The Screenshot That Exposed a Silent Bug: GPU Workers Falsely Idle in the cuzk Status Panel

Message Overview

In message [msg 2662] of this opencode session, the user submitted a concise but critical bug report:

@2026-03-13-171005_1601x1187_scrot.png W0/W1 GPU workers show idle when they are not

The message consists of two parts: an image attachment (a screenshot captured at 17:10:05 UTC on March 13, 2026) and a one-line textual description. The image was loaded via the Read tool from the path /tmp/czk/2026-03-13-171005_1601x1187_scrot.png. This message, though brief, marks a pivotal moment in the session: it validates a suspicion the assistant had already voiced moments earlier, and it launches a multi-threaded debugging effort that will uncover a wiring gap in the SnapDeals GPU proving path.

The Context That Made This Message Inevitable

To understand why this message was written, we must reconstruct the moments leading up to it. The assistant had just completed a major deployment cycle: the vast-manager Go backend and HTML UI had been rebuilt, uploaded to the manager host at 10.1.2.104, and the cuzk status panel was live on port 1236. The assistant had committed the changes as dda96181 and declared "All done" ([msg 2661]).

But during the verification step in [msg 2657], the assistant had already noticed something peculiar. While inspecting the live status data piped through a Python formatting script, the output showed:

GPU: [(0, 'idle', None), (1, 'idle', None)]

Yet the same data dump showed partitions in the gpu state with measured durations: "P12 ... gpu=587ms", "P11 ... gpu=5.2s". The assistant's internal monologue at that moment was telling: "Wait — the partitions go up to P15 (16 partitions)... And GPU workers show idle even though P6/P7 in the 3rd pipeline are in gpu state — they may have just transitioned." The assistant dismissed it with a plausible explanation: perhaps the workers had just transitioned and the status snapshot was taken at an unlucky moment.

The user's screenshot proved otherwise. The discrepancy was not a transient artifact of sampling timing — it was a persistent, reproducible bug. The GPU workers were always showing idle while partitions were actively being proven on the GPU. The screenshot captured this frozen-in-time: workers labeled W0 and W1 displayed "idle" while the pipeline visualization showed partitions with active GPU durations.

The Reasoning Behind the Report

The user's decision to capture and submit this screenshot reveals several layers of reasoning. First, the user was actively monitoring the UI after the deployment, likely refreshing the page and watching the panel update. The "jumpiness" fixes from the previous round had been deployed (caching last response, adding min-height CSS), and the user was verifying the visual stability. In doing so, they noticed a semantic error that was more concerning than the cosmetic flicker: the data was wrong.

The user's phrasing — "W0/W1 GPU workers show idle when they are not" — demonstrates a clear understanding of the system's expected behavior. The user knows that if partitions are in the gpu state, the corresponding GPU workers should report a proving state with a current_partition field. The user is not reporting a UI rendering glitch; they are reporting a data integrity issue. The panel is displaying information that contradicts the observable reality of the system.

This distinction matters. A cosmetic bug (the "jumpiness" reported in [msg 2639]) is a nuisance. A data integrity bug — where the monitoring system shows idle workers while work is being done — is a reliability concern. If the status panel cannot be trusted to show accurate worker states, it undermines the entire purpose of the monitoring infrastructure. The user's message implicitly communicates this urgency: the flashy new panel is nice, but it needs to be correct.

Assumptions Made

The user's message makes several implicit assumptions:

1. The screenshot is self-evident. The user assumes that the visual discrepancy between "idle" labels and active GPU partitions is immediately recognizable. They do not describe the image contents; they trust that the assistant will parse the screenshot correctly.
2. The bug is in the status tracking code, not the UI. By saying "GPU workers show idle," the user implicitly identifies the backend data source as the problem. The UI merely renders what the API returns. The user assumes the assistant will trace the issue upstream rather than patching the frontend to override the value.
3. The problem is reproducible. The user does not say "sometimes" or "occasionally." They state it as a fact: "show idle when they are not." This implies the bug is persistent and systematic, not a race condition or sampling artifact.
4. The assistant has the context to fix it. The user does not provide code snippets, logs, or reproduction steps. They assume the assistant knows the codebase well enough to locate the root cause from a single screenshot and a one-line description.

Mistakes and Incorrect Assumptions

The assistant's earlier assumption in [msg 2657] — that the idle workers might reflect a recent transition — was incorrect. The user's screenshot demonstrated that the idle state was not a momentary snapshot artifact but a persistent condition. This is a classic debugging pitfall: when a suspicious data point appears in a system under active development, it is tempting to rationalize it as a transient state or a timing issue rather than a genuine bug.

