The Pivot Point: Wiring a Remote GPU Proving Daemon into Curio's Task Orchestrator

In the sprawling codebase of Curio—Filecoin's storage-provider orchestration layer—a quiet but momentous transition occurs at message index 3411. After dozens of rounds spent researching, designing, and building the infrastructure for a remote GPU proving daemon called cuzk, the assistant types a single sentence that marks the boundary between preparation and execution:

"Good. Now let me wire the cuzk client into the task implementations. Let me start with PoRep:"

This is not a dramatic declaration. It is a simple status update paired with a todo-list dump. Yet within the arc of this multi-segment coding session—spanning the design of Phase 12's split GPU proving API, the benchmarking of memory-bandwidth interventions, and the creation of a low-memory deployment guide—this message represents the precise moment when theory becomes practice. The infrastructure is built. The client library compiles. The FFI methods are written. Now the assistant must reach into the heart of Curio's task scheduler and rewire how proof generation flows through the system.

The Road to This Moment

To understand why message 3411 matters, one must appreciate what came before it. The preceding messages (indices 3370 through 3410) document a concentrated burst of infrastructure construction. The assistant had been systematically working through a checklist: first, adding a CuzkConfig struct to Curio's configuration types in deps/config/types.go, complete with sensible defaults for the daemon's address and port. Next came the creation of a Go gRPC client package at lib/cuzk/, generated from protobuf definitions that already existed in the extern/cuzk/cuzk-proto directory. This required installing protoc-gen-go and protoc-gen-go-grpc, wrestling with protobuf import paths, and flattening a nested directory structure that the code generator had produced. Finally, the assistant wrote lib/ffi/cuzk_funcs.go—a new file providing SealCalls methods that split the existing proof pipeline: vanilla proof generation (which requires local sector data) stays on the Curio node, while the GPU-intensive SNARK computation is delegated to the remote cuzk daemon via gRPC.

Each of these steps was validated. The config package compiled. The generated protobuf stubs compiled. The FFI methods passed go vet (the only errors being pre-existing CGO/FVM issues unrelated to the new code). By message 3410, the assistant had confirmed that go build ./lib/cuzk/ succeeded. The stage was set.

The Message as a Pivot

Message 3411 is deceptively brief. Its surface content is a todo-list update showing five completed research items and an implicit "next action" indicator pointing at the PoRep task. But the todo list itself reveals the depth of preparation:

The Reasoning Process

The todo list embedded in the message provides a window into the assistant's reasoning. The items are ordered by priority, with the highest-priority research completed first. But more revealing is what is not on the list. There is no item for "Design integration architecture" or "Decide how to split vanilla vs SNARK proving." Those decisions had already been made in earlier rounds, documented in the todo list shown in message 3393. The assistant had already determined the key architectural principle: the cuzk integration would not modify the existing SealCalls.PoRepSnark method. Instead, it would create new methods (PoRepSnarkCuzk, SnapProveCuzk, etc.) that generate vanilla proofs locally and then submit the result to the daemon. This preserves backward compatibility—nodes without cuzk continue to use the existing GPU path—while enabling a clean separation of concerns.

The assistant also understood the implications for Curio's resource scheduler. When cuzk is enabled, the task's TypeDetails() must report zero GPU and RAM requirements, because the heavy computation happens on a remote machine. The CanAccept() method must query the daemon's queue depth for backpressure, rather than checking local resource availability. These design decisions are not visible in message 3411 itself, but they are the logical consequence of the research the assistant had completed.## Assumptions Embedded in the Approach

The assistant's plan carries several implicit assumptions that deserve scrutiny. The first is that the cuzk daemon will be reachable and responsive when tasks execute. The integration uses gRPC, which implies a network dependency—if the daemon is down, tasks that delegate to it will fail. The assistant's design handles this through the CanAccept() backpressure mechanism: if the daemon's queue is full or unreachable, the task simply won't be accepted for execution. But this assumes that the daemon's health can be determined quickly, and that a transient failure won't cause tasks to be stuck in limbo.

A second assumption concerns the vanilla proof data. The PoRepSnarkCuzk method generates the vanilla proof locally (which requires access to the sector's sealed and unsealed data) and then transmits it to the daemon. This assumes that the vanilla proof is self-contained—that the daemon does not need any additional context about the sector, the miner, or the chain state to complete the SNARK computation. The assistant verified this by reading GeneratePoRepVanillaProof and confirming that it returns a byte slice (the JSON-serialized C1 output), which is all the daemon needs to run SealCommitPhase2.

