The Moment of Truth: Live-Testing the Priority Queue for Ordered GPU Scheduling

Introduction

In the high-stakes world of zero-knowledge proof generation at scale, the difference between a system that works and one that works efficiently often comes down to scheduling. When multiple proof jobs compete for GPU resources, the order in which partitions are dispatched can mean the difference between predictable throughput and chaotic pipeline stalls. Message [msg 2931] captures a pivotal moment in the development of the cuzk CUDA ZK proving daemon: the first live test of a newly deployed priority queue system designed to enforce FIFO ordering of partition processing across concurrent proof pipelines.

The message itself is deceptively simple — a bash command that waits 90 seconds for SRS (Structured Reference String) loading, then queries the HTTP status API to inspect the system's state. But the output it reveals tells a rich story about the system's behavior under the new scheduling regime, and raises important questions about what "ordered scheduling" actually means in practice.

The Message

Wait for SRS to load (~60s), then check:
[bash] sleep 90 && ssh -o ConnectTimeout=10 -p 40612 root@141.0.85.211 'curl -sf --max-time 3 http://127.0.0.1:9821/status | python3 -c "
import sys, json
d = json.load(sys.stdin)
print(f\"Uptime: {d[\"uptime_secs\"]:.0f}s  Memory: {d[\"memory\"][\"used_bytes\"]/1e9:.1f}/{d[\"memory\"][\"total_bytes\"]/1e9:.1f} GB\")
print(f\"Synth active: {d[\"synthesis\"][\"active\"]}  Buffers: synth={d[\"buffers\"][\"synth_in_flight\"]} provers={d[\"buffers\"][\"provers_in_flight\"]}\")
for p in d[\"pipelines\"]:
    states = []
    for pp in p[\"partitions\"]:
        s = pp[\"state\"]
        tag = f\"P{pp[\"index\"]}:{s}\"
        states.append(tag)
    print(f\"  Job {p[\"job_id\"][:8]} ({p[\"proof_kind\"]}): done={p[\"partitions_done\"]}/{p[\"total_partitions\"]}  {\" \".join(states)}\")
for w in d[\"gpu_workers\"]:
    j = str(w[\"current_job\"])[:8] if w[\"current_job\"] else \"-\"
    p = w[\"current_partition\"] if w[\"current_partition\"] is not None else \"-\"
    b = f\"{w[\"busy_secs\"]:.1f}s\" if w[\"busy_secs\"] else \"\"
    print(f\"  GPU{w[\"worker_id\"]}: {w[\"state\"]} job={j} P{p} {b}\")
print(f\"Counters: completed={d[\"counters\"][\"total_completed\"]} failed={d[\"counters\"][\"total_failed\"]}\")
"'
Uptime: 114s  Memory: 427.3/429.5 GB
Synth active: 8  Buffers: synth=8 provers=13
  Job ps-snap- (snap-update): done=0/16  P0:synthesizing P1:pending P2:pending P3:pending P4:pending P5:synthesizing P6:pending P7:pending P8:pending P9:pending P10:pending P11:pending P12:pending P13:pending P14:pending P15:pending
  Job ps-snap- (snap-update): done=0/16  P0:pending P1:pending P2:pending P3:synthesizing P4:pending P5:pending P6:pending P7:pending P8:pending P9:pending P10:pending P11:pending P12:pending P13:pending P14:pending P15:pending
  Job ps-snap- (snap-update): done=0/16  P0:pending P1:pending P2:pending P3:pending P4:pending P5:pending P6:pending P7:pending P8:pending P9:pending P10:pending P11:pending P12:pending P13:pending P14:pending P15:pending
  Job ps-snap- (snap-update): done=0/16  P0:pending P1:pending P2:pending P3:pending P4:pending P5:pending P6:pending P7:pending P8:pending P9:pending P10:pending P11:pending P12:pending P13:pending P14:pending P15:pending
  Job ps-snap- (snap-update): done=0/16  P0:pending P1:pending P2:pending P3:pending P4:pending P5:pending P6:pending P7:pending P8:pending P9:pending P10:pending P11:pending P12:pending P13:pending P14:pending P15:pending
  Job ps-snap- (snap-update): done=0/16  P0:pending P1:pending P2:pending P3:pending P4:pending P5:pending P6:pending P7:pending P8:pending P9:pending P10:pending P11:pending P12:pending P13:pending P14:pending P15:pending
Counters: completed=0 failed=0

Why This Message Was Written: The Reasoning and Context

This message represents the culmination of a multi-hour debugging and implementation effort captured across segments 20 and 21 of the coding session. The assistant had identified a fundamental scheduling pathology in the cuzk proving pipeline: when multiple proof jobs were submitted concurrently, all partitions from all jobs were dispatched as independent tokio tasks racing on a Notify-based budget semaphore. This caused a "thundering herd" problem where every wakeup triggered a chaotic scramble among all waiting partitions, with random selection across pipelines. The result was that all pipelines stalled together instead of completing sequentially — a worst-case behavior for a system designed for predictable throughput.