The assistant had the evidence in hand during the verification step — the Python dump showed idle workers alongside active GPU partitions — but lacked the conviction to treat it as a definitive bug. The user's independent confirmation was necessary to trigger the investigation.

Input Knowledge Required

To fully understand this message, one needs knowledge of:

• The cuzk engine architecture: The GPU proving pipeline involves synthesis (CPU-side constraint evaluation) followed by GPU proving (CUDA kernel execution). Each GPU worker is a dedicated thread that acquires a mutex, runs CUDA kernels, and releases the mutex. The status tracker records worker state transitions via partition_gpu_start and partition_gpu_end calls.
• The StatusTracker API: The GpuWorkerState struct has a busy: bool field that is set to true in partition_gpu_start and reset to false in partition_gpu_end. The snapshot serialization maps busy to the "proving" or "idle" string.
• The SnapDeals proving path: SnapDeals proofs use a different pipeline path than WinningPoSt and WindowPoSt. The assistant had recently added a partitioned pipeline for SnapDeals with overlapping synthesis and GPU proving. This new path might not have been fully wired into the status tracking system.
• The vast-manager UI: The HTML panel renders GPU workers as cards with state labels. The state comes directly from the JSON API response's gpu_workers array.
• The SSH ControlMaster tunneling architecture: The vast-manager proxies cuzk status via SSH tunnels to remote instances, meaning the data flows through multiple layers before reaching the browser.

Output Knowledge Created

This message triggers the creation of significant new knowledge:

1. The root cause identification: The assistant will trace the partition_gpu_start call site in engine.rs and discover that it only fires when parent_job_id.is_some() — a conditional guard that excludes certain SnapDeals code paths. The partition_gpu_end at line 140 also has a conditional guard. This means the SnapDeals GPU proving path never updates the GPU worker status.
2. The proof kind truncation bug: The user's follow-up in [msg 2665] ("Also ps-snap- is trimmed of a bit early") reveals a second bug: the proof kind label in the UI is truncated, cutting off at "ps-snap-" instead of showing the full "ps-snap-update" or similar. This will require investigation into how job_id is constructed and how the UI renders the proof kind label.
3. A todo list restructuring: The assistant will create a prioritized todo list with "Fix GPU worker status showing idle during active proving" as the top priority, followed by "Fix proof kind label truncation" ([msg 2666]).
4. A debugging methodology: The assistant will use grep to find all call sites of partition_gpu_start and partition_gpu_end, discovering only two call sites each — a suspiciously low number that immediately suggests incomplete coverage.

The Thinking Process Visible in the Assistant's Response

The assistant's response in [msg 2663] reveals a structured debugging thought process:

"Good catch. GPU workers show 'idle' but there are partitions clearly in gpu state (P12 '587ms gpu', P11 '5.2s gpu'). This means the StatusTracker GPU worker updates aren't being triggered in the SnapDeals proving path."

This is a precise diagnosis: the assistant immediately identifies the absence of update calls as the root cause, not a logic error in the update calls themselves. The reasoning is that if partition_gpu_start were being called correctly, the busy flag would be set and the snapshot would show "proving." Since it shows "idle," the call is simply not being made.

The assistant then reads status.rs to verify the API surface, greps for call sites, and discovers the sparse coverage. The investigation in [msg 2668] and [msg 2669] reveals that partition_gpu_start at line 2486 only fires when parent_job_id.is_some(). This conditional guard was likely added to handle a specific case (perhaps batched proofs or multi-sector jobs) but inadvertently excluded the SnapDeals partitioned path.

The Broader Significance

This message, for all its brevity, represents a critical quality gate in the development process. The cuzk status panel had just been deployed and committed. The user's first act of real-world validation uncovered not one but two bugs. This is the moment when the monitoring infrastructure transitions from "it works on my machine" to "it works in production under real Curio proof load."

The GPU worker idle bug is particularly insidious because it creates a false sense of safety. An operator watching the panel would see idle workers and might conclude that the system is underutilized or stalled, when in fact the GPUs are fully occupied. In a production monitoring context, this could lead to incorrect operational decisions — scaling up workers unnecessarily, misdiagnosing performance bottlenecks, or failing to detect GPU failures.

The user's message, though only 12 words plus a screenshot, catalyzed a debugging session that would ultimately fix a fundamental wiring gap in the SnapDeals proving path, making the monitoring system trustworthy for real operations.