From Black Box to Observatory: Deploying the Unified Memory Manager and Building a Real-Time Status API for cuzk

Introduction

The most critical transition in any production system is the moment it stops being a black box. Before that moment, operators can only infer what happened from logs and post-mortem analysis. After it, they can watch the system breathe in real time — see the memory curve rise as partitions are dispatched, watch GPU workers pick up and complete work, and understand exactly where the bottlenecks are. This article tells the story of that transition for the cuzk GPU proving engine, a CUDA-based zero-knowledge proof generator for the Filecoin network.

The work in this segment unfolded in two distinct acts. In Act One, the assistant completed the deployment and end-to-end validation of a unified budget-based memory manager — a sophisticated byte-level admission control system that had been designed and implemented across previous segments. This was the moment of truth: would the memory manager actually work on a real production machine with co-resident processes, or would it crash and burn? The answer was a qualified success that required one final round of diagnosis and tuning. In Act Two, the assistant pivoted to build something entirely new: a real-time status API that would expose the engine's internal state — pipeline progress, memory budget utilization, GPU worker activity — to a lightweight HTTP endpoint designed for SSH-based polling from a remote management dashboard.

This article examines both acts in depth, exploring the architectural decisions, the operational constraints, the diagnostic reasoning, and the systematic integration methodology that characterized the assistant's approach. The story is not just about what was built, but about how a complex software system was made both safe and observable — and what that process reveals about the nature of AI-assisted engineering.

Act One: The Memory Manager's Moment of Truth

The State of Play

When this segment began, the unified budget-based memory manager had already been designed, implemented, and committed across multiple files in the cuzk codebase ([msg 2371]). The core idea was elegant: replace the static partition_workers semaphore — a simple integer limit on how many partitions could be processed concurrently — with a byte-level MemoryBudget that tracked every major memory consumer under a single, configurable budget. The three dominant consumers were the Structured Reference String (SRS) cache at approximately 44 GiB, the Pre-Compiled Circuit Evaluator (PCE) cache at approximately 26 GiB, and the per-partition synthesis working set at approximately 13.6 GiB each.

The implementation had already been through several iterations. A critical bug had been discovered during an earlier deployment attempt: the evictor callback, which runs inside the budget's async acquire() method, had been calling blocking_lock() on the SRS manager's tokio::sync::Mutex. This caused a panic because blocking_lock() is not allowed from within an async runtime context. The fix was to switch to try_lock() — if the mutex is held, the evictor simply skips SRS eviction for that iteration and lets the acquire loop retry ([msg 2375]). This fix was uncommitted at the start of the segment, and committing it was the first order of business.

There was also a known but lower-priority race condition: when multiple proofs arrive simultaneously, each one pre-acquires SRS budget before checking whether SRS is already loaded. With three concurrent proofs each pre-acquiring 44 GiB, the system temporarily reserves 132 GiB of budget that it doesn't actually need. The extra reservations are dropped when ensure_loaded() discovers SRS is already loaded, but during the race window they consume budget unnecessarily. This was documented but deferred.

The Deployment: From OOM to Success

The assistant deployed the fixed binary to a remote machine with 755 GiB RAM, an RTX 5090 GPU (32 GiB VRAM), and 64 CPU cores. The machine was also running Curio, a Filecoin storage and proving node, which was cordoned (not actively proving) but still consuming significant shared memory.

The first test used an auto-detected budget of 750 GiB — essentially the full system RAM minus a 5 GiB safety margin. The result was an OOM kill: RSS climbed to 498 GiB across 30 concurrent partitions, and the Linux OOM killer stepped in ([msg 2375]). The root cause was that the auto-detection read total system RAM, but co-resident Curio processes were consuming approximately 226 GiB (RSS plus shared memory), leaving only about 529 GiB actually available for cuzk. The 5 GiB safety margin was grossly inadequate.

The assistant diagnosed this by examining /proc/meminfo and free -h on the remote machine, calculating the actual headroom: 755 GiB total minus 226 GiB for Curio minus 50 GiB for OS overhead equals approximately 479 GiB available for cuzk. With baseline SRS+PCE at ~70 GiB, that left about 409 GiB for working sets, or roughly 28 partitions at 14.3 GiB each (the actual per-partition cost observed during the OOM run).

