The Quiet Edit: Wiring Remote GPU Proving into Curio's SnapDeals Task

Message Overview

The subject message is deceptively brief:

[assistant] Now update the Do() method to use cuzk: [edit] /home/theuser/curio/tasks/snap/task_prove.go Edit applied successfully.

At first glance, this appears to be nothing more than a routine code edit confirmation — a single line of intent followed by a tool result. Yet this message sits at the inflection point of a major architectural transformation: the integration of a remote GPU proving daemon (cuzk) into the Curio Filecoin storage miner's task orchestrator. The edit it describes is the moment the SnapDeals prove task crosses over from local GPU-bound proof generation to a split architecture where vanilla proof computation stays on the storage machine while the heavy SNARK computation is shipped to a remote daemon. Understanding why this single edit matters requires unpacking the full context of the integration effort, the architectural decisions that preceded it, and the assumptions baked into the design.

The Broader Context: A Remote GPU Proving Pipeline

The message belongs to segment 33 of a long-running opencode session focused on the cuzk proving engine — a custom Groth16 proof generation pipeline for Filecoin's Proof-of-Replication (PoRep) and related proofs. The session had just completed Phase 12 of the cuzk engine itself, which involved implementing a split GPU proving API with memory backpressure, benchmarking memory scaling across nine configurations, and establishing RAM tier recommendations (128 GiB minimum, 256 GiB sweet spot, 512+ GiB optimal). With the proving engine itself stable, the focus shifted to integration: wiring cuzk into Curio's task orchestrator so that production tasks could actually use it.

The integration plan, laid out in the preceding messages, was methodical. It involved four layers:

  1. Configuration: Adding a CuzkConfig section to Curio's configuration types (deps/config/types.go) with a gRPC endpoint address and an enable flag.
  2. Client library: Creating a Go gRPC client package (lib/cuzk/client.go) generated from the existing protobuf definitions, wrapping the connection, queue status queries, and proof submission.
  3. Bridge functions: Writing lib/ffi/cuzk_funcs.go — methods on the existing SealCalls struct that split the proof pipeline: generate the vanilla proof locally (because it needs sector data on disk) and then submit the vanilla proof to the cuzk daemon for the GPU-intensive SNARK computation.
  4. Task wiring: Modifying each task type (PoRep, SnapDeals, WindowPoSt, WinningPoSt, proofshare) to optionally delegate SNARK computation to cuzk when the daemon is enabled. By message 3432, the assistant had already completed the PoRep task wiring and was working through the SnapDeals prove task. Messages 3430–3431 added the cuzk client import and field to the SnapDeals task struct. Message 3432 is the next logical step: updating the Do() method — the method that actually executes the task's work — to call the cuzk-aware proof function instead of the local one.

What the Edit Actually Does

The Do() method in tasks/snap/task_prove.go is the heart of the SnapDeals prove task. It is called by Curio's harmony task scheduler when the task is ready to execute. In the original code, Do() would call something like sb.PoRepSnark(...) or sb.SnapProve(...) — methods that run the entire proof pipeline locally, including the GPU-bound SNARK computation that consumes ~200 GiB of memory and requires a high-end GPU.

The edit in message 3432 changes Do() to check whether the cuzk client is initialized. If it is, the method calls the new cuzk-aware variant — for example, PoRepSnarkCuzk(...) from lib/ffi/cuzk_funcs.go — which generates the vanilla proof locally and then sends it to the cuzk daemon via gRPC. If cuzk is not configured, the original local path is preserved.

This is a textbook example of the strategy pattern: the task's execution logic is swapped out based on configuration, without changing the task lifecycle or the scheduler's view of the task.

Architectural Decisions and Assumptions

Several important design decisions are visible in the lead-up to this edit, and they reveal the assistant's assumptions about the system.

Decision 1: Split the proof pipeline at the vanilla/SNARK boundary. The assistant recognized that the proof pipeline has two distinct phases: vanilla proof generation (CPU-bound, requires direct access to sector data on disk) and SNARK computation (GPU-bound, memory-intensive, can be remote). By splitting at this boundary, the storage machine only needs to do the lightweight vanilla generation, while the expensive GPU work is offloaded. This assumes that the vanilla proof output (a JSON-serialized byte array) is small enough to transmit over gRPC without significant overhead — a reasonable assumption given that it's a few kilobytes at most.

