Verification Under Load: Confirming Ordered Synthesis in the cuzk Proving Pipeline

Introduction

In the high-stakes world of zero-knowledge proof generation, where proofs for Filecoin's consensus mechanisms can consume hundreds of gigabytes of memory and hours of compute time, the difference between a smoothly functioning pipeline and a stalled cluster often comes down to scheduling discipline. Message [msg 2838] captures a pivotal moment in the development of the cuzk CUDA ZK proving daemon: the live verification that a newly implemented ordered synthesis scheduler is working correctly under real workload conditions. This message is not merely a status check—it is a deliberate experimental observation designed to confirm that a fundamental architectural fix has resolved a critical performance pathology.

The Message

The assistant writes:

Both jobs are submitted and SRS is loading from disk (44 GiB). This will take a minute or two. Let me wait and then check the synthesis progress:

>

``bash sleep 60 && ssh -o ConnectTimeout=10 -p 40612 root@141.0.85.211 'curl -sf --max-time 3 http://127.0.0.1:9821/status | python3 -m json.tool' ``

>

``json { "uptime_secs": 131.942684893, "memory": { "total_bytes": 429496729600, "available_bytes": 51410319176 }, "synthesis": { "max_concurrent": 44, "active": 18 }, "pipelines": [ { "job_id": "bd3aad5d-1dcb-45ab-8a61-e64276865ac5", "proof_kind": "porep-c2", "started_secs_ago": 63.083767326, "total_partitions": 10, "partitions_done": 0, ... } ], "gpu_workers": [...] } ``

At first glance, this looks like a routine status poll. But in the context of the preceding development work, every field in this JSON payload tells a story about a system that was, until very recently, malfunctioning in a subtle and damaging way.

The Problem That Preceded This Message

To understand why message [msg 2838] matters, we must understand the bug it was designed to confirm had been fixed. In the previous segment of work ([msg 2817] through [msg 2837]), the team had identified a fundamental scheduling problem in the cuzk proving pipeline. The original design dispatched all partitions from all jobs as independent tokio tasks racing on a Notify-based budget acquire mechanism. This caused a "thundering herd" problem: when budget became available, all waiting partitions would wake up simultaneously and compete for resources. The result was that partitions from different jobs would be interleaved arbitrarily, causing all active pipelines to make slow progress simultaneously rather than completing jobs one at a time. This meant that no single proof would finish quickly—every job stalled together in a state of partial completion.

The fix, implemented but not yet deployed at the time of the previous messages, replaced the per-partition tokio::spawn approach with an mpsc::channel-based ordered scheduler. In this new design, a synthesis worker pool pulls partitions from a FIFO queue, ensuring that partitions from earlier jobs are processed before later ones. This is a classic scheduling insight: for batch workloads where completing any single job is valuable, FIFO ordering often outperforms fair interleaving because it maximizes job completion rate rather than partition-level fairness.

What This Message Reveals

The status response in message [msg 2838] contains several critical data points that together confirm the fix is working:

Memory pressure confirms the budget system is operational. The status shows used_bytes implicitly at approximately 378 GiB (calculated from 400 GiB total minus 51 GiB available). This is exactly what the budget-based memory manager should be doing: allowing synthesis to consume nearly all of the 400 GiB budget while keeping a small reserve. The fact that 378 GiB is in use confirms that the SRS parameters (44 GiB) have been loaded and that synthesis is actively allocating working memory for 18 concurrent partition synthesis tasks.

The dynamic max_concurrent calculation is correct. The synthesis.max_concurrent field shows 44, which matches the expected value computed from the budget (400 GiB) divided by the per-partition working memory for SnapDeals (~9 GiB). This confirms that the code change replacing the static synthesis_concurrency config parameter with a budget-based computation is functioning correctly.

Only one pipeline is active. This is the most important signal. The pipelines array contains exactly one entry—the first job with ID bd3aad5d-1dcb-45ab-8a61-e64276865ac5. The second job, which was submitted concurrently, is not visible in the pipeline list. This is precisely the behavior expected from the ordered FIFO scheduler: the second job's partitions are queued behind the first job's partitions and will not begin synthesis until the first job's partitions have been processed. Under the old Notify-based system, both jobs would have appeared in the pipeline list simultaneously, each making partial progress.

Synthesis is actively working but has not yet completed any partitions. The active count is 18 out of 44 max, and partitions_done is 0 after 63 seconds. This is consistent with the early stages of proof synthesis, where the first batch of partitions is being processed. The fact that 18 synthesis tasks are running (not all 44) suggests that the system is respecting the budget and not over-committing resources.

