The Moment of Deployment: Starting the Parallel Synthesis Daemon

cd /home/theuser/curio/extern/cuzk && \
  FIL_PROOFS_PARAMETER_CACHE=/data/zk/params \
  RUST_LOG=info \
  nohup ./target/release/cuzk-daemon \
    --config /tmp/cuzk-parallel.toml \
    > /tmp/cuzk-parallel-stdout.log 2>/tmp/cuzk-parallel.log &
echo "daemon pid=$!"

At first glance, message [msg 1869] appears to be the most mundane kind of developer action: restarting a daemon with a new configuration file. A nohup process, a redirected log, a PID echoed to the terminal. Yet this single bash command represents a critical inflection point in a multi-session engineering effort to optimize the cuzk Groth16 proof generation pipeline for Filecoin's Proof-of-Replication (PoRep). The command is the culmination of a design and implementation cycle that began with a waterfall timeline visualization, passed through a deep architectural refactoring of the engine's concurrency model, and ended with the deployment of a parallel synthesis mechanism designed to eliminate a structural GPU idle gap. This article examines that message in detail: the reasoning that produced it, the decisions encoded in its flags and environment variables, the assumptions it carries, and the benchmark results that followed.

The Waterfall That Revealed the Gap

The story behind this command begins with a diagnostic tool. In the preceding messages, the engineer had implemented a "waterfall timeline instrumentation" system that recorded wall-clock timestamps for every synthesis start, synthesis end, GPU pickup, and GPU completion event in the proving pipeline. When rendered as a text-based timeline, the output was unambiguous: the GPU was idle for approximately 12 seconds per proof cycle.

The root cause was architectural. The cuzk engine's synthesis task ran as a single tokio::spawn loop that pulled proof requests from a scheduler, called process_batch().await, and blocked until the CPU-intensive synthesis completed before pulling the next request. The GPU, meanwhile, processed each proof in about 27 seconds, but synthesis took roughly 39 seconds. Because synthesis was strictly sequential—only one proof could be synthesized at a time—the GPU would finish proof N and then wait 12 seconds for synthesis of proof N+1 to complete. GPU utilization sat at 70.9%, with the remaining 29% representing pure structural idle time.

The waterfall visualization made the pattern unmistakable:

P1  SSSSSSSSSGGGGGGGG
P2           SSSSSSSSSSSGGGGGGGG
P3                      SSSSSSSSSSSGGGGGGGG

Each proof's synthesis block started only after the previous proof's synthesis ended. The pipeline's "lookahead" channel (capacity 1) was insufficient to bridge the gap because there was only one synthesis task producing work.

Design Decisions: Semaphore Over Spawn

The engineer considered several approaches to closing the GPU idle gap. The simplest would have been to spawn multiple independent synthesis tasks, each pulling from the scheduler. But this raised questions about backpressure, error handling, and resource contention. The chosen solution—a tokio::sync::Semaphore—was elegant in its simplicity.

The design, articulated in [msg 1860], worked as follows: the synthesis loop would acquire a semaphore permit before spawning a process_batch call as a fire-and-forget tokio task. If the semaphore had no available permits (because synthesis_concurrency tasks were already running), the loop would block until one completed. This provided natural backpressure: the GPU channel's bounded capacity would eventually stall the synthesis tasks if the GPU fell behind, while the semaphore prevented runaway memory consumption from too many concurrent syntheses.

A critical design discussion occurred when the engineer asked: "Can parallel synthesis replace batching?" The engine had two concurrency mechanisms: max_batch_size (which accumulated multiple proofs into a single GPU call) and the new synthesis_concurrency. The analysis showed that with synthesis_concurrency=2, a new proof would arrive at the GPU every ~20 seconds (39s / 2), which was less than the GPU's 27-second processing time. The GPU would never idle. Batching, by contrast, achieved its throughput improvement by amortizing GPU fixed costs over multiple proofs, but the data showed marginal benefit on this hardware. The conclusion: parallel synthesis subsumed the throughput benefit of batching, and max_batch_size=1 could become the recommended default.

