Designing a Live Status API for a GPU Proving Engine: Architecture Decisions in the Cuzk Memory Manager

Introduction

In the high-stakes world of Filecoin proof generation, where 32 GiB proofs must be computed under tight memory budgets across distributed GPU clusters, observability is not a luxury—it is a necessity. This article examines a single message from an opencode coding session in which an AI assistant designs and begins implementing a real-time status API for the cuzk proving daemon, a GPU-accelerated proof generation engine for the Filecoin network. The message, indexed as <msg id=2412> in the conversation, captures the assistant's reasoning process as it navigates a complex design space: how to expose live pipeline progress, memory budget states, synthesis limiter status, and GPU worker utilization to an external monitoring UI polling at 500ms intervals.

This message is a turning point in the session. The preceding messages (see <msg id=2391> through <msg id=2400>) document the successful end-to-end testing of the unified budget-based memory manager—a major architectural achievement that replaced a fragile static concurrency limit with a byte-level memory admission control system. With the memory manager proven in production (3/3 proofs verified, peak RSS 488 GiB safely under the 529 GiB available, memory correctly returning to baseline), the user's next request was deceptively simple: "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" (see <msg id=2407>).

What follows is a masterclass in systems-level API design. The assistant's reasoning reveals a deep understanding of the tradeoffs between protocol choice, dependency management, concurrency models, and the practical realities of SSH-based monitoring in a distributed deployment. This article unpacks every layer of that reasoning, examining the assumptions, decisions, and knowledge structures that shaped the final design.

The Context: Why a Status API Was Needed

The Memory Manager Success Story

To understand why this message matters, we must first appreciate what came before it. The cuzk proving daemon is a GPU-accelerated engine for generating Filecoin proofs of replication (PoRep), a compute-intensive operation that requires loading large Structured Reference Strings (SRS, ~44 GiB) and Pre-Compiled Constraint Evaluators (PCE, ~26 GiB) into GPU memory, then synthesizing and proving up to 30 partitions concurrently. The original implementation used a static partition_workers semaphore that limited concurrency by partition count, ignoring the actual memory footprint of each partition's working set. This led to out-of-memory (OOM) crashes when partitions with different memory requirements were dispatched simultaneously.

The unified budget-based memory manager, implemented across segments 14-17 of the conversation, replaced this with a byte-level admission control system. The MemoryBudget struct tracked reservations for SRS, PCE, synthesis working sets (shells, aux, provers), and GPU proving buffers, using a try_lock()-based evictor callback to free SRS and PCE entries when budget was tight. The system was tested on a remote 755 GiB machine with a 400 GiB explicit budget (accounting for ~226 GiB consumed by co-resident Curio processes), successfully processing 30 partitions across 3 concurrent proofs with peak RSS at 488 GiB and throughput of 0.759 proofs/min.

The Observability Gap

The memory manager worked, but it was invisible. The only way to understand what the engine was doing was to grep through TIMELINE log entries—a tedious, post-hoc analysis that revealed concurrency patterns only after the fact. The user, who was managing a fleet of GPU nodes through a vast-manager HTML UI, needed real-time visibility into:

1. Pipeline progress: Which jobs were registered, which partitions were synthesizing vs. waiting for GPU, and which had completed
2. Limiter states: How much memory budget was consumed vs. available, and how many synthesis permits were in use
3. Major allocations: Whether SRS and PCE were loaded, and how much memory they consumed
4. GPU worker states: Which workers were idle, which were proving, and what they were working on The user's follow-up message (see <msg id=2411>) clarified the operational context: the API would be polled at 500ms intervals by an SSH-based poller running on a manager host. Ports were not exposed to the service network, and the team did not want to push metrics from all runners to a central service. This was a "perf debug view"—a lightweight, non-critical diagnostic endpoint that needed to be simple enough to curl over an SSH tunnel.

The Message: A Design Session in Real Time

The subject message <msg id=2412> is the assistant's response to the user's request. It is not a single coherent output but a window into an iterative design process. The message contains:

1. An extensive "Agent Reasoning" section (approximately 1,500 words) that documents the assistant's internal deliberation
2. A series of file read operations that explore the existing codebase to inform design decisions This structure—reasoning first, then action—is characteristic of the opencode assistant's methodology. The reasoning section is particularly valuable because it reveals not just what was decided, but why alternatives were rejected.