The Reasoning and Motivation Behind This Message

The assistant's decision to wait 60 seconds and then poll the status API was not arbitrary—it was a carefully timed observation. The assistant knew from previous interactions that loading the 44 GiB SRS parameters from disk into pinned GPU memory takes a significant amount of time (roughly 30–60 seconds based on earlier logs). By waiting 60 seconds, the assistant ensured that SRS loading would be complete and that synthesis would have had time to begin, but not so much time that the first job would have already finished (each partition takes on the order of minutes to synthesize).

The choice to use curl against the local status API (port 9821) rather than checking the daemon logs directly was also deliberate. The status API provides a structured, machine-readable view of the system state that is easier to parse and interpret than log lines. The assistant then used python3 -m json.tool to pretty-print the JSON, making the response human-readable for analysis.

Assumptions and Potential Pitfalls

This message rests on several assumptions that are worth examining. The assistant assumes that the ordered synthesis scheduler is the cause of the single-pipeline observation, but there could be alternative explanations. For instance, it is possible that the second job simply had not been submitted yet—perhaps the cuzk-bench batch command had not finished submitting both jobs within the observation window. However, this is unlikely given that the batch command was launched with --concurrency 2, meaning both jobs would be submitted in parallel.

Another assumption is that the status API accurately reflects the internal state of the pipeline scheduler. The pipelines field is populated by the StatusTracker module, which records pipeline lifecycle events. If there were a bug in the status tracking code (e.g., a race condition in the RwLock-backed snapshot), the API might report an incomplete picture. However, the status tracking system had been tested in previous segments and was known to be reliable.

The assistant also assumes that 63 seconds of synthesis with 0 partitions done is normal. This is consistent with the expected behavior for PoRep proofs with 10 partitions, where each partition requires significant computation. But if synthesis were stuck due to some other bug (e.g., a deadlock in the worker pool), the same observation would result. The assistant does not investigate this further in this message, implicitly trusting that progress will be visible on the next check.

Input Knowledge Required

To fully understand this message, one needs knowledge of several interconnected systems:

  1. The cuzk proving daemon architecture: Understanding that proofs are divided into partitions, that synthesis and GPU proving are separate phases, and that the daemon manages multiple concurrent pipelines.
  2. The budget-based memory manager: Knowledge that the system uses a 400 GiB total budget, that each partition requires ~9–13 GiB of working memory, and that max_concurrent is computed dynamically from the budget.
  3. The ordered synthesis scheduler: Understanding that the fix replaced a Notify-based race with an mpsc::channel FIFO queue, and that the expected behavior is sequential job completion.
  4. The status API schema: Familiarity with the JSON fields (pipelines, synthesis, gpu_workers, memory) and what they represent.
  5. The deployment environment: Knowledge that the daemon runs on a remote machine with specific port mappings (9820 for gRPC, 9821 for status API) and that SSH tunneling is used for access.

Output Knowledge Created

This message produces several valuable pieces of knowledge:

  1. Confirmation that the ordered scheduler is working: The single-pipeline observation is strong evidence that the FIFO scheduling fix is effective.
  2. Validation of the budget-based max_concurrent calculation: The value 44 matches expectations, confirming that the dynamic computation is correct.
  3. Evidence of normal memory pressure: The 378 GiB usage confirms that the memory manager is allowing synthesis to utilize the budget effectively.
  4. A baseline for performance comparison: The observation that 0 partitions are done after 63 seconds provides a data point for future optimization work.
  5. Operational confidence: The fact that the daemon is stable under load (no crashes, no error messages) after the recent series of fixes (evictor panic, GPU worker race, ordered scheduling) indicates that the system is approaching production readiness.

The Thinking Process

The assistant's reasoning in this message follows a clear experimental pattern: hypothesis formation, controlled observation, and evidence interpretation. The hypothesis is that the ordered synthesis scheduler will cause jobs to be processed sequentially rather than concurrently. The controlled observation is the 60-second wait followed by a status API poll. The evidence interpretation is the single pipeline entry in the response.

Notably, the assistant does not rush to declare success. The message is presented as an observation ("Let me wait and then check the synthesis progress") rather than a conclusion. The assistant is gathering data, and the interpretation is left implicit in the presentation of the JSON response. This is characteristic of a disciplined debugging approach: let the data speak before drawing conclusions.

The choice to include the full JSON response rather than a summary is also significant. It allows the reader (the user) to examine all fields and draw their own conclusions. The assistant is being transparent about what it sees, not filtering or editorializing.

Conclusion

Message [msg 2838] is a quiet but significant moment in the development of the cuzk proving daemon. It represents the transition from theory to evidence—from knowing that the ordered synthesis scheduler should work to seeing that it does work under real load. The single pipeline entry in the status response is the visible signature of a fundamental architectural improvement. While further verification (waiting for the first job to complete and the second to begin) would be needed for complete confidence, this message provides the first piece of confirming evidence that the FIFO scheduling fix is effective. In the broader narrative of the cuzk project, this message marks the point where the system's behavior aligns with its design, setting the stage for the next phase of optimization and performance tuning.