From Memory Manager to Observatory: Designing a Real-Time Status API for the cuzk Proving Engine

The Message

In message 2420 of the opencode session, the assistant wrote:

I now have a complete picture. Let me design and implement the status API. The approach:

1. New status.rs in cuzk-core — StatusTracker with per-job/partition/worker state, updated at key lifecycle points 2. Update engine.rs — Embed tracker, add update calls at SYNTH_START/END, GPU_PICKUP/END, job register/complete 3. Update config.rs — Add status_listen to DaemonConfig 4. Update main.rs — Spawn lightweight raw-TCP HTTP server serving /status as JSON

[todowrite] {"todos":[{"content":"Explore codebase: daemon HTTP layer, engine state structures, GPU worker state","priority":"high","status":"completed"},{"content":"Design status API response schema","priority":"high","status":"completed"},{"content":"Create status.rs with StatusTracker + JSON snapshot types",...

This message appears deceptively simple — a short planning note with four bullet points and a todo list. But beneath its surface lies a significant architectural pivot. The assistant has just finished deploying and validating a complex unified memory manager for the cuzk GPU proving engine, a system that replaced a static concurrency semaphore with a byte-level budget tracking SRS, PCE, and synthesis working sets across 30 concurrent partitions on a 755 GiB remote machine. After successful end-to-end testing with 3/3 proofs passing verification, the user requested something new: a status API exposing pipeline progress, limiter states, major allocations, and GPU worker states for a 500ms-polled HTML UI. Message 2420 is the bridge between that request and its implementation — a moment of architectural synthesis where the assistant consolidates its understanding of the codebase and lays out a precise, four-step execution plan.

The Reasoning and Motivation: Why This Message Was Written

To understand why message 2420 exists, we must understand what preceded it. Messages 2413 through 2419 show the assistant engaged in intensive codebase exploration. It read engine.rs multiple times, examining the PartitionedJobState struct, the synthesis dispatcher, the GPU worker finalization path, and the process_partition_result helper. It read memory.rs to understand the MemoryBudget API. It read config.rs to see the existing DaemonConfig structure. It read pipeline.rs to examine the PceCache and the static buffer flight counters (SYNTH_IN_FLIGHT, GPU_IN_FLIGHT, etc.). It even read the workspace Cargo.toml to understand the crate layout. Each of these reads was a deliberate probe: the assistant was mapping the terrain before committing to a design.

Message 2420 is the crystallization of that exploration. The assistant states "I now have a complete picture" — a claim that carries weight because it is backed by seven rounds of targeted reading. The message serves three simultaneous purposes:

It declares readiness to the user. The assistant signals that it has finished its reconnaissance and is prepared to act. This is a communication protocol: the user has been waiting (perhaps watching the tool calls scroll by) and the assistant now says "I understand the problem and I have a plan."
It commits to an architecture. The four bullet points are not vague aspirations — they are concrete, ordered steps that map directly to files and changes. "New status.rs in cuzk-core" is a file creation. "Update engine.rs" is a modification. "Add status_listen to DaemonConfig" is a config schema change. "Spawn lightweight raw-TCP HTTP server" is a runtime addition. Each step is independently verifiable and sequentially dependent.
It externalizes the assistant's working memory. The todowrite block serves as a scratchpad, tracking what has been done (exploration, design) and what is in progress (creating status.rs). This is the assistant's way of managing its own context window — by writing down the state machine, it reduces the risk of losing track mid-implementation.

The Thinking Process: How Decisions Were Made

The assistant's thinking process is visible in the trajectory from message 2413 to message 2420. Let me trace the decision chain:

Decision 1: Where to put the status tracker. The assistant considered several locations. The existing buffer flight counters in pipeline.rs (lines 70–86) already track SYNTH_IN_FLIGHT, GPU_IN_FLIGHT, and PENDING_SYNTH as static atomics. But these are simple counters, not rich state. The assistant could have extended them with more atomics, but that would be fragile and limited. Instead, the assistant chose a new module — status.rs in cuzk-core — with a proper StatusTracker backed by RwLock. This is a deliberate architectural choice: the tracker needs to capture per-job state (job IDs, proof kinds, timestamps), per-partition state (synthesis progress, GPU pickup), and per-worker state (GPU worker concurrency). Atomics cannot represent this richness. A RwLock-guarded struct can.