The Reasoning Journey

The assistant's reasoning follows a branching path through several design dimensions:

Protocol Choice (gRPC vs. HTTP): The existing daemon uses gRPC via tonic for its proof submission protocol. The assistant immediately recognizes that gRPC is wrong for this use case: "for this debug/status view, we need a simple HTTP JSON endpoint that can be easily queried via curl/ssh tunnels." This is a pragmatic decision driven by the operational context—SSH-based polling with curl demands HTTP/1.1, not HTTP/2 gRPC.

Port Strategy (Same Port vs. Separate Port): The user said "listen on the port," which the assistant interprets as the existing daemon port. This triggers a detailed exploration of multiplexing options. Tonic 0.12 supports accept_http1(true), which would allow routing both gRPC and HTTP requests through the same listener. The assistant considers this approach but ultimately rejects it: "But integrating a custom HTTP handler into tonic's routing gets messy with tower services and axum dependencies." The cleaner solution is a separate port, defaulting to the gRPC port plus one (9821 if gRPC is on 9820).

HTTP Framework (Axum vs. Hyper vs. Raw TCP): This is where the assistant's reasoning becomes most intricate. It cycles through several options:

• Axum: The cleanest API, with tonic 0.12 integration support. But it would add a new dependency.
• Hyper: Already pulled in transitively by tonic, so no new dependencies. But it requires more boilerplate.
• Raw tokio TCP with manual HTTP/1.1 parsing: Zero dependencies, maximum control. The assistant seriously considers this: "For a single JSON endpoint, I can handle the request parsing and response formatting directly..." The assistant oscillates between these options, reflecting a genuine design tension. The final settled approach—a lightweight HTTP server using axum on a separate port—represents a compromise between cleanliness and dependency management. Concurrency Model (std::sync::RwLock vs. tokio::sync::RwLock): The status API needs to be polled at 500ms, meaning read-heavy access with infrequent writes (engine lifecycle events). The assistant correctly identifies that std::sync::RwLock is appropriate here because "the critical sections are brief—just setting fields and cloning snapshots." Using tokio::sync::RwLock would add unnecessary async overhead for what is essentially a synchronous data structure access. State Architecture (Dedicated Tracker vs. Querying Existing Mutexes): The engine already has a JobTracker behind a Mutex, and GPU workers run in separate tokio tasks. The assistant considers two approaches: (1) adding a separate shared status structure that workers update directly, or (2) querying the existing JobTracker mutex on each poll. The decision to create a dedicated StatusTracker with its own RwLock is driven by the read-heavy polling pattern: "for 500ms polling intervals the latter should work fine as long as the critical sections stay short." Data Retention (Ephemeral vs. Persistent Job History): The assistant considers whether completed jobs should be retained in the status output. The initial thought was to keep them for 60 seconds, but the assistant simplifies: "the simplest approach is to keep all jobs in the tracker since we're only dealing with 3-30 concurrent proofs at most, so memory isn't a concern."

Assumptions Embedded in the Design

Every design decision rests on assumptions, and this message is rich with them. Identifying these assumptions is crucial for understanding both the strengths and potential weaknesses of the final implementation.

Assumption 1: SSH-Based Polling Implies HTTP/1.1

The assistant assumes that because the manager uses SSH-based polling, the status endpoint must speak HTTP/1.1 (not HTTP/2 gRPC). This is a reasonable assumption—curl over SSH tunnels works best with plain HTTP. However, it forecloses the possibility of using the existing gRPC infrastructure with a custom protobuf service definition, which would have provided type safety and streaming capabilities. The tradeoff is accepted: simplicity over formalism.

Assumption 2: 500ms Polling Is "Lightweight"

The assistant repeatedly characterizes the 500ms polling interval as "lightweight" and "fast." This assumption drives the choice of std::sync::RwLock over tokio::sync::RwLock and the decision to create a dedicated tracker rather than querying the engine's internal mutexes. However, 500ms is actually quite aggressive for a debug endpoint—it means 120 requests per minute, or 7,200 requests per hour per node. If the status snapshot generation involves any serialization overhead or lock contention, this could become a non-trivial CPU load. The assistant implicitly assumes that JSON serialization of a few dozen partition states is cheap enough to sustain this rate.

Assumption 3: The Status Data Fits in Memory

The assistant assumes that tracking all jobs (including completed ones) is feasible because "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 high-throughput proof generation (hundreds of proofs per minute), the unbounded retention of completed jobs could become a memory leak. The assistant's decision to add a completed_at field rather than implementing cleanup logic reflects an assumption that the monitoring use case is bounded.