The assistant then reconfigured to an explicit total_budget = "400GiB" — a carefully calculated figure that would allow approximately 24 concurrent partitions while keeping peak RSS around 413 GiB, well within the 479 GiB ceiling ([msg 2380]). The config was updated, the daemon was restarted, and a benchmark was launched.

The Results: Validation at Last

The second run was a complete success. All 30 partitions (3 proofs × 10 partitions each) processed concurrently. The RSS curve behaved exactly as expected: startup from 12 MB to 488 GiB as SRS loaded and PCE extracted, steady state between 390-425 GiB as partitions completed and new ones started, and a cooldown back to 74.6 GiB baseline. All 3/3 proofs passed verification with a self-check (verify_seal passed for every proof). Throughput was 0.759 proofs per minute (79.0 seconds per proof effective), with an average wall time of 207.7 seconds and average GPU prove time of 114.9 seconds ([msg 2400]).

This was a hard-won victory. The memory manager had been through multiple iterations across several segments: the initial design, the core implementation, the blocking_lock() panic, the SRS race condition, and now the OOM from auto-detected budget. Each failure produced knowledge that was consolidated into a comprehensive status report at [msg 2371] — a document that cataloged failure modes, quantified memory usage with precise numbers, and prioritized next steps.

The Knowledge Created

The successful deployment created several forms of institutional knowledge:

  1. Validated memory architecture numbers: Baseline RSS ~70 GiB (SRS 44 GiB + PCE 26 GiB), per-partition working memory ~13.6 GiB (a/b/c vectors 12.5 GiB + aux 0.74 GiB + density 48 MB), two-phase GPU release dropping 12.5 GiB at prove_start and 1.1 GiB at prove_finish.
  2. A catalog of failure modes with resolutions: The evictor panic (fixed with try_lock()), the SRS double-acquisition race (known, lower priority), and the OOM from auto-detected budget (fixed by explicit budgeting with knowledge of co-resident processes).
  3. A validated deployment model: The system could run 30 partitions concurrently on a 755 GiB machine with a 400 GiB explicit budget, achieving 0.759 proofs/min throughput with peak RSS at 488 GiB and clean memory return to baseline.
  4. A file manifest: Every file created or modified, with descriptions of what changed, serving as an auditable changelog for the entire memory manager implementation. But success revealed a new problem: the system was still opaque. When the user asked "What was the max parallel pipelines in the test run?" ([msg 2402]), the assistant had to write an ad-hoc Python script to compute concurrency from raw TIMELINE event traces. The answer — a peak of 23 concurrent partitions out of 30 — was interesting, but it was buried in logs. The system was a black box that could only be understood through post-hoc analysis.

Act Two: Building the Observatory

The User's Request: A Concise Specification

The pivot came in [msg 2407], where the user issued a remarkably concise 27-word request:

"Commit; Create an API (listen on the port) that exposes progress of all pipelines, limiter states (mem, synth), major allocs, gpu mem budgets / worker states"

This message encodes a rich set of requirements. Breaking it down:

The Design Phase: Architecture Decisions

The assistant's response to this request was methodical. It began by exploring the codebase ([msg 2413] through [msg 2419]), reading the engine's dispatch logic, GPU worker finalization paths, partition result processing, pipeline static counters, and configuration structures. This exploration revealed several key facts that shaped the design:

  1. The engine's process_partition_result function was a centralized choke point where all GPU results were processed — an ideal insertion point for status tracking.
  2. The pipeline module already had static atomic counters (SYNTH_IN_FLIGHT, PROVERS_IN_FLIGHT, AUX_IN_FLIGHT) that could be exposed to the status module.
  3. The DaemonConfig struct used a listen: String field for its primary socket, providing a pattern to follow for the status endpoint.
  4. The daemon binary (main.rs) was separate from the core library (cuzk-core), meaning the HTTP server would live in the binary while the status data model lived in the library. The assistant then designed a JSON status schema and created a new status.rs module in cuzk-core ([msg 2421]). The design centered on a StatusTracker struct backed by an RwLock<StatusSnapshot> — a concurrency primitive that allows multiple readers (the HTTP polling endpoint) to snapshot state without blocking each other, while a single writer (the engine lifecycle hooks) updates state atomically. Several architectural decisions shaped this design: Protocol choice: HTTP over gRPC. The assistant immediately recognized that gRPC was wrong for this use case. The existing daemon used tonic for its proof submission protocol, but the status endpoint needed to be easily queryable via curl over SSH tunnels. HTTP/1.1 JSON was the natural choice. The assistant considered using tonic's accept_http1(true) feature to multiplex HTTP/1.1 requests through the existing gRPC port, but judged the integration too complex for the benefit. Instead, it opted for a separate configurable status_listen port. Port strategy: Separate port, adjacent to gRPC. The assistant chose a separate port rather than multiplexing through the existing gRPC endpoint. The status_listen config option defaults to the gRPC port plus one, making it predictable without hardcoding. This separation also meant the status server could be started or stopped independently without affecting proof submission. HTTP framework: axum over raw TCP. The assistant cycled through several options — axum, hyper, raw tokio TCP — before settling on axum on a separate port. The key consideration was dependency management: adding axum as a new dependency was acceptable for the cleanliness it provided over manual HTTP parsing. The assistant checked whether axum was already a dependency (it was not) and verified that its transitive dependencies would not conflict with existing ones. Concurrency model: std::sync::RwLock over tokio::sync::RwLock. The assistant correctly identified that std::sync::RwLock was appropriate because the critical sections — setting fields on the snapshot, cloning the snapshot for the HTTP response — were synchronous and brief. Using tokio::sync::RwLock would add unnecessary async overhead and require the HTTP handler to be async as well. State ownership: Dedicated tracker rather than engine queries. Rather than querying the engine's existing mutexes on each poll, the assistant created a dedicated StatusTracker with its own RwLock. This avoided lock contention on the engine's hot path and allowed the HTTP handler to read state independently. The tracker is a lightweight struct that the engine updates at lifecycle points; the HTTP handler clones the latest snapshot without ever touching engine internals.

The Schema Design

The JSON status schema was designed to be comprehensive yet easy to consume. The assistant defined four main snapshot types:

  1. StatusSnapshot: The top-level response, containing a list of jobs, a list of GPU workers, memory status, and aggregate counters (proofs completed, failures, throughput).
  2. JobSnapshot: Per-job state including job ID, proof kind, current phase (registering, synthesizing, proving, complete), partition count, and per-partition detail (synthesis status, GPU pickup time, completion status).
  3. WorkerSnapshot: Per-GPU-worker state including worker index, active job ID, current activity (idle, picking up, proving), and utilization metrics.
  4. MemoryStatus: Budget configuration (total budget, current usage, available), SRS allocation status, PCE allocation status, and the current synthesis concurrency level. This schema was designed to answer the questions the user had explicitly asked about (pipeline progress, limiter states, major allocs, GPU worker states) while also providing aggregate metrics that would be useful for the HTML dashboard.

The Integration Phase: Systematic Wiring

With the StatusTracker module created, the assistant faced the most delicate part of the implementation: wiring it into the Engine's lifecycle at every meaningful transition point. This was a systematic, multi-step process that spanned approximately 50 messages ([msg 2420] through [msg 2475]).

The assistant decomposed the integration into a sequence of small, verifiable steps, each building on the previous one:

Step 1: Add the tracker field to the Engine struct ([msg 2425], [msg 2427]). A single edit that added status_tracker: Arc<crate::status::StatusTracker> to Engine and initialized it in Engine::new(). This was the foundation — nothing else could happen until the tracker existed as part of the engine.

Step 2: Register workers in Engine::start() ([msg 2428]). When GPU workers are spawned, register them with the tracker so the status snapshot knows how many workers exist and their initial idle state. This required threading the tracker into the worker spawn logic.

