The Opening Move of a Systematic Sweep: Benchmarking partition_workers for Optimal GPU Throughput

Introduction

In the middle of an intense optimization campaign for the cuzk SNARK proving engine—a system responsible for generating Filecoin Proof-of-Replication (PoRep) Groth16 proofs—the assistant receives a succinct command from the user: "sweep 10,12,15,18,20". This is not a casual request. It is the culmination of a multi-phase engineering effort spanning seven prior phases of architectural redesign, each targeting a specific bottleneck in a pipeline that consumes ~200 GiB of peak memory and spans Go, Rust, and CUDA code. The user wants to know: what is the optimal partition_workers setting for the newly implemented Phase 8 dual-worker GPU interlock?

The subject message ([msg 2251]) is the first concrete action in response to that request. It is a deceptively simple message: the assistant writes a configuration file with partition_workers = 10, starts the daemon, and records the PID. But this message sits at a critical juncture—it represents the transition from design and implementation to empirical validation, from theoretical optimization to data-driven tuning. Understanding this message requires understanding the entire trajectory that led to it, the reasoning behind the sweep methodology, the operational assumptions baked into the approach, and the knowledge that this single benchmark point will generate.

Context: The Optimization Campaign That Preceded This Message

To understand why this message exists, one must appreciate the engineering context. The cuzk proving engine is a high-performance system for generating Groth16 zk-SNARK proofs for Filecoin storage proofs. The system had been through seven prior phases of optimization:

• Phase 1-5: Established the baseline pipeline architecture, implemented slotted partition processing, diagnosed GPU idle gaps, and introduced parallel synthesis.
• Phase 6: Implemented a slotted partition pipeline for memory reduction (71 GiB vs 228 GiB peak).
• Phase 7: Designed and implemented per-partition dispatch architecture, which improved throughput but revealed GPU utilization gaps caused by static mutex contention in the C++ CUDA kernel.
• Phase 8 (just committed): The dual-worker GPU interlock—a surgical refactoring of the C++ static mutex in generate_groth16_proofs_c to cover only the CUDA kernel region (NTT+MSM, batch additions, tail MSMs), while CPU preprocessing and b_g2_msm run outside the lock. This allowed two GPU workers per device to interleave: one does CPU work while the other runs CUDA kernels. The Phase 8 implementation had already been benchmarked with partition_workers = 20 (the previous sweet spot), yielding 44.0s/proof at concurrency 5 jobs 3—a 13.2% improvement over Phase 7's 50.7s/proof. A quick test with partition_workers = 30 had regressed to 60.4s/proof due to CPU contention from 30 simultaneous synthesis workers starving GPU preprocessing threads. But the user recognized that the optimal partition_workers value might have shifted with Phase 8's architectural changes. The dual-worker interlock changed the CPU/GPU resource balance: now two GPU workers per device compete for CPU preprocessing resources, whereas before there was only one. The old sweet spot of pw=20 might no longer be optimal. The user's request—"sweep 10,12,15,18,20"—was a demand for empirical data across a range of values to find the new optimum.

The Subject Message: Anatomy of a Benchmark Point

The message itself is straightforward in structure but rich in meaning. It consists of two tool calls:

1. A heredoc write that creates a complete TOML configuration file at /tmp/cuzk-sweep.toml. This config specifies all parameters for the daemon: the listen address, SRS parameter cache path and preload list, GPU configuration (empty device list with 2 workers per device), synthesis settings (0 threads = auto, partition_workers = 10), pipeline settings (enabled, lookahead=3, concurrency=1), and logging level.
2. A daemon launch that starts the cuzk-daemon with this config in the background via nohup, redirecting output to a log file, and echoes the PID. The assistant then waits—the next message ([msg 2252]) will poll for the "cuzk-daemon ready" signal before running the benchmark.

Why This Message Was Written: The Reasoning and Motivation

The motivation is multi-layered. At the surface level, the assistant is following the user's instruction to sweep five partition_workers values. But the deeper reasoning reveals several important design decisions:

First, the assistant chooses to restart the daemon for each configuration rather than hot-reloading the parameter. This is a deliberate trade-off. Hot-reloading partition_workers would require the engine to reconfigure its thread pool while running, which is complex, error-prone, and could introduce measurement noise from in-flight work. Restarting the daemon is simpler and ensures a clean state for each benchmark. The cost is time—each restart requires waiting for SRS preload, which takes ~30-60 seconds—but the benefit is measurement integrity.