Assumption 4: Separate Port Is Acceptable

The assistant assumes that adding a separate status port (defaulting to gRPC port + 1) is acceptable. This assumption is validated by the user's SSH-based polling model—the vast-manager can forward both ports. However, it adds operational complexity: firewall rules must allow the additional port, and the daemon must bind to two ports instead of one. The assistant briefly considers multiplexing on the gRPC port but rejects it due to complexity, implicitly assuming that the operational cost of a second port is lower than the engineering cost of multiplexing.

Assumption 5: The Engine Should Own the Status Tracker

The assistant decides to embed the StatusTracker as a field in the Engine struct, created during Engine::new() and passed through closures to synthesis tasks and GPU workers. This assumes that the engine is the natural owner of status information. An alternative would be to make the status tracker an independent service that subscribes to engine events, but that would require an event bus or callback mechanism that doesn't exist. The assistant's approach is pragmatic but creates a coupling between the engine's lifecycle and the status system.

Knowledge Required to Understand This Message

To fully grasp the assistant's reasoning, one needs knowledge spanning several domains:

Rust Concurrency and Synchronization

The assistant's deliberation between std::sync::RwLock and tokio::sync::RwLock reveals a nuanced understanding of Rust's concurrency primitives. std::sync::RwLock is a blocking lock that can cause deadlocks if held across async yield points, but it is more efficient for brief, non-async operations. tokio::sync::RwLock is designed for async contexts but has higher overhead. The assistant correctly identifies that the status tracker operations (setting fields, cloning snapshots) are synchronous and brief, making std::sync::RwLock the right choice.

HTTP Protocol and gRPC Multiplexing

The assistant's exploration of tonic 0.12's accept_http1(true) capability requires understanding how HTTP/2 and HTTP/1.1 can coexist on the same TCP port. Tonic's HTTP/2 server can be configured to accept HTTP/1.1 connections and route them through a tower service layer, but integrating a custom handler requires composing axum routers with tonic's gRPC router. The assistant correctly judges that this integration is "messy" and opts for a separate port.

GPU Proving Pipeline Architecture

The assistant's design of the status tracker's data structures reflects a deep understanding of the proving pipeline's lifecycle: jobs are registered, partitions are dispatched for synthesis (CPU-bound), synthesized partitions are queued for GPU proving, GPU workers pick up partitions and prove them, and results are assembled into final proofs. The status tracker must capture each of these stages with timing information. The assistant identifies the key lifecycle events: SYNTH_START, SYNTH_END, GPU_PICKUP, GPU_END, and job completion.

Dependency Management in Rust Workspaces

The assistant's concern about adding axum as a dependency reflects an understanding of Rust workspace dynamics. Adding a new dependency to cuzk-daemon is not free—it increases compile times, binary size, and the attack surface. The assistant checks whether hyper is already available as a transitive dependency (it is, via tonic) and considers using it directly before settling on axum for cleanliness.

Filecoin Proof Types

The assistant distinguishes between partitioned proofs (PoRep C2 with 10 partitions) and non-partitioned proofs (WinningPoSt, WindowPoSt, SnapDeals with single partitions). This distinction matters for the status tracker because partitioned proofs have a more complex lifecycle with per-partition state tracking, while non-partitioned proofs are simpler.

Knowledge Created by This Message

The message produces several forms of output knowledge that shape the subsequent implementation:

Design Decisions (Explicit)

1. Protocol: HTTP/1.1 JSON, not gRPC
2. Port: Separate configurable status_listen port (defaulting to gRPC port + 1)
3. Framework: Axum (or lightweight alternative) for the HTTP server
4. Concurrency: std::sync::RwLock for the status tracker
5. State ownership: Dedicated StatusTracker struct owned by the Engine
6. Data retention: Keep all jobs indefinitely, with completed_at marker
7. Snapshot model: Generate JSON snapshots on-demand from the tracker

Architecture Blueprint

The assistant establishes a three-layer architecture:

1. cuzk-core/src/status.rs: The StatusTracker module with serializable snapshot types (StatusSnapshot, JobStatus, PartitionStatus, WorkerStatus, MemoryStatus). This is the data model layer.
2. Engine integration: The tracker is wired into the engine at key lifecycle points—job registration, synthesis start/end, GPU pickup/end, and job completion. This is the instrumentation layer.
3. cuzk-daemon/src/main.rs: A lightweight HTTP server that exposes a /status endpoint, querying the engine's tracker and returning JSON. This is the presentation layer.