The fix, implemented in the immediately preceding messages ([msg 2890] through [msg 2930]), replaced the chaotic per-partition tokio::spawn model with an ordered mpsc::channel and a synthesis worker pool that pulls FIFO. Each SynthesizedJob and PartitionWorkItem was given a job_seq field — a monotonically increasing counter — and a new PriorityWorkQueue struct was introduced that uses (job_seq, partition_idx) as the sort key for ordering. The dispatcher task was restructured to push partitions through this queue rather than spawning them as independent tasks.

The message at [msg 2931] is the first live observation of this new system in action. The assistant had just deployed the binary to a remote machine after a protracted battle with zombie processes (the old cuzk-ordered daemon had become a defunct process holding GPU memory and ports), waited for memory cleanup (the user had warned that the old process needed 1-2 minutes to release ~400 GiB of pinned CUDA memory), and finally started the new daemon on the primary ports. A test was launched: two concurrent PoRep proofs via cuzk-bench batch --type porep --count 2 --concurrency 2.

The 90-second sleep before the status check is deliberate and revealing. It reflects the assistant's deep understanding of the system's cold-start behavior: the SRS must be loaded from disk into GPU memory, a process that takes approximately 60 seconds for the 400+ GiB dataset used by Filecoin proofs. Checking the status before SRS loading completes would show an idle system and provide no useful information about scheduling behavior. The assistant is not just checking "is it running?" — they are waiting for the system to reach a steady state where meaningful scheduling dynamics are visible.

What the Output Reveals: Analysis of the Live State

The status output at 114 seconds uptime is rich with information. The memory usage of 427.3 GiB out of 429.5 GiB total confirms that SRS loading has completed — almost the entire budget is consumed by the loaded parameters, leaving only ~2 GiB of headroom for synthesis and proving working memory. This is exactly the behavior the budget-based memory manager was designed to enforce.

The synthesis system shows 8 active synthesis tasks with synth_in_flight=8 and provers_in_flight=13. This indicates that the synthesis workers are actively processing partitions, and some have already completed synthesis and moved to the GPU proving stage (the provers_in_flight counter tracks partitions waiting for or undergoing GPU proving).

The most striking detail is the job listing. Instead of the expected two PoRep jobs, the output shows six snap-update jobs, each with 16 partitions. All have done=0/16 — no partitions have completed yet. This is surprising and immediately raises questions. The assistant had submitted cuzk-bench batch --type porep --count 2 --concurrency 2, which should have produced two PoRep jobs, not six SnapDeals update jobs. Something is clearly different from what was expected.

Looking more closely at the partition states, we can see the FIFO ordering in action. The first job (ps-snap-) has partitions P0 and P5 in "synthesizing" state, while all others are "pending". The second job has only P3 synthesizing. The remaining four jobs show all partitions as pending. This is consistent with ordered scheduling: the system is working through the first job's partitions before moving to subsequent jobs. The fact that two partitions of the first job are synthesizing simultaneously (P0 and P5) reflects the synth_max budget allowing multiple parallel synthesis tasks within a single job, but the ordering constraint ensures that later jobs don't get ahead of earlier ones.

Assumptions and Surprises

The message reveals several assumptions baked into the assistant's mental model. The most significant assumption is that the test would produce PoRep (Proof of Replication) jobs. The assistant explicitly ran cuzk-bench batch --type porep, yet the status shows snap-update (SnapDeals) jobs. This discrepancy could indicate:

  1. A configuration issue where the benchmark tool is using a different proof type than requested
  2. A mismatch between the test parameters and the available proving data (the c1.json file might contain SnapDeals parameters)
  3. A subtle bug in how the benchmark tool interprets the --type porep flag The assistant also assumed that after 114 seconds, some partitions would have completed (the counters show completed=0). This is not necessarily a problem — 16 partitions per job × 6 jobs = 96 partitions total, and synthesis of a single partition can take tens of seconds. But it does mean the system is still in its warm-up phase and the true scheduling behavior won't be visible until partitions start completing and the GPU workers begin proving. Another implicit assumption is that the FIFO ordering is working correctly. The evidence is consistent with FIFO — the first job has the most synthesizing partitions — but it's not definitive proof. The system could simply be slower at starting later jobs for other reasons (e.g., the dispatcher processes jobs sequentially and hasn't reached later ones yet). A true test of FIFO ordering would require observing that partitions from the first job complete before partitions from the second job begin GPU proving, which requires waiting for the full pipeline to drain.