Decision 2: What lifecycle points to instrument. The assistant identified four key events by reading the engine code: job registration (when a proof request is submitted), SYNTH_START/END (when synthesis begins and completes), GPU_PICKUP/END (when a GPU worker picks up a partition and finishes proving), and job completion (when all partitions are assembled). These were not chosen arbitrarily — they correspond to the existing buf_synth_start(), buf_synth_done(), buf_gpu_start(), and buf_gpu_done() calls in pipeline.rs. The assistant is mapping the new status tracker onto the existing instrumentation points, ensuring consistency.

Decision 3: How to serve the status. The assistant chose "lightweight raw-TCP HTTP server" rather than pulling in a full HTTP framework like hyper or actix-web. This is a conscious tradeoff: the status endpoint is a single route (/status), polled at 500ms intervals. A full HTTP framework would add compile time, dependency weight, and attack surface. Raw tokio TCP with manual HTTP response formatting is sufficient. This decision reflects the assistant's awareness of the deployment context — this is a GPU proving daemon running on a remote machine, not a public-facing web service.

Assumptions Made

The message makes several assumptions, most of which are reasonable but worth examining:

Assumption 1: The status tracker can be wired synchronously. The assistant plans to add update calls at key lifecycle points. But the engine is deeply async — synthesis runs in spawned tasks, GPU workers run in spawned tasks, and the finalizer runs in a spawned task. The StatusTracker uses RwLock (a synchronous primitive), not RwLock from tokio::sync. This means the tracker updates must be careful not to hold the lock across await points. The assistant implicitly assumes that the status updates are fast, non-blocking operations that can be done with a synchronous lock. This is likely correct for a snapshot-based tracker (write a snapshot, release the lock), but it is an assumption worth noting.

Assumption 2: The JSON schema is already designed. The todowrite block lists "Design status API response schema" as completed. The message does not show the schema, but the assistant has presumably sketched it during the exploration phase. The schema must include pipeline progress (how many proofs submitted, in synthesis, on GPU, completed), limiter states (budget usage, concurrency limits), major allocations (SRS size, PCE cache size), and GPU worker states (how many workers active, which partitions they are processing). This is a non-trivial design that the assistant has already done offline.

Assumption 3: The process_partition_result function can be extended. The assistant plans to pass the status tracker into process_partition_result, which is a public helper function called from both the inline fallback path and the spawned finalizer task. This function currently takes a fixed set of parameters. Adding a tracker parameter requires updating all call sites. The assistant assumes this refactoring is straightforward and does not break existing callers.

Assumption 4: The daemon binary (cuzk-daemon) has a main.rs that can be extended. The assistant plans to "Update main.rs" to spawn the HTTP server. This assumes that the daemon binary exists, that it has a main() function that sets up the engine and enters a run loop, and that there is a natural place to insert a tokio task for the HTTP listener. The exploration of Cargo.toml (message 2417) confirmed the existence of cuzk-daemon as a workspace member.

Input Knowledge Required

To understand message 2420, a reader needs knowledge of:

The cuzk proving engine architecture. The engine is the central coordinator, owning the scheduler, GPU workers, and SRS manager. It supports a pipelined mode where synthesis (CPU-bound) and GPU proving are overlapped. Proofs are partitioned into multiple chunks that are proved in parallel on the GPU and then assembled.
The existing instrumentation. The pipeline.rs module has static atomic counters (SYNTH_IN_FLIGHT, GPU_IN_FLIGHT, PENDING_SYNTH) that track buffer flight. These are updated at synthesis and GPU lifecycle points. The status API is a richer replacement for these counters.
The memory manager. The assistant has just deployed a unified budget-based memory manager that tracks SRS, PCE, and synthesis working sets in bytes. The status API needs to expose these budget states so operators can see memory pressure.
The config structure. DaemonConfig is a TOML-deserialized struct in config.rs. Adding a status_listen field (an address like 127.0.0.1:9877) requires adding a field with #[serde(default)] so existing configs without it still work.
The GPU worker lifecycle. GPU workers run in a loop, picking up synthesized partitions from a buffer, proving them on GPU, and then calling process_partition_result to route the result to the assembler or directly to the response channel.