Implementation Plan

The assistant produces a concrete todo list with prioritized steps:

1. Explore codebase (completed in the message)
2. Design status API response schema (completed in reasoning)
3. Create status.rs with StatusTracker + JSON snapshot types
4. Wire StatusTracker into Engine lifecycle
5. Add status_listen config option to DaemonConfig
6. Add HTTP server to daemon binary

Code Exploration Results

The message includes several file reads that produce knowledge about the existing codebase:

• cuzk-daemon/src/main.rs: The daemon uses tonic for gRPC, not HTTP. The main() function sets up the gRPC server with Server::builder().add_service(...).serve(listen_addr).
• cuzk-daemon/Cargo.toml: Dependencies include tokio, tracing, anyhow, clap, and tonic. No HTTP framework is currently a direct dependency.
• cuzk-core/Cargo.toml: Core library has serde, serde_json, and uuid as dependencies—all useful for the status API.
• cuzk-core/src/lib.rs: The module structure shows batch_collector, config, engine, memory, pipeline, prover, scheduler, srs_manager, and types modules. The new status module will be added here.
• cuzk-core/src/engine.rs: The engine struct has fields for scheduler, srs_manager, budget, pce_cache, and shutdown_tx. The process_partition_result function at line 128 is identified as a key insertion point for status tracking.
• cuzk-core/src/pipeline.rs: Static atomics (SYNTH_IN_FLIGHT, PROVERS_IN_FLIGHT, etc.) are identified as candidates for status exposure.

Mistakes and Incorrect Assumptions

While the assistant's reasoning is generally sound, several aspects warrant critical examination:

The Multiplexing Missed Opportunity

The assistant's dismissal of tonic's HTTP/1.1 multiplexing capability may have been premature. Tonic 0.12's accept_http1(true) feature, combined with tower's service routing, allows serving both gRPC and REST endpoints from the same port without significant complexity. The assistant's concern about "messy tower services and axum dependencies" reflects a lack of familiarity with tonic's routing API rather than a genuine architectural limitation. A more thorough investigation might have revealed that tonic's Routes API can compose gRPC services with custom HTTP handlers using axum::Router as a tower service. This would have eliminated the need for a separate port entirely.

The Raw TCP Overengineering

The assistant's serious consideration of "raw tokio TCP with manual HTTP/1.1 parsing" for a single JSON endpoint is a classic overengineering trap. While it would indeed have "zero dependencies," it would also require manually handling HTTP request parsing, response formatting, connection management, and error handling—all of which are notoriously error-prone. The assistant correctly abandons this approach, but the fact that it was considered suggests a bias toward minimal dependencies that, if followed, would have produced fragile, unmaintainable code.

The 500ms Polling Assumption

The assistant assumes that 500ms polling is "lightweight" without considering the cumulative load of multiple nodes. If the vast-manager monitors 10 nodes, each polled at 500ms, that's 20 requests per second. Each request requires a JSON serialization of the full engine state. While the assistant's choice of std::sync::RwLock minimizes lock contention, the serialization cost could become significant if the number of tracked jobs grows. A more robust design might include caching the serialized snapshot and only regenerating it when state changes, rather than on every poll.

The Missing Authentication and Authorization

The assistant does not consider security for the status endpoint. In the current deployment model (SSH-based polling), the endpoint is only accessible through SSH tunnels, which provides transport-level security. But if the design were ever extended to direct network access, the status endpoint would expose sensitive operational data (memory usage, job IDs, GPU utilization) without any authentication. This is acceptable for a "perf debug view" but should be documented as a design constraint.

The Unbounded Job Retention

The decision to keep all completed jobs indefinitely, while justified for the current workload (3-30 concurrent proofs), creates a latent memory leak. If the system is ever used for continuous proof generation (e.g., a mining operation generating proofs around the clock), the status tracker's HashMap of jobs would grow without bound. A more future-proof design would implement a circular buffer or time-based eviction (e.g., keep jobs for 60 seconds after completion). The assistant briefly considers this but dismisses it as unnecessary complexity.

The Thinking Process: A Window into Expert Design

The most valuable aspect of this message is the assistant's thinking process, which reveals how an expert systems designer navigates a complex problem space. Several patterns emerge:

Iterative Refinement

The assistant does not arrive at the final design in a single leap. Instead, it cycles through alternatives, revisiting decisions as new information emerges. For example, the HTTP framework choice evolves through four stages:

1. Initial thought: "spin up a lightweight hyper server on a separate port"
2. Refinement: "use axum with tonic's native support"
3. Backtracking: "raw tokio TCP with manual HTTP/1.1 parsing"
4. Settlement: "add a minimal HTTP server using axum" This iterative refinement is characteristic of expert design. Rather than committing to the first viable option, the assistant explores the design space, evaluating tradeoffs before settling on a solution.