Second, the assistant writes a full config file for each sweep point rather than using command-line overrides. This ensures reproducibility. Each config file is a complete snapshot of the daemon's configuration, so if a later benchmark produces anomalous results, the exact config can be inspected. The assistant also names each log file distinctly (/tmp/cuzk-sweep-pw10.log, pw12.log, etc.), creating an audit trail.

Third, the assistant chooses c=5 j=3 (5 proofs with concurrency 3) as the benchmark workload. This is the standard throughput benchmark used throughout the optimization campaign. It stresses the system with enough work to measure steady-state throughput while being short enough to run repeatedly. The concurrency of 3 means three proofs are in-flight simultaneously, which exercises the dual-worker GPU interlock by creating overlapping GPU work across multiple sectors.

Fourth, the assistant uses a temporary config file at /tmp/cuzk-sweep.toml rather than modifying the project's example config. This isolates the sweep from any accidental changes to the repository's configuration files and makes cleanup trivial.

Assumptions Made by the Assistant

Several assumptions underpin this message and the sweep methodology:

1. The benchmark is deterministic enough that a single run per configuration is sufficient. The assistant does not plan to run multiple trials per pw value and average them. This assumes that the cuzk daemon's throughput is stable across runs with the same configuration, which is reasonable for a dedicated benchmarking machine but ignores potential variance from OS scheduling, thermal throttling, or memory bandwidth contention.
2. The order of sweep points does not matter. The assistant starts at pw=10 and works upward. This assumes no hysteresis effects—that running pw=10 first does not influence pw=12's result (e.g., through GPU temperature buildup or memory fragmentation). Starting from the lowest value and working up is a reasonable choice to avoid thermal accumulation from higher CPU loads earlier.
3. The daemon restart + SRS preload is fast enough to complete within the benchmark session. The assistant budgets ~30-60 seconds per restart. This assumption is tested repeatedly in the subsequent messages, where several restarts time out (the while ! grep -q "cuzk-daemon ready" loop has a 120-second timeout in the bash tool). The assistant does not anticipate the operational hiccups that will occur.
4. The sed command is reliable for modifying the config file between sweeps. This assumption proves incorrect—multiple times in the subsequent messages, the sed substitution fails because the previous command (which included pkill) terminates before sed runs, or because the string pattern doesn't match. The assistant has to learn to separate the pkill and sed into distinct tool calls.
5. The benchmark results will show a clear optimum. The assistant expects that one pw value will be significantly better than others, providing a clear recommendation. In reality, the results are tightly clustered (43.5-44.9s/proof), requiring careful interpretation.

Mistakes and Incorrect Assumptions

While the message itself is technically correct, the sweep methodology it inaugurates encounters several problems in subsequent messages:

1. The pkill + sed + daemon launch in a single bash call is fragile. When the assistant tries to chain pkill -f cuzk-daemon; sleep 2; sed -i ...; nohup ... in a single command ([msg 2255]), the pkill terminates the daemon but the subsequent commands may not execute if the bash tool's process group is affected. The assistant learns to split these into separate tool calls.
2. The sed substitution relies on the previous value being present in the file. When the assistant tries sed -i 's/partition_workers = 10/partition_workers = 12/' but the file already contains 12 from a previous failed attempt, the substitution silently does nothing. The assistant discovers this when checking the config and finding the wrong value ([msg 2259]).
3. The timeout on the SRS preload wait loop is too tight. The while ! grep -q "cuzk-daemon ready" loop times out at 120 seconds multiple times ([msg 2256], [msg 2269], [msg 2280]), usually because the daemon failed to start (the nohup command was part of a previous timed-out command and never executed). The assistant recovers by checking for the log file and restarting manually. These are not failures of the subject message itself, but they reveal assumptions about tool execution semantics that the assistant must learn through trial and error.