Step 3: Thread the tracker through the synthesis dispatcher ([msg 2430], [msg 2433], [msg 2434]). The synthesis dispatcher runs in a spawned tokio task that captures clones of shared state. The tracker needed to be cloned and captured in this closure so that synthesis events could be recorded. The assistant read the relevant section of engine.rs to understand the closure structure before making the edit.

Step 4: Add the st parameter to dispatch_batch and process_batch ([msg 2434] through [msg 2442]). These functions are the inner dispatch loop of the engine. Adding the tracker as a parameter required updating five call sites, each of which needed to pass the tracker reference. The assistant systematically worked through each call site, ensuring the parameter was threaded correctly.

Step 5: Add tracking calls inside process_batch ([msg 2445], [msg 2447]). This was the critical edit — the moment when the tracker actually began recording lifecycle events. The assistant encountered a subtle difficulty: process_batch contains two parallel code paths (PoRep and SnapDeals), and the initial edit attempt failed because the pattern matched both paths. The assistant recognized the problem, read more context to find a unique anchor, and applied the corrected edit.

Step 6: Wire GPU completion tracking ([msg 2459] through [msg 2468]). The assistant identified process_partition_result as the centralized choke point for GPU result processing. By adding the tracker as a parameter to this function and updating its three call sites, the assistant guaranteed that every GPU completion — regardless of code path (Supraseal fast path, non-Supraseal fallback, finalizer task) — would be recorded.

Step 7: Wire job completion tracking ([msg 2469] through [msg 2473]). The final lifecycle event — when all partitions of a job are complete — was wired into the job finalization path. The assistant read the relevant section of engine.rs to understand the completion logic before making the edit.

Step 8: Add the status_listen config option ([msg 2475]). The last prerequisite before the HTTP server. A single field, status_listen: Option<String>, was added to DaemonConfig, making the status endpoint optional and configurable.

Step 9: Add failure tracking for error paths ([msg 2490]). The assistant added partition_failed calls to the SnapDeals error paths, ensuring that failed partitions are also recorded in the status tracker rather than silently disappearing.

The Thinking Process: What the Integration Reveals

The assistant's approach to this integration reveals several characteristic patterns of expert engineering reasoning:

Top-down decomposition. The assistant did not attempt to write the status tracking integration in one monolithic edit. Instead, it decomposed the task into a sequence of small, dependency-ordered steps: create the module, add the field, register workers, thread through closures, update call sites, add tracking calls, add config. Each step was independently verifiable (the code compiled, the tracker existed, the parameter was passed), and the final step — the actual tracking calls — was the smallest and most focused change of all.

Read-edit-verify discipline. Before each edit, the assistant read the relevant section of the file to understand the surrounding context. After each edit, it confirmed success and moved to the next task. This discipline was especially important for the third GPU completion call site (the non-Supraseal fallback), which was structurally different from the other two paths. The assistant read the file before editing, ensuring precision.

Dependency chain awareness. When the assistant added the status tracker to process_partition_result, it immediately traced the impact: the function is called from three places, each of which must now pass the tracker. But those call sites are themselves inside closures and spawned tasks, which means the tracker must be cloned and captured at each level. The assistant systematically worked through this chain, never assuming that a change at one level automatically propagated.

Context-driven requirements interpretation. The assistant did not treat the user's request as a fixed specification. Instead, it interpreted the request through the lens of the operational context. When the user said "listen on the port," the assistant considered what "the port" meant in the context of SSH-based polling. When the user asked for "limiter states," the assistant mapped this to specific engine components (synthesis semaphore, memory budget). This contextual interpretation is what separates a mechanical implementation from a thoughtful design.

Dependency-aware decision making. The assistant consistently considered the dependency implications of each choice. When considering axum for the HTTP server, it checked whether it was already a dependency. When considering hyper, it noted that it was already pulled in transitively by tonic. This awareness reflects a mature understanding of software maintenance — every dependency is a liability that must be justified.

The Bridge Between Two Acts

The transition from Act One (memory manager) to Act Two (status API) was not arbitrary. It was driven by a fundamental operational need: after the OOM incident, the user could not afford to fly blind. The memory manager made the system safe, but the status API made it understandable. The two acts are connected by a single insight: a system that works but cannot be observed is only half-built.