The Implementation Trail

The code changes spanned three files. In cuzk-core/src/config.rs, a new synthesis_concurrency field was added to PipelineConfig with a default value of 1 (preserving backward compatibility). In cuzk-core/src/engine.rs, the synthesis task loop was refactored: instead of awaiting process_batch directly, the loop now acquired a semaphore permit and spawned the synthesis work as a separate tokio task. The process_batch function itself was extracted into a standalone async function that could be spawned independently. In cuzk.example.toml, the new parameter was documented for operators.

The build succeeded cleanly ([msg 1865]), with only pre-existing warnings from the bellperson dependency. The bench tool also compiled successfully ([msg 1866]). The old daemon was killed ([msg 1867]), a new configuration file was written with synthesis_concurrency=2 ([msg 1868]), and then came the subject message: the moment of deployment.

What the Command Encodes

The bash command in [msg 1869] is dense with meaning. The FIL_PROOFS_PARAMETER_CACHE environment variable points to the directory containing the SRS (Structured Reference String) parameters—approximately 44 GiB of elliptic curve data that must be loaded into GPU memory before proving can begin. The RUST_LOG=info flag controls the tracing subscriber's verbosity. The nohup invocation and output redirections reflect the daemon's production deployment model: it runs as a background process, with stdout capturing structured tracing events and stderr capturing the new timeline instrumentation events.

The --config /tmp/cuzk-parallel.toml flag is the heart of the experiment. That configuration file, written in the preceding message, contains the critical line synthesis_concurrency = 2. This single parameter transforms the engine's concurrency model from a single-threaded synthesis pipeline to a parallel one, allowing two proofs to be synthesized simultaneously on the machine's 96 CPU cores.

The PID echoed back—daemon pid=38109—confirms the process launched successfully. The engineer then waited 30 seconds for SRS loading and engine initialization before proceeding to benchmark.

Assumptions Carried Forward

This deployment carried several assumptions that would soon be tested. The first was that the machine's 754 GiB of RAM could sustain two concurrent syntheses, each holding approximately 136 GiB of intermediate data (10 partitions per proof). Earlier memory benchmarks had shown a peak of 371 GiB with two concurrent clients, suggesting headroom, but the exact memory profile of dual synthesis had not been measured.

The second assumption was that CPU contention would not become a new bottleneck. The machine had 96 cores, and the GPU's proving step included CPU-bound work (notably the b_g2_msm multi-scalar multiplication). Running two syntheses simultaneously would consume CPU cores that the GPU prover also needed. The engineer was aware of this risk but chose to measure rather than speculate.

The third assumption was that the semaphore-based approach would not introduce deadlocks or starvation. The bounded GPU channel provided a natural pressure valve: if the GPU fell behind, the channel would fill, and synthesis tasks would block on send, which would cause the semaphore to release permits, which would prevent new syntheses from starting. This feedback loop was theoretically sound but untested.

What Followed

The benchmark results, visible in [msg 1871], were nuanced. With synthesis_concurrency=2 and client concurrency -j 3, GPU utilization jumped to 99.3%, effectively eliminating the idle gap. However, the overall throughput improvement was modest—approximately 5-7%, from ~45.3 seconds per proof to ~42.2 seconds. The waterfall timeline revealed why: CPU contention. Running two full 10-partition syntheses simultaneously competed with the GPU prover's CPU-intensive steps, inflating both synthesis and GPU times. Over-aggressive client concurrency (-j 4) led to catastrophic contention at 60.2 seconds per proof, while insufficient depth (-j 2) left the pipeline starved.

The engineer had successfully saturated the GPU, only to discover that the bottleneck had shifted from the GPU to the CPU. The system's 96 cores were a shared resource that could not be fully dedicated to synthesis without impacting the GPU's CPU-bound work. This is the law of diminishing returns in pipeline optimization: each intervention reveals the next constraint.

Conclusion

Message [msg 1869] is a single bash command, but it represents the culmination of a rigorous diagnostic and design cycle. The waterfall instrumentation provided the data; the semaphore-based parallel synthesis provided the mechanism; the configuration file provided the control. The command itself is the bridge between theory and measurement, between code and reality. Its modest syntax—a cd, an environment variable, a nohup—belies the depth of engineering judgment it encodes. And the benchmark results that followed, while not the unqualified success the engineer might have hoped for, revealed the next frontier: reducing the absolute CPU time of synthesis through the Phase 5 Wave 2/3 optimizations that would become the focus of subsequent work.