The First CUZK_TIMING Breakdown: How Printf Buffering Almost Derailed a Performance Diagnosis

In the systematic hunt for the Phase 4 performance regression in the cuzk SNARK proving engine, the assistant had reached a critical juncture. The A2 pre-sizing optimization had been fully reverted from pipeline.rs, the build was clean, and the instrumented CUDA code—sprinkled with CUZK_TIMING printf statements throughout groth16_cuda.cu—was compiled into the daemon binary. Everything was in place for the first end-to-end test that would reveal, at last, where the 17-second regression from the 88.9-second baseline was hiding.

But software performance engineering has a way of humbling even the most meticulous plans. The first test ran, the proof completed in 102.7 seconds, and the daemon log was full of useful information—except for the one thing the assistant needed most. The CUZK_TIMING lines were nowhere to be found.

The Vanishing Printf

The assistant had confirmed, through multiple rounds of verification, that the instrumentation was compiled into the binary. Running strings on the libgroth16_cuda.a archive showed all six timing messages: pin_abc_ms, prep_msm_ms, ntt_msm_h_ms, batch_add_ms, tail_msm_ms, and b_g2_msm_ms. The daemon binary itself contained the strings. The CUDA source code had the printf calls. Yet when the test ran and the log was inspected, the output was silent on CUZK_TIMING ([msg 930]).

The assistant's first hypothesis was that the output was going to a different file descriptor. But checking /proc/<pid>/fd/1 and /proc/<pid>/fd/2 confirmed that both stdout and stderr were redirected to the log file. The CUDA printf should have appeared there.

The root cause was a classic C stdio pitfall: full buffering. When stdout is redirected to a file (rather than a terminal), the C runtime switches from line-buffered to fully-buffered mode. The printf output accumulates in a 4KB (or larger) buffer and is only flushed when the buffer fills or the program exits. In the CUDA host code, the printf calls were writing to stdout, but the buffer was never explicitly flushed. The daemon process, which runs continuously, never exited after the proof completed, so the buffer remained unflushed ([msg 935]).

The fix was straightforward but essential: add fflush(stderr) after each CUZK_TIMING printf call. The assistant edited all six printf locations in groth16_cuda.cu to include the flush (<msg id=938-944>). This is the kind of debugging that looks trivial in hindsight but can consume hours of confusion if not caught early.

The Build That Almost Didn't Recompile

With the fflush fix applied, the assistant needed to rebuild. But here the build system added its own twist. The supraseal-c2 package uses a build.rs script to invoke nvcc for CUDA compilation, and the resulting .a archive lives outside the standard Cargo output directory in target/release/build/supraseal-c2-*/out/. Simply running cargo build after editing the .cu file did trigger a recompilation—the file size changed from 2,791,224 bytes to 2,791,536 bytes—but the output was subtle enough that the assistant initially doubted it had worked (<msg id=953-954>).

This moment highlights a recurring theme in the session: the build system is a first-class component of the debugging workflow. When working with mixed-language projects that involve Cargo build scripts, CUDA compilers, and custom output directories, verifying that changes have actually been compiled requires more than a glance at the build log. The assistant had to check file sizes, run strings on the archive, and verify the daemon binary's timestamp before proceeding with confidence.

The First CUZK_TIMING Breakdown

With the flushed build ready, the assistant started a fresh daemon, loaded the 44 GiB SRS parameter file, and ran the single-proof test. This time, the output was clear:

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 revelatory. The total proof time was 101.3 seconds, with synthesis at 61.0 seconds and the GPU wrapper at 40.1 seconds. But the individual GPU phase breakdown told a stark story:

• pin_abc (B1 — cudaHostRegister): 5,733 ms. The optimization that was supposed to cost 150–300 ms was actually taking 5.7 seconds. Pinning ~125 GiB of host memory (10 circuits × 3 arrays × ~4.17 GiB each) required touching every page, and mlock-adjacent operations on that scale dominated the GPU phase overhead.
• ntt_msm_h: 21.4 seconds. The core GPU compute was fast—the NTT and H-polynomial MSMs completed in 21.4 seconds, which was actually faster than the baseline's total GPU time of ~34 seconds.
• b_g2_msm: 22.8 seconds. The B_G2 MSMs ran on the CPU side, parallel to the GPU work. Thanks to the A4 parallelization, this ran concurrently and finished before the GPU, so it was not on the critical path.
• GPU internal total: 24.1 seconds. The pure GPU path (NTT + batch_add + tail) was 24.1 seconds—a full 10 seconds faster than the baseline's 34-second GPU phase. The verdict was immediate and unambiguous: B1 (cudaHostRegister) was the primary culprit, adding 5.7 seconds of overhead for a DMA bandwidth benefit that was negligible in the single-proof case. The assistant's analysis estimated the overhead was 19× worse than the original projection of 150–300 ms ([msg 968]).