Input Knowledge Required

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

CUDA ZK proving pipeline architecture: Understanding that proof generation involves two main phases — synthesis (circuit construction, CPU-bound) and proving (GPU computation). Partitions are independent units of work within a proof job.

The priority queue implementation: The PriorityWorkQueue struct with (job_seq, partition_idx) ordering, the mpsc::channel-based dispatch replacing tokio::spawn, and the synth_max budget computation.

The budget-based memory manager: The 400 GiB total budget, with SRS consuming most of it and the remainder allocated to synthesis/proving working memory (~13.6 GiB per partition for PoRep, ~8.6 GiB for SnapDeals).

The status API schema: The fields synthesis.active, buffers.synth_in_flight, buffers.provers_in_flight, pipelines[].partitions[].state, and gpu_workers[].state all have specific meanings that must be interpreted in context.

The deployment environment: The remote machine uses an overlay filesystem where /usr/local/bin/cuzk cannot be replaced, requiring binaries to be deployed to /data/. The machine has ~400 GiB of GPU memory and runs the cuzk daemon as a background process.

The history of the scheduling problem: The previous implementation caused thundering herd wakeups and random partition selection, which this fix aims to resolve.

Output Knowledge Created

This message creates several pieces of valuable knowledge:

  1. Confirmation that the daemon starts and runs correctly with the priority queue changes. The status API responds, memory management works, and synthesis workers are active.
  2. Evidence of ordered scheduling behavior: The first job has more partitions in "synthesizing" state than subsequent jobs, consistent with FIFO ordering. This is the first observable signal that the fix is working as intended.
  3. A discrepancy in proof type: The system is processing SnapDeals jobs instead of the expected PoRep jobs, which warrants investigation.
  4. Baseline performance data: At 114 seconds with 8 active synthesis tasks and 0 completed partitions, this provides a baseline for future comparison.
  5. Validation of the status API: The API is returning rich, structured data that enables real-time monitoring of the pipeline — exactly the capability the assistant had built in the preceding segments.

The Thinking Process

The assistant's reasoning in this message is primarily about observation and hypothesis testing. The chain of thought is:

  1. "I've deployed the priority queue binary and started it on the primary ports."
  2. "I've submitted a test with 2 concurrent PoRep proofs."
  3. "SRS loading takes ~60 seconds, so I need to wait before checking."
  4. "After 90 seconds (114s uptime), SRS should be loaded and synthesis should be active."
  5. "Let me query the status API to see the scheduling behavior." The choice of a 90-second delay is not arbitrary — it's calculated based on known system characteristics. The assistant is thinking: "I need to wait long enough for the system to reach a state where scheduling decisions are visible, but not so long that the test completes and I miss the action." The Python formatting of the status output is also deliberate. Rather than dumping raw JSON, the assistant extracts and formats specific fields to create a human-readable summary that highlights the scheduling-relevant information: which partitions are in which state, which jobs are being processed, and how the GPU workers are allocated. This reflects a mindset of targeted instrumentation — extract just enough information to test the hypothesis (FIFO ordering is working) without drowning in irrelevant data. The fact that the assistant doesn't immediately react to the unexpected proof type (SnapDeals vs PoRep) in this message is also telling. It suggests either: (a) the assistant hasn't noticed the discrepancy yet, (b) the assistant is prioritizing the scheduling observation over the proof type detail, or (c) the assistant recognizes that the proof type doesn't affect the scheduling test. Given the assistant's demonstrated thoroughness in previous messages, (b) or (c) seems most likely — the primary hypothesis being tested is "does FIFO ordering work?" and the proof type is a secondary concern.

Conclusion

Message [msg 2931] is a moment of empirical validation in the software engineering lifecycle. After hours of design, implementation, debugging, deployment, and dealing with operational quirks (zombie processes, memory cleanup delays, port conflicts), the assistant finally gets to observe the system in its natural habitat. The output is not conclusive — no partitions have completed, the proof type is unexpected, and the sample size is small — but it's the first data point in what will become a chain of evidence about the priority queue's effectiveness.

The message exemplifies a disciplined engineering approach: form a hypothesis (FIFO ordering will prevent pipeline stalling), implement a fix, deploy it to a realistic environment, and observe the system's behavior before drawing conclusions. The assistant doesn't declare victory based on this single observation — they simply gather data and prepare for the next round of analysis. This is the essence of evidence-driven development in complex systems.