The pw=14 Benchmark That Tested the Limits of Memory Backpressure

Introduction

In the high-stakes world of Filecoin proof generation, every second counts and every gigabyte matters. The SUPRASEAL_C2 pipeline, responsible for producing Groth16 proofs for the Proof-of-Replication (PoRep) protocol, is a computational behemoth that routinely consumes hundreds of gigabytes of memory and runs for minutes per proof. Optimizing this pipeline is a delicate dance between throughput, memory pressure, and hardware constraints. This article examines a single, deceptively simple message from an opencode coding session — a bash command that starts a daemon with a new configuration — and unpacks the rich tapestry of engineering reasoning, iterative optimization, and hard-won knowledge that it represents.

The Message

The subject message, delivered by the AI assistant at index 3216, reads as follows:

[assistant] [bash] pkill -f cuzk-daemon 2>/dev/null; pkill -f "rss" 2>/dev/null; sleep 2
FIL_PROOFS_PARAMETER_CACHE=/data/zk/params nohup /home/theuser/curio/extern/cuzk/target/release/cuzk-daemon --config /tmp/cuzk-p12-pw14.toml > /home/theuser/cuzk-p12-nodebug-pw14.log 2>&1 &
echo "PID=$!"; sleep 3

On its surface, this is a routine operation: kill any running instances of the daemon and its RSS monitor, then launch a new daemon with a configuration file pointing to partition_workers = 14. The nohup ensures it survives shell exit, output is redirected to a log file, and the assistant waits three seconds for initialization. But this message is anything but routine. It represents a pivotal moment in a weeks-long optimization campaign — the first attempt to run the Phase 12 split GPU proving API with 14 partition workers after a series of critical memory backpressure fixes.

The Context: A Pipeline Under Pressure

To understand why this message matters, we must step back and examine the problem domain. The SUPRASEAL_C2 pipeline generates Groth16 proofs for Filecoin storage proofs. Each proof requires synthesizing multiple "partitions" — computational units that evaluate circuit constraints and produce proof components. The synthesis phase is CPU-bound, generating large evaluation vectors (the a/b/c vectors, each consuming ~12 GiB per partition). These synthesized partitions are then consumed by GPU workers that perform the heavy number-theoretic transform (NTT) and multi-scalar multiplication (MSM) operations.

The Phase 12 "split API" was a major architectural change that decoupled GPU work from CPU post-processing. Instead of each GPU worker holding a partition hostage while it performed both GPU computation and CPU-side finalization, the split API allowed the GPU to return early after the GPU-intensive portion (prove_start), while CPU finalization (prove_finalize) happened asynchronously. This improved throughput by hiding the latency of b_g2_msm operations behind other work.

But the split API introduced a dangerous memory problem. When CPU synthesis outpaces GPU consumption — which is the normal case, since synthesis is fast and GPU work is slow — completed partitions pile up in memory, waiting for GPU workers to become available. Without proper backpressure, this accumulation could balloon to catastrophic levels. Indeed, earlier attempts to run with partition_workers=12 resulted in OOM (out-of-memory) crashes at 668 GiB of resident memory, far exceeding the system's 755 GiB budget.

The Three-Pronged Memory Backpressure Fix

The messages immediately preceding our subject message document the implementation and benchmarking of three critical fixes that together constitute the memory backpressure mechanism:

Early a/b/c free: Immediately after prove_start returns from the GPU, the ~12 GiB evaluation vectors (a, b, c) are deallocated. The GPU no longer needs them — it has already transferred the data it requires — so holding them in memory serves no purpose. This single change reclaims ~120 GiB across 10 partitions.
Channel capacity auto-scaling: The synthesis-to-GPU channel, which carries completed partitions from CPU workers to GPU workers, was previously hardcoded to a capacity of 1. This meant that if a synthesis completed while the channel was full, it would block on send() while still holding its large allocations. By scaling the channel capacity to max(synthesis_lookahead, partition_workers), the channel can accommodate all in-flight partitions without blocking.
Partition permit held through send: A semaphore mechanism bounds the total number of partitions in flight. Previously, the permit was released as soon as synthesis completed, allowing a new synthesis to start even if the previous partition was still waiting in the channel. The fix holds the permit until the channel send() succeeds, ensuring that the number of outstanding partitions never exceeds partition_workers. The results were dramatic. With pw=12, the pipeline went from OOM at 668 GiB to running successfully at 399.7 GiB peak RSS — a 40% reduction. Throughput was 37.7 seconds per proof, competitive with the pre-fix baseline.

The Reasoning Behind pw=14

With pw=12 working reliably, the natural next question was: can we push further? The assistant's reasoning, visible in the preceding messages, reveals a systematic exploration of the configuration space. pw=10 delivered ~38.5 seconds per proof with ~317 GiB peak RSS. pw=12 delivered ~37.7-38.5 seconds per proof with ~400 GiB peak RSS. The improvement from pw=10 to pw=12 was modest — roughly 0.8 seconds per proof — but came at a memory cost of ~80 GiB. The question was whether pw=14 would continue this trend, offering further throughput gains, or whether they had hit a different bottleneck.