Input Knowledge Required to Understand This Message

To fully grasp what is happening here, a reader needs:

1. Understanding of the cuzk proving engine architecture: That partition_workers controls how many CPU threads are used to synthesize circuit partitions in parallel, and that this directly competes with GPU preprocessing threads for CPU resources.
2. Knowledge of the Phase 8 dual-worker interlock: That two GPU workers per device now share a C++ mutex, interleaving CPU preprocessing and CUDA kernel execution. This changes the CPU resource profile compared to Phase 7's single-worker design.
3. Familiarity with the benchmark methodology: That c=5 j=3 means 5 proofs with concurrency 3, and that throughput is measured as total wall time divided by 5 proofs. The assistant has been using this benchmark throughout the optimization campaign.
4. Awareness of the hardware platform: A 96-core AMD Zen4 machine with an RTX 5070 Ti GPU. The optimal partition_workers value is highly platform-dependent—a machine with fewer cores would have a lower sweet spot.
5. Understanding of the SRS preload mechanism: The Structured Reference String (SRS) is a large (~30+ GiB) parameter file that must be loaded into GPU memory before proving can begin. This preload takes significant time and is why the assistant waits for the "cuzk-daemon ready" signal before benchmarking.

Output Knowledge Created by This Message

This message creates several forms of knowledge:

1. A reproducible configuration for pw=10: The complete TOML file is written to disk, serving as both the configuration for this benchmark point and a template for subsequent points. Any engineer can inspect this file to see exactly what parameters were used.
2. A log file at /tmp/cuzk-sweep-pw10.log that will contain the daemon's startup sequence, SRS preload timing, and eventually the GPU timeline events for the benchmark. This is the raw data source for post-hoc analysis.
3. A benchmark result (determined in the next message, [msg 2253]): pw=10 yields 43.5s/proof, which ties with pw=12 for the best throughput in the sweep. This is the key empirical finding.
4. A methodological template: The pattern established here—write config, kill daemon, start daemon, wait for ready, run benchmark, record result—is replicated for pw=12, 15, 18, and 20. The assistant learns from the operational failures in early iterations and refines the approach.
5. Evidence for the optimal range: The full sweep reveals that pw=10-12 is the optimal range for this 96-core machine, with pw=10 and pw=12 both achieving 43.5s/proof, pw=18 at 43.8s/proof, and pw=15 and pw=20 showing slight regressions to 44.8s/proof and 44.9s/proof respectively. This is a narrow band—only ~3% variation across the entire sweep—but it confirms that lower partition counts (10-12) are preferable with the Phase 8 dual-worker interlock, compared to pw=20 which was optimal in Phase 7.

The Thinking Process Visible in the Message

While the message does not contain explicit reasoning (it is purely action-oriented), the thinking process is visible in the structure of the config file and the sequencing of operations:

• The config file includes all parameters, not just partition_workers. This shows the assistant is thinking about reproducibility and isolation. By writing a complete config, the assistant ensures that no stale parameters from a previous config file leak into the benchmark.
• The assistant uses nohup and backgrounding. This indicates awareness that the daemon must persist beyond the lifetime of the bash tool invocation. The daemon runs as a long-lived server process, not a one-shot command.
• The log file is named with the pw value. This is a deliberate organizational choice that makes it easy to correlate logs with configurations. The assistant is thinking ahead to analysis time.
• The assistant echoes the PID. This is a debugging affordance—if the daemon needs to be killed or inspected, the PID is immediately available.
• The config uses devices = [] and gpu_threads = 0. These settings tell the engine to auto-detect GPUs and auto-configure thread counts. The assistant is trusting the auto-detection rather than hardcoding device indices, which is appropriate for a sweep that might be run on different hardware.

Conclusion

The subject message ([msg 2251]) is the first step in a systematic empirical investigation that closes out Phase 8 of the cuzk optimization campaign. It is a message that bridges design and measurement—taking a theoretical improvement (the dual-worker GPU interlock) and subjecting it to rigorous benchmarking across a parameter sweep. The assistant's approach reflects an engineering mindset: isolate variables, control the environment, create audit trails, and let data drive decisions.

The message itself is simple—a config file write and a daemon launch—but it carries the weight of the entire optimization campaign behind it. It represents the shift from "does it work?" to "what's the best setting?" and from "we improved it" to "we know exactly how much and under what conditions." The sweep that begins here will produce a clear result: pw=10-12 is the optimal range, providing a 13-17% improvement over Phase 7 with the dual-worker interlock, and establishing the production configuration for the cuzk proving engine.

In the broader narrative of the coding session, this message is the final act of validation. After seven phases of architectural redesign, hundreds of lines of C++, Rust, and Go code, and countless benchmark runs, the assistant is answering the last question: what is the right knob setting for production?