Decision 2: Zero out local resource costs when cuzk is enabled. In the preceding edits to TypeDetails(), the assistant set GPU and RAM requirements to zero when the cuzk client is present. This tells Curio's scheduler that the task requires no local GPU or significant RAM, allowing it to schedule more tasks on the same machine. The assumption here is that the scheduler uses these resource costs for capacity planning and that zeroing them is safe because the real GPU work happens elsewhere. This is correct for the scheduler's local view, but it does mean the scheduler loses visibility into the daemon's actual load — which is why CanAccept() was also modified to query the daemon's queue depth for backpressure.

Decision 3: Use CanAccept() for backpressure rather than resource accounting. Rather than trying to model the daemon's GPU capacity in Curio's local resource tracker, the assistant chose to have CanAccept() — the method that determines whether the task engine should accept a new task — query the daemon's queue status via gRPC. This is a pragmatic choice: the daemon knows its own capacity, and querying it directly avoids duplicating state. The assumption is that the gRPC call is fast enough to be made in the scheduling hot path, which is reasonable if the daemon is on the same network.

Decision 4: Follow the PoRep pattern exactly. The assistant had already wired the PoRep task before starting on SnapDeals. The SnapDeals integration mirrors the PoRep changes exactly: add the client field, update the constructor, modify Do(), modify CanAccept(), modify TypeDetails(). This consistency is intentional — it minimizes the chance of introducing task-specific bugs and makes the integration easier to review and maintain.

Input Knowledge Required

To understand what message 3432 is doing, a reader needs to know:

Output Knowledge Created

This edit produces a concrete change to the codebase: the SnapDeals prove task now has a conditional execution path that delegates SNARK computation to the cuzk daemon when configured. The output knowledge includes:

The Thinking Process Visible in the Message Sequence

While message 3432 itself contains no explicit reasoning — it is a bare edit command — the surrounding messages reveal the assistant's systematic approach. The assistant is working through a todo list, task by task, in a consistent order:

  1. Research (messages 3381–3411): Read existing task implementations, understand the harmony task framework, examine the proof pipeline, study the proofshare task's remote proving pattern.
  2. Infrastructure (messages 3387–3416): Add config types, generate protobuf stubs, create the gRPC client wrapper, write the bridge functions in cuzk_funcs.go.
  3. PoRep wiring (messages 3420–3427): Add client field, update constructor, modify Do(), CanAccept(), TypeDetails(). Verify with go vet.
  4. SnapDeals wiring (messages 3429–3434): Same pattern as PoRep. Message 3430 adds the import and field. Message 3432 updates Do(). Messages 3433–3434 update CanAccept() and TypeDetails().
  5. Proofshare wiring (messages 3436–3440): More complex due to the computeProof helper function, but follows the same conceptual pattern. The assistant's thinking is visibly shaped by the principle of minimum surprise: each task is modified in the same way, using the same field name, the same conditional check, and the same delegation pattern. There is no task-specific optimization or deviation — the goal is to get the integration working correctly and consistently before optimizing individual paths.

Mistakes and Incorrect Assumptions

Within the scope of message 3432 itself, there are no visible mistakes — the edit was applied successfully and the subsequent go vet (message 3435) showed no Go-level errors. However, examining the broader sequence reveals a subtle assumption that could become problematic: the assistant assumes that the SnapDeals proof pipeline has the same vanilla/SNARK split as PoRep. While both proof types do have this split, the SnapDeals pipeline uses ReadSnapVanillaProof instead of GeneratePoRepVanillaProof, and the SNARK function may have different parameters. The cuzk_funcs.go bridge methods need to handle these differences. If the assistant wrote a generic SnapProveCuzk method that doesn't account for SnapDeals-specific parameters, the edit in message 3432 would compile but produce incorrect proofs at runtime.

This is not a flaw in the edit itself but in the assumption that the PoRep pattern maps perfectly onto SnapDeals. The proofshare task integration that follows (messages 3436–3440) reveals additional complexity — the computeProof function signature had to be updated to accept the cuzk client, and LSP errors required several rounds of corrections. This suggests that the SnapDeals integration may also need similar signature adjustments that aren't visible in the brief edit message.

Conclusion

Message 3432 is a study in minimalism. It is four lines of conversation — a statement of intent, a file path, and a success confirmation — yet it represents the culmination of dozens of preceding messages of research, planning, and infrastructure work. The edit it describes is the moment the SnapDeals prove task gains the ability to offload its most expensive computation to a remote GPU daemon, transforming Curio's proof pipeline from a monolithic local process into a distributed architecture. In the broader narrative of the cuzk integration, this message is not the most dramatic or complex, but it is the one where the design becomes reality: the code path splits, and the GPU work leaves the machine.