Dependency-Aware Decision Making

The assistant consistently considers the dependency implications of each choice. When considering axum, it checks whether it's "already a dependency in the project." When considering hyper, it notes that it's "already pulled in transitively by tonic." This dependency awareness reflects a mature understanding of software maintenance—every dependency is a liability that must be justified.

Context-Driven Requirements Interpretation

The assistant does not treat the user's request as a fixed specification. Instead, it interprets the request through the lens of the operational context. When the user says "listen on the port," the assistant considers what "the port" means in the context of SSH-based polling. When the user asks for "limiter states," the assistant maps this to specific engine components (synthesis semaphore, memory budget). This contextual interpretation is what separates a mechanical implementation from a thoughtful design.

Risk Assessment Through Prototyping

The assistant uses code exploration (file reads) as a form of prototyping. By reading the actual engine code, it validates assumptions about where lifecycle events occur and what data is available. The exploration of process_partition_result (line 128 of engine.rs) confirms that this function is the single point where partition GPU results are processed, making it an ideal insertion point for status tracking. This code-level validation prevents design errors that would be invisible at the architectural level.

The Role of Uncertainty

The assistant's reasoning is marked by uncertainty markers: "I could either...", "The cleanest approach is probably...", "Actually, wait—". These markers reveal genuine deliberation rather than post-hoc rationalization. The assistant is thinking through the problem in real time, and the uncertainty is a feature, not a bug. It shows that the assistant is considering alternatives rather than executing a pre-scripted plan.

The Implementation That Follows

While the subject message focuses on design, the subsequent messages (see <msg id=2413> through <msg id=2473>) document the implementation. The assistant creates:

1. cuzk-core/src/status.rs: A new module with StatusTracker (backed by RwLock<StatusSnapshot>), serializable snapshot types, and methods for updating state at each lifecycle event.
2. Engine integration: The StatusTracker is added as a field to Engine, created in Engine::new(), and wired into the synthesis dispatcher, GPU worker spawn, and process_partition_result function. The assistant modifies approximately 20 locations in engine.rs to pass the tracker through closures and call update methods.
3. Config extension: A status_listen option is added to DaemonConfig, allowing the status server address to be configured independently of the gRPC server.
4. Pipeline atomics exposure: The static atomic counters in pipeline.rs (SYNTH_IN_FLIGHT, PROVERS_IN_FLIGHT, etc.) are made pub(crate) so the status module can read them. The implementation follows the design blueprint established in the subject message, demonstrating the coherence between design and execution.

Conclusion

The subject message <msg id=2412> is far more than a simple response to a feature request. It is a detailed design document that captures the reasoning behind every architectural decision in the cuzk status API. The assistant navigates a complex design space spanning protocol choice, port strategy, HTTP framework selection, concurrency models, state architecture, and data retention policies—all while maintaining awareness of dependency implications, operational constraints, and the practical realities of SSH-based monitoring.

The message reveals several key insights about expert systems design:

1. Design is iterative: The assistant cycles through alternatives, refining decisions as new information emerges. The final design is not the first idea but the result of a structured exploration of the tradeoff space.
2. Context drives architecture: The operational context (SSH-based polling, 500ms intervals, debug-only use case) shapes every decision. A different context would have produced a different design.
3. Dependencies are liabilities: The assistant's consistent attention to dependency implications reflects a mature understanding of software maintenance. Every dependency must be justified.
4. Uncertainty is productive: The assistant's willingness to express uncertainty and consider alternatives leads to better decisions. The message's reasoning section is valuable precisely because it documents what was rejected, not just what was chosen.
5. Code exploration validates design: The assistant reads actual source files to validate assumptions, preventing design errors that would be invisible at the architectural level. The status API that emerges from this design process is lightweight, focused, and appropriate for its intended use case. It does not over-engineer for hypothetical future requirements, nor does it under-invest in the core functionality. It is a textbook example of context-driven API design—a system that fits its operational niche precisely because the designer understood the niche before designing the system. For anyone building observability infrastructure for distributed compute systems, this message offers a masterclass in how to think about the problem. The specific technologies (cuzk, tonic, axum, Filecoin) are incidental; the reasoning patterns are universal. The next time you need to expose internal state from a complex system, ask yourself the questions the assistant asked: Who will consume this data? How will they access it? How often will they poll? What state transitions matter? What can we afford to ignore? The answers to these questions, not the choice of HTTP framework or serialization format, determine whether your status API will be a useful tool or a maintenance burden.