Reverting B1 and Measuring the Impact

The assistant moved quickly to revert B1. The pinning code—both the cudaHostRegister calls at the start of generate_groth16_proofs_c and the cudaHostUnregister calls at the end—was removed from groth16_cuda.cu (<msg id=969-973>). After rebuilding and running two proofs, the results were consistent:

| Metric | Baseline | With B1 | Without B1 (Run 1) | Without B1 (Run 2) | |---|---|---|---|---| | Total | 88.9s | 101.3s | 94.4s | 94.0s | | Synthesis | 54.7s | 61.0s | 60.3s | 60.5s | | GPU (wrapper) | 34.0s | 40.1s | 33.8s | 33.2s | | GPU (internal) | n/a | 24.1s | 25.8s | 25.4s | | Peak RSS | 202.9 GiB | 203.1 GiB | 203.1 GiB | — |

The GPU wrapper time dropped from 40.1 seconds to 33.2–33.8 seconds, essentially matching or slightly beating the baseline of 34.0 seconds. The CUDA internal time of 25.4–25.8 seconds was fast and healthy. The gap between the wrapper time (33.2s) and the CUDA internal time (25.4s) was explained by the b_g2_msm (23.5s) running in parallel with GPU compute—it finished just before the GPU, adding some synchronization overhead but not on the critical path.

But the synthesis time remained stubbornly elevated at 60.3–60.5 seconds, compared to the 54.7-second baseline. With A2 already reverted and B1 now removed, the only remaining Phase 4 change affecting synthesis was A1 (SmallVec). The assistant was facing an unexpected finding: a change designed to eliminate heap allocations and speed up synthesis was making it ~10.5% slower.

Building a Synth-Only Microbenchmark

To isolate the synthesis regression without the overhead of GPU proving and SRS loading, the assistant built a dedicated synth-only microbenchmark subcommand in cuzk-bench (<msg id=1000-1012>). This was a pivotal engineering decision. Instead of waiting 90+ seconds for each end-to-end test (including SRS load, GPU prove, and daemon startup), the microbenchmark could run three iterations of synthesis in roughly the same time as a single full proof.

The implementation required:

1. Adding cuzk-core as an optional dependency to cuzk-bench/Cargo.toml
2. Adding a synth-bench feature flag
3. Adding the SynthOnly command variant to the CLI
4. Implementing the handler that calls synthesize_porep_c2_batch directly, bypassing the daemon and GPU The microbenchmark loaded the C1 output from disk, called the synthesis function directly, and reported per-iteration timing. It was a textbook example of building the right tool for the diagnosis—a principle that recurs throughout this session.

The SmallVec A/B Test

With the microbenchmark ready, the assistant ran four configurations, each with three iterations:

| Configuration | Iteration 1 | Iteration 2 | Iteration 3 | Average | |---|---|---|---|---| | Vec (original) | 54.5s | 54.5s | 54.5s | 54.5s | | SmallVec cap=1 | 59.6s | 59.5s | 59.9s | 59.7s | | SmallVec cap=2 | 60.0s | 60.0s | 60.0s | 60.0s | | SmallVec cap=4 | 60.2s | 60.2s | 60.2s | 60.2s |

The results were remarkably consistent and conclusive. Vec completed synthesis in 54.5 seconds, while every SmallVec variant—regardless of inline capacity—was 5–6 seconds slower. The inline capacity had almost no effect: cap=1 averaged 59.7s, cap=2 averaged 60.0s, and cap=4 averaged 60.2s. The slight upward trend with larger capacity was within noise.

This was a surprising result. SmallVec is designed to eliminate heap allocations for small vectors by storing elements inline. For SHA-256 circuits where most linear combinations have 1–3 terms, the expectation was that SmallVec would avoid millions of heap allocations and improve performance. Instead, it degraded it.

Why Is SmallVec Slower?

The assistant's analysis turned to the hardware. On an AMD Threadripper PRO 7995WX (Zen4, 96 cores), the cache hierarchy is generous: 32 KB L1d per core, 1 MB L2 per core, and up to 384 MB of L3 shared across the chip. But the SmallVec change increased the stack footprint of each Indexer struct dramatically:

• Vec: 24 bytes per Indexer (pointer + length + capacity), with the actual data on the heap
• SmallVec cap=1: ~56 bytes per Indexer (40 bytes inline + 8 length + 1 discriminant + padding)
• SmallVec cap=4: ~170 bytes per Indexer (160 bytes inline + 8 length + 1 discriminant + padding) During synthesis, each enforce() call creates 3 LinearCombination objects, each containing 2 Indexer structs (one for variables, one for coefficients). That's 6 Indexers per enforce() call, called millions of times across 10 partitions running in parallel via rayon. With cap=4, the hot stack data per enforce() call jumped from ~144 bytes (Vec) to ~1020 bytes (SmallVec). The larger stack frames could cause: 1. Increased L1 data cache misses: The working set of each rayon thread's hot stack grows, evicting other useful data 2. Stack growth overhead: Rayon's work-stealing scheduler may need to allocate more stack space per thread 3. Register pressure: The compiler may spill more registers to stack with larger structs But the fact that cap=1 was just as slow as cap=4 suggested the issue wasn't simply about cache line pressure. If the regression were purely about stack footprint, cap=1 (56 bytes per Indexer, fitting in a single 64-byte cache line) should have performed closer to Vec. The consistent 5–6 second penalty across all capacities pointed to a deeper issue—perhaps related to how SmallVec interacts with the compiler's optimization decisions, or how the smallvec! macro and SmallVec's branching logic affect code generation in the hot loop. The assistant prepared to gather low-level perf stat hardware counters—L1/L2/L3 cache misses, branch mispredicts, instructions per cycle (IPC)—on a single-partition run to understand the root cause (<msg id=1036-1038>). This would be the next step in the diagnosis.