The assistant's reasoning across both acts reveals a consistent methodology. In Act One, the assistant consolidated its understanding of the memory architecture into a comprehensive status report ([msg 2371]), cataloging failure modes, quantifying memory usage, and prioritizing next steps. In Act Two, the assistant applied the same methodology to the status API: explore the codebase, design the schema, implement the core module, wire it in, add configuration, and build the presentation layer. In both cases, the assistant's approach was systematic, incremental, and grounded in code-level understanding.

The assistant also demonstrated a strong sense of when to stop. After completing the status tracker integration and adding the status_listen config option, the assistant recognized that the next step — building the actual HTTP server in main.rs — was a separate task that would follow naturally. Rather than trying to do everything at once, the assistant completed the integration layer and left the HTTP server for the next round of work. This discipline — knowing when a task is "done enough" to commit and move on — is a hallmark of experienced engineers.

Assumptions and Their Implications

Several assumptions underpinned the work in this segment, and examining them reveals both the strengths and potential weaknesses of the final implementation:

Assumption: The status tracker should use std::sync::RwLock rather than a lock-free approach. The assistant chose RwLock for its simplicity, but this creates a potential contention point. With 500ms polling and potentially dozens of status updates per second (from multiple GPU workers completing partitions), the write lock could starve readers or vice versa. A lock-free approach using atomic swap of Arc<StatusSnapshot> would have eliminated contention entirely. The assistant implicitly assumed that RwLock write acquisitions would be fast enough — an assumption grounded in the fact that status updates are simple struct mutations, not I/O-bound operations.

Assumption: The status data fits in memory indefinitely. The assistant decided to keep all completed jobs in the tracker rather than implementing cleanup logic, reasoning that "we're only dealing with 3-30 concurrent proofs at most." This is true for the current workload, but it embeds a scalability assumption. If the system were ever used for continuous proof generation, the unbounded retention of completed jobs could become a memory leak.

Assumption: process_partition_result is the only place where GPU results are finalized. The assistant's grep confirmed three call sites, but there is always the risk of dynamic dispatch or indirect calls that bypass the function. The assistant did not verify that no other code paths produce GPU completions without going through process_partition_result. This is a reasonable assumption given the codebase structure, but it is an assumption nonetheless.

Assumption: The status endpoint is a monitoring convenience, not a core protocol requirement. Making the HTTP server optional via Option<String> ensures that operators who don't need it are not forced to run an extra service. But it also means that the status tracker is always running (recording data internally) even when no one is reading it — a small but nonzero overhead in memory and CPU.

Assumption: The HTTP server can be lightweight and stateless. The assistant's design for the HTTP server (to be built in the next round) uses a simple axum handler that clones the latest snapshot and returns it as JSON. This is stateless from the HTTP perspective — each request is independent and requires no session management. This is appropriate for a polling-based monitoring endpoint.

Conclusion

The work in this segment represents a complete arc: from a working memory manager to an observable proving engine. The assistant navigated a complex design space spanning memory architecture, async Rust concurrency, HTTP protocol choice, dependency management, and lifecycle instrumentation — all while maintaining a systematic, incremental approach that minimized risk and maximized verifiability.

The most striking aspect of this story is not the complexity of the individual components, but the coherence of the overall design. The memory manager and the status API are not separate features bolted onto the engine; they are two sides of the same coin. The memory manager provides safety through byte-level admission control; the status API provides understanding through real-time visibility. Together, they transform the cuzk proving daemon from a black box that sometimes crashes into a transparent system that operators can monitor, diagnose, and trust.

For anyone studying how AI assistants approach large-scale software engineering tasks, this segment offers a rich case study in task decomposition, dependency management, architectural reasoning, and the importance of observability in production systems. The specific technologies (cuzk, tonic, axum, Filecoin) are incidental; the reasoning patterns are universal.

The assistant's methodology — top-down decomposition, read-edit-verify discipline, dependency chain awareness, context-driven requirements interpretation, and knowing when to stop — is a template that can be applied to any complex integration task. And the final result — a memory-safe, observable proving engine — is a testament to the power of systematic, incremental engineering.