The assistant's thinking, reconstructed from the conversation, reveals several assumptions:

That synthesis parallelism is the limiting factor: If the GPU workers are starved for work because synthesis cannot keep up, adding more partition workers should improve throughput. But if synthesis is already fast enough to keep the GPUs busy, additional workers just consume memory.
That the DDR5 bandwidth wall hasn't been reached: The chunk summary tells us that "Higher pw values (14, 16) consumed more memory without improving throughput, hitting the DDR5 bandwidth wall." The assistant was probing to find this wall.
That the memory backpressure fixes would scale: The three fixes were designed to bound memory regardless of partition count. The assistant needed to verify that pw=14 would not trigger a new OOM.

The Methodology: A Pattern of Rigorous Benchmarking

The assistant's approach throughout this session exemplifies disciplined experimental methodology. Each configuration change is tested with:

Clean state: The daemon and RSS monitor are killed before each test to eliminate interference between runs.
Consistent workload: The benchmark uses cuzk-bench batch --type porep --c1 /data/32gbench/c1.json --count 20 --concurrency 20, running 20 proofs with 20-way concurrency.
Memory monitoring: A background process polls RSS every 5 seconds and logs it to a separate file.
Multiple runs: The assistant frequently runs the benchmark twice to distinguish systematic effects from noise.
Comparison to baselines: Every result is compared against Phase 12 baseline (37.1s/proof) and Phase 11 baseline (36.7s/proof). This rigor is essential because the system exhibits significant variance — the Phase 12 baseline of 37.1s was later found to be an outlier, with consistent results settling at 38.5-38.9s/proof. Without multiple runs and careful comparison, the assistant could easily draw false conclusions.

Input Knowledge Required

To fully understand this message, one needs substantial domain knowledge:

Groth16 proof generation: Understanding that proofs are composed of multiple partitions, each requiring CPU synthesis followed by GPU computation.
The split API architecture: Knowing that prove_start and prove_finalize are now decoupled, allowing GPU resources to be freed earlier.
Memory accounting: Understanding that a/b/c evaluation vectors are the dominant memory consumer (~12 GiB per partition), and that in-flight partitions accumulate in channel buffers.
The hardware environment: A system with 755 GiB RAM, 2 GPUs, and DDR5 memory, where bandwidth contention is a known bottleneck.
The optimization history: The progression from Phase 9 (PCIe transfer optimization) through Phase 10 (abandoned two-lock design) to Phase 11 (memory bandwidth interventions) and Phase 12 (split API).

Output Knowledge Created

This message, combined with the benchmark that follows it, produces critical knowledge:

pw=14 does not improve throughput: The chunk summary tells us that pw=14 (and pw=16) consumed more memory without improving throughput. The DDR5 bandwidth wall had been reached.
pw=12 is the optimal configuration: It delivers the best throughput-to-memory ratio, with 37.7-38.5 seconds per proof at ~400 GiB peak RSS.
The memory backpressure fixes scale: Even at pw=14, the pipeline does not OOM, confirming that the three fixes (early a/b/c free, channel capacity auto-scaling, permit held through send) correctly bound memory regardless of partition count.
The bottleneck has shifted: The limiting factor is no longer memory pressure or OOM risk, but DDR5 memory bandwidth. Further throughput gains require addressing bandwidth contention, not parallelism.

The Thinking Process

The assistant's reasoning, visible in the messages leading up to this one, reveals a sophisticated mental model of the system. After benchmarking pw=10 and pw=12, the assistant observes: "pw=12 gives 37.7-38.5s, averaging ~38.1s. That's better than pw=10's consistent 38.5-38.9s." The improvement is real but small — roughly 0.4-0.8 seconds per proof. The assistant then considers whether the regression from the Phase 12 baseline (37.1s) is due to the early a/b/c free adding deallocation overhead, but a GPU timing analysis shows the GPU is actually 0.5s slower per partition, suggesting memory pressure effects rather than CPU deallocation cost.

The decision to try pw=14 is a natural experiment: if the small improvement from pw=10 to pw=12 is real, perhaps pw=14 will continue the trend. But the assistant is also aware of the risk — each additional partition worker adds memory pressure. The configuration file is created with cat > /tmp/cuzk-p12-pw14.toml, a simple four-section config that sets partition_workers = 14 while keeping gpu_workers_per_device = 2 and gpu_threads = 32 unchanged.

Conclusion

The message at index 3216 is a single bash command — barely a line of code — but it encapsulates the essence of systems optimization: the iterative cycle of hypothesis, experiment, measurement, and refinement. The assistant had just solved a critical memory problem that had been causing OOM crashes, validated the fix across multiple configurations, and was now probing the boundaries of the newly stable system. The pw=14 experiment would reveal that the next bottleneck was not memory capacity but memory bandwidth — a finding that would shape the next phase of optimization.

This message is a testament to the power of disciplined experimentation in complex systems. Each benchmark, each configuration tweak, each log analysis builds a cumulative understanding of where time and memory are actually going. The assistant's methodical approach — clean state, consistent workload, multiple runs, careful comparison — transforms what could be a chaotic debugging exercise into a structured scientific inquiry. And the result is not just a faster proof pipeline, but a deep understanding of the system's behavior at the boundary of its physical constraints.