A third assumption is about the resource accounting model. By zeroing out GPU and RAM requirements in TypeDetails() when cuzk is enabled, the assistant is telling Curio's scheduler that these tasks consume no local resources. This is technically correct—the computation happens elsewhere—but it means the scheduler cannot account for the remote machine's capacity. If multiple Curio nodes all point at the same cuzk daemon, they could collectively submit more work than the daemon can handle. The backpressure in CanAccept() mitigates this, but only if the daemon's queue depth accurately reflects its true capacity.

Input Knowledge Required

To understand message 3411, a reader must be familiar with several layers of the Curio architecture. First, the harmony task framework: tasks implement a TaskInterface with methods like Do() (execute the work), CanAccept() (check if the task can be scheduled), and TypeDetails() (declare resource requirements). Second, the existing proof pipeline: SealCalls.PoRepSnark generates a vanilla proof via GeneratePoRepVanillaProof and then runs the SNARK via SealCommitPhase2. Third, the configuration system: Curio uses a TOML-based config with subsystem-specific structs like CurioSubsystemsConfig. Fourth, the gRPC client pattern: the assistant generated protobuf stubs and created a wrapper client that handles connection management and request/response serialization.

The reader also needs to understand the distinction between "vanilla proof" and "SNARK proof" in Filecoin's PoRep protocol. The vanilla proof is a computational commitment that proves the sector data is correctly encoded; the SNARK proof is a succinct zero-knowledge proof that compresses the vanilla proof into a small, verifiable certificate. Only the SNARK phase requires the GPU-intensive Groth16 proving that cuzk is designed to accelerate.

Output Knowledge Created

Message 3411 itself does not produce new code—it is a transition point. But the knowledge it embodies is the blueprint for the integration that follows. The todo list captures the research outcomes: the assistant now knows the exact signatures of PoRepSnark, ProveUpdate, GeneratePoRepVanillaProof, and the harmony task lifecycle methods. It knows that the proofshare task already demonstrates a pattern for remote proof delegation. It knows that the cuzk client library compiles and is ready for use.

The message also establishes the sequence of implementation. By saying "Let me start with PoRep," the assistant implicitly defines the order of the remaining work: PoRep first, then SnapDeals prove, then proofshare (PSProve). This ordering is reflected in the subsequent messages, where the assistant systematically edits each task file in that exact sequence.

The Thinking Process in the Todo List

The todo list embedded in message 3411 is more than a progress tracker—it is a window into the assistant's mental model. The five completed items are not boilerplate; they represent specific investigative threads that the assistant pursued across dozens of file reads and grep searches. Item 3, "Research: Read lib/ffi SealCalls to understand current proof execution flow," for example, required reading sdr_funcs.go and snap_funcs.go to trace the exact call chain from task execution to FFI invocation. Item 4, "Research: Read proofshare task for remote proving pattern," involved studying how the existing proofshare task already delegates proof computation to a remote service—a pattern that the cuzk integration would replicate.

The todo list also reveals what the assistant considers "done." The research items are marked completed, but the actual implementation items are notably absent from this particular snapshot. The assistant had not yet started writing the integration code for PoRep, Snap, or proofshare. This is consistent with the message's role as a pivot: the research phase is complete, and the implementation phase is about to begin.

Broader Significance

Message 3411 sits at the intersection of two major efforts in this coding session. The first is the optimization of the GPU proving pipeline itself—Phase 12's split API, memory backpressure, and low-memory benchmarking. The second is the integration of that optimized pipeline into Curio's production task orchestrator. Without the integration, the cuzk daemon is a standalone tool that requires manual invocation. With it, Curio can automatically route proof computation to the daemon based on task availability, resource pressure, and queue depth.

This integration is what makes the cuzk daemon practically useful. The dozens of rounds spent designing Phase 12's architecture, benchmarking memory usage, and tuning channel capacities would be academic without the wiring that connects Curio's task scheduler to the daemon's gRPC API. Message 3411 is the moment when those two threads converge—when the assistant shifts from "building the engine" to "installing it in the vehicle."

For a reader following the session, this message provides orientation. The todo list answers the question "What has been accomplished?" while the single sentence "Let me start with PoRep" answers "What comes next?" It is a rare moment of explicit planning in a session that otherwise moves at a rapid, iterative pace—a pause to take stock before diving into the most complex part of the implementation.