The 203-GiB Data Point: How a Single Memory Measurement Unlocked the Phase 4 Regression Diagnosis

cat /tmp/cuzk-phase4-mem2.csv | sort -t, -k2 -n | tail -3
1771368525552,212987468,203.12
1771368526647,212987492,203.12
1771368527742,212987636,203.12

At first glance, message [msg 966] appears to be the most mundane of operations: three lines of CSV data showing memory usage hovering around 203 GiB. A timestamp, a kilobyte count, a gibibyte figure. Yet this single command and its output represent the culmination of a multi-hour diagnostic odyssey through one of the most complex performance regressions in the cuzk SNARK proving engine. The numbers on the screen—203.12 GiB—were not merely a measurement. They were a verdict.

The Context: A Regression That Shouldn't Have Happened

To understand why this message matters, one must understand the journey that led to it. The cuzk team had successfully completed Phases 0 through 3 of their optimization roadmap, building a pipelined Groth16 proving engine for Filecoin's Proof-of-Replication (PoRep) that achieved a baseline of 88.9 seconds for a single 32 GiB sector proof. Phase 4 was supposed to be the victory lap—a set of compute-level micro-optimizations that would shave seconds off an already fast pipeline.

Five optimizations were implemented in Phase 4, Wave 1:

The Diagnostic Campaign

What followed was a textbook exercise in disciplined performance engineering. The team did not blindly revert all changes. Instead, they systematically instrumented, measured, and isolated.

The first suspect was A2 (pre-sizing). The upfront allocation of massive vectors (the PoRep C2 circuit has ~130 million constraints across 10 partitions) could cause a page-fault storm as the kernel lazily mapped physical pages. A2 was partially reverted from the multi-sector synthesis path, but one remaining call site in pipeline.rs still needed attention.

The second suspect was B1 (cudaHostRegister). Pinning ~125 GiB of host memory requires touching every page, which could add significant overhead. But without precise instrumentation, this was just a guess.

The team added CUZK_TIMING printf instrumentation to the CUDA host code in groth16_cuda.cu, adding timing measurements around each phase of GPU proving: pinning, prep MSM, B_G2 MSM, NTT/MSM H, batch addition, and tail MSM. But when they ran the first instrumented test, no CUZK_TIMING output appeared. The C printf calls were being fully buffered because stdout was redirected to a file. The fix was to replace printf with fprintf(stderr, ...) followed by fflush(stderr)—a small but critical plumbing fix that unlocked the entire diagnostic pipeline.

The First Breakthrough: B1 as the Primary Culprit

With the instrumentation working, message [msg 963] revealed the first timing breakdown:

CUZK_TIMING: pin_abc_ms=5733 num_circuits=10 abc_bytes_each=4168923808
CUZK_TIMING: prep_msm_ms=1722
CUZK_TIMING: gpu_tid=0 ntt_msm_h_ms=21417
CUZK_TIMING: gpu_tid=0 batch_add_ms=1446
CUZK_TIMING: gpu_tid=0 tail_msm_ms=1259 gpu_total_ms=24123
CUZK_TIMING: b_g2_msm_ms=22849 num_circuits=10

The data was unambiguous: B1 (cudaHostRegister) was adding 5.7 seconds of overhead just to pin memory, far exceeding the estimated 150–300 milliseconds. The team immediately reverted B1 and re-ran the test. The total proof time dropped from 101.3 seconds to 94.4 seconds—a meaningful improvement, but still 5.5 seconds above the 88.9-second baseline.

The remaining regression was now isolated to synthesis, which was taking 60.3 seconds versus the expected ~55 seconds. And the only synthesis-related optimization still in play was A1 (SmallVec).

The Microbenchmark Verdict

To isolate the A1 effect without the noise of GPU proving and SRS loading, the team built a synth-only microbenchmark subcommand in cuzk-bench. They ran four configurations—Vec (original), SmallVec with inline capacity 1, capacity 2, and capacity 4—each for three iterations. The results were conclusive:

| Configuration | Synthesis Time | |---|---| | Vec (original) | 54.5 s | | SmallVec cap=1 | 59.6 s | | SmallVec cap=2 | 60.0 s | | SmallVec cap=4 | 60.2 s |

SmallVec, regardless of inline capacity, caused a 5–6 second regression in synthesis time. This was deeply puzzling: SmallVec was supposed to reduce heap allocations by storing small vectors inline, which should have improved cache behavior and reduced allocation overhead. Instead, it was substantially slower.

The Memory Question

This is where message [msg 966] enters the story. With B1 reverted and A2 fully removed, the team had one remaining question before diving into the SmallVec mystery: what was the memory profile?

The B1 optimization (cudaHostRegister) was motivated by a memory concern: without pinning, cudaMemcpyAsync falls back to synchronous staging through an internal 32 MiB pinned buffer, cutting bandwidth by roughly 50%. But if the memory overhead of pinning was 5.7 seconds, the trade-off was clearly not worth it—at least for this hardware configuration.