Themes and Lessons

This chunk of the session exemplifies several enduring themes in disciplined performance engineering:

Instrumentation is only useful if you can read it. The CUZK_TIMING printf's were compiled into the binary, but C stdio buffering made them invisible. The fflush fix was a one-line change per printf, but without it, the entire instrumentation investment would have been wasted. This is a reminder that observability infrastructure must be tested end-to-end before it can be trusted.

Build systems are part of the debugging loop. In a multi-language project with Rust, CUDA, and custom build scripts, verifying that changes have actually been compiled requires explicit checks. The assistant learned to check file sizes, run strings on archives, and verify binary timestamps—a small but essential discipline.

Microbenchmarks accelerate diagnosis. The synth-only microbenchmark reduced iteration time from ~90 seconds to ~60 seconds for three runs, and eliminated the variance from GPU timing and SRS loading. This allowed the assistant to run four configurations with three iterations each in rapid succession, producing a clear and conclusive result.

Not all optimizations optimize. The SmallVec change was well-intentioned and theoretically sound, but on the actual hardware and workload, it caused a 10.5% regression. The assistant's willingness to test, measure, and accept the counterintuitive result is the hallmark of data-driven performance engineering.

The regression is now isolated. After reverting A2 and B1, and identifying A1 (SmallVec) as the cause of the remaining 5–6 second synthesis slowdown, the assistant has a clear target. The next step—gathering perf stat hardware counters—will reveal why SmallVec is slower on this AMD Zen4 system, guiding the path to a fix or a decision to revert A1 entirely.

References

[1] "The Blueprint in the Room: How Two Documents Shaped the Diagnosis of a Performance Regression in the cuzk SNARK Proving Engine" — [chunk 13.1] [2] "The Art of the Go-Ahead: How a Single Sentence Unlocked Autonomous Debugging in a Complex CUDA Optimization Pipeline" — [chunk 13.1] [3] "The Diagnostic Prelude: How Two Git Commands Anchored a Performance Regression Investigation" — [chunk 13.1] [4] "The Pause Before the Diagnosis: A Status Summary at the Crossroads of Performance Engineering" — [chunk 13.1] [5] "The Weight of a Single Word: How 'proceed' Anchors a Performance Engineering Crisis" — [chunk 13.1] [6] "The Turning Point: A Systematic Plan Meets Performance Regression" — [chunk 13.1] [7] "The Grep That Saved a Performance Investigation: Systematic Regression Diagnosis in the cuzk SNARK Prover" — [chunk 13.1] [8] "The Surgical Read: Diagnosing a Performance Regression One Line at a Time" — [chunk 13.1] [9] "The Art of the Targeted Revert: Diagnosing a Performance Regression One Edit at a Time" — [chunk 13.1] [10] "The Art of the Clean Revert: How One Grep Command Reveals the Discipline Behind Performance Engineering" — [chunk 13.1] [11] "The Discipline of Reversion: Cleaning Up After a Failed Optimization" — [chunk 13.1] [12] "The Checkpoint: A Single Status Update in a Systematic Regression Hunt" — [chunk 13.1] [13] "The Build That Didn't Recompile: A Lesson in Incremental Performance Debugging" — [chunk 13.1] [14] "The Build Checkpoint: A Pivot Point in Performance Regression Diagnosis" — [chunk 13.1] [15] "The Build That Wasn't: A Lesson in CUDA Compilation Verification" — [chunk 13.1] [16] "The Touch That Didn't Recompile: Build System Nuances in CUDA Performance Engineering" — [chunk 13.1] [17] "The Wrong Package: A Build System Micro-Moment in Performance Regression Diagnosis" — [chunk 13.1] [18] "The Silent Read: Navigating Build System Complexity in a Multi-Language Rust Workspace" — [chunk 13.1] [19] "A Moment of Discovery: Tracing a Build Failure Through Cargo Features" — [chunk 13.1] [20] "The Art of the Build: How Reading a Cargo.toml Unlocked a Performance Regression Diagnosis" — [chunk 13.1] [21] "The Build That Almost Wasn't: Diagnosing Feature Flags in a Performance Regression Hunt" — [chunk 13.1] [22] "The Build System Detective: Forcing CUDA Recompilation in a Complex Multi-Repo Rust Workspace" — [chunk 13.1] [23] "The Hidden Build Artifact: Diagnosing CUDA Compilation in a Multi-Language Proving Pipeline" — [chunk 13.1]