Output Knowledge Created

Message 2420 creates a blueprint that will be realized in subsequent messages. The output knowledge includes:

A four-step implementation plan that can be executed sequentially. Each step has a clear deliverable: a new file, modifications to existing files, config changes, and a runtime addition.
A mapping between engine lifecycle events and status tracker updates. The assistant has identified exactly where in the codebase to insert tracking calls: at SYNTH_START/END (which correspond to buf_synth_start()/buf_synth_done() in the synthesis dispatcher), at GPU_PICKUP/END (which correspond to buf_gpu_start()/buf_gpu_done() in the GPU worker loop), at job registration (in the submit() method), and at job completion (in process_partition_result).
A design decision about the HTTP server. The choice of "lightweight raw-TCP" over a full framework is an architectural constraint that will affect the implementation. It means the HTTP server will manually parse HTTP requests and format JSON responses, likely using tokio::net::TcpListener and string formatting.
A dependency injection pattern. The status tracker will be created in the engine and passed through to the synthesis dispatcher, GPU workers, and result processors. This is a form of dependency injection — the tracker is a cross-cutting concern that needs to be available in multiple async contexts.

Mistakes and Incorrect Assumptions

The message is a plan, not an implementation, so it is difficult to identify mistakes without seeing the outcome. However, we can identify potential risks:

Risk 1: Lock contention under polling. If the HTTP server polls the StatusTracker at 500ms intervals while the engine is updating it at high frequency (e.g., during partition dispatch), the RwLock could become contended. The assistant assumes this is acceptable, but if the lock hold time is non-trivial (e.g., if serializing the snapshot requires iterating over many jobs), contention could cause latency spikes in the proving pipeline.

Risk 2: The raw-TCP HTTP server may be too minimal. Serving JSON over HTTP requires handling partial reads, connection keep-alive, and error responses. A raw TCP approach without an HTTP parser library may miss edge cases (e.g., chunked transfer encoding, HTTP/1.1 pipelining). The assistant assumes the monitoring UI will send simple GET /status requests without complex HTTP features, which is reasonable but not guaranteed.

Risk 3: The tracker may miss edge cases. The assistant identified four lifecycle points, but there may be others. For example, what happens when a job fails during synthesis? What happens when the GPU worker crashes? The failure paths in the engine (visible in the code reads at lines 1400 and 2300) already call tracker.record_failure(), suggesting the assistant considered this, but the plan does not explicitly mention error tracking.

The Broader Context: Why This Matters

Message 2420 sits at the intersection of two major engineering efforts. The first is the unified memory manager — a deep, systemic change to how the cuzk engine allocates and releases GPU memory. The second is the status API — an observability layer that makes the engine's internals visible to operators. The memory manager was invisible; the status API makes it visible. Without the status API, operators would have to infer memory pressure from crash logs and OOM events. With it, they can watch budget usage in real time, see which proofs are stuck in synthesis, and understand GPU worker utilization.

The assistant's approach in message 2420 is characteristic of a senior engineer: explore before designing, design before implementing, and communicate the plan before executing. The four bullet points are a contract between the assistant and the user — a promise that these are the changes that will be made, in this order, with these outcomes. The todowrite block is the assistant's way of tracking progress against that contract.

In the messages that follow (2421 and beyond), the assistant will execute this plan: creating status.rs, wiring it into the engine, adding the config field, and building the HTTP server. Message 2420 is the moment of commitment — the point at which exploration ends and construction begins.