However, the team needed to confirm that reverting B1 did not cause memory usage to spike. The baseline memory footprint for a single PoRep proof was approximately 200 GiB—already an enormous figure driven by the need to hold 10 partitions' worth of constraint assignments, each with a/b/c vectors totaling ~4 GiB per partition, plus the SRS parameters (~44 GiB on disk, loaded into GPU memory).

Message [msg 966] shows the peak memory measurement from the memory monitor that was started alongside the daemon:

1771368525552,212987468,203.12
1771368526647,212987492,203.12
1771368527742,212987636,203.12

The three lines represent consecutive one-second samples at the memory peak: 203.12 GiB. This is essentially identical to the baseline of ~200 GiB. The CSV format—timestamp_ms,rss_kb,rss_gib—confirms that the process RSS stabilized at 212,987,636 KB, or 203.12 GiB.

This measurement was the good-news data point the team needed. It confirmed that:

  1. Reverting B1 did not increase memory usage. The 203 GiB figure was consistent with the baseline, meaning the memory pinning optimization was pure overhead with no memory benefit on this run.
  2. The remaining regression (5.5 seconds) was not memory-related. If memory had spiked due to the A1 SmallVec change, that would have pointed to a different root cause. But the memory was flat, narrowing the investigation to CPU-side effects.
  3. The other optimizations (A4 parallel B_G2, D4 window tuning) had no measurable memory impact. This was important because it meant they could be kept without concern.

What This Message Reveals About the Debugging Process

Message [msg 966] is a masterclass in systematic performance debugging. The team did not jump to conclusions or revert all changes at once. Instead, they:

  1. Instrumented precisely. The CUZK_TIMING printf's were added to the CUDA host code to get microsecond-level phase breakdowns.
  2. Fixed measurement infrastructure. When printf output was lost to buffering, they diagnosed and fixed the issue (fflush) rather than giving up.
  3. Isolated variables. They reverted one optimization at a time (A2 first, then B1) and measured the impact of each.
  4. Built targeted microbenchmarks. The synth-only subcommand allowed them to test A1 without the noise of GPU operations.
  5. Triangulated with multiple data sources. Timing data (CUZK_TIMING), throughput data (bench results), and memory data (message [msg 966]) were all collected and cross-referenced. The memory measurement in message [msg 966] served as the final sanity check. It answered the question: "Did we break anything else while fixing the regression?" The answer was no—memory was stable at 203 GiB, consistent with expectations.

The Deeper Puzzle: Why SmallVec Is Slower

With the memory data confirming that the regression was purely a CPU synthesis issue, the team could focus on understanding why SmallVec—a change that should have improved performance—was actually 5–6 seconds slower on their AMD Zen4 Threadripper PRO 7995WX system.

The working hypothesis was that SmallVec's branch-on-inline-capacity logic was causing branch mispredictions at scale. The LC indexer processes millions of linear combinations during constraint synthesis, and each one involves checking whether the vector's length exceeds the inline capacity. If the branch predictor cannot learn the pattern (because most vectors are small but some are large), the pipeline stalls accumulate to seconds of wasted cycles.

The team prepared to run perf stat to collect hardware counter data—L1/L2/L3 cache misses, branch mispredictions, instructions per cycle (IPC)—on a single-partition synthesis run. This would reveal whether the SmallVec regression was caused by branch misprediction, cache pressure, or some other microarchitectural effect.

The Broader Significance

Message [msg 966] sits at the intersection of several themes that define the cuzk project:

The scale of Filecoin proving. A single proof consumes 203 GiB of RAM. This is not a toy workload—it is one of the most computationally intensive operations in the blockchain space, requiring careful memory management and hardware-aware optimization.

The importance of measurement. Without the CUZK_TIMING instrumentation, the B1 regression would have remained a mystery. Without the memory monitor, the team could not have ruled out memory-side effects. Every optimization decision was grounded in data.

The discipline to revert. The team had invested significant effort in implementing B1 and A2. Reverting them was not a failure—it was a success of the measurement-driven approach. The optimizations that survived (A4, D4) were kept because they were measured to be neutral or beneficial.

The hardware-specific nature of optimization. SmallVec may be faster on other architectures (Intel Ice Lake, AMD Milan) but slower on Zen4. The team's willingness to test and measure on their actual target hardware, rather than relying on conventional wisdom, is a hallmark of serious performance engineering.

Conclusion

A single bash command, three lines of CSV data, 203.12 GiB. Message [msg 966] is a reminder that in performance engineering, the most valuable data points are often the simplest. The memory measurement did not require sophisticated tooling—just a shell script that polled /proc/pid/stat every second and wrote to a CSV file. But that simple measurement, combined with the timing data from CUZK_TIMING and the microbenchmark results, formed a complete picture of the Phase 4 regression.

The team now knew: B1 was the primary culprit (5.7 seconds), A1 was the secondary culprit (5–6 seconds in synthesis), A2 was neutral after full reversion, and A4 and D4 were safe to keep. The memory profile was stable at 203 GiB. The path forward was clear: understand why SmallVec regresses on Zen4, fix or revert it, and reclaim the 5.5 seconds that stood between 94.4 seconds and the 88.9-second baseline.

The 203-GiB data point was not the end of the story—it was the confirmation that the team was on the right track.