The Critical Edit: Wiring Remote GPU Proving into Curio's PoRep Task

Message Overview

[assistant] Now update Do() to use cuzk when available: [edit] /home/theuser/curio/tasks/seal/task_porep.go Edit applied successfully.

This single message, appearing at index 3424 in a sprawling opencode session spanning dozens of segments and thousands of messages, represents a deceptively small but architecturally pivotal moment. The assistant issues one edit command to modify the Do() method of the PoRepTask struct in Curio's seal task subsystem. On the surface, it is a three-line change. In reality, it is the keystone of an integration that fundamentally rearchitects how Filecoin proof generation is distributed across a cluster. Understanding why this particular edit matters, and what it presupposes, requires unpacking the entire chain of reasoning that led to it.

The Context: A Multi-Phase Optimization Journey

To grasp the significance of message 3424, one must first understand the journey that preceded it. The opencode session began as a deep-dive investigation into the SUPRASEAL_C2 Groth16 proof generation pipeline for Filecoin's Proof-of-Replication (PoRep). The assistant had spent the earlier segments (segments 0 through 32) exhaustively mapping the call chain from Curio's Go task layer through Rust FFI into C++/CUDA kernels, accounting for the pipeline's ~200 GiB peak memory footprint, identifying nine structural bottlenecks, and proposing three composable optimization proposals: Sequential Partition Synthesis, Persistent Prover Daemon, and Cross-Sector Batching.

By segment 28, the focus had shifted from pure analysis to implementation. The assistant designed, benchmarked, and iterated on a series of phases (Phase 10 through Phase 12) for the cuzk proving daemon — a persistent GPU process that eliminates SRS loading overhead and manages memory more efficiently than the one-shot proof generation approach. Phase 12 introduced a split API that decoupled GPU worker critical path from CPU post-processing, and by segment 32, the assistant had consolidated Phase 12 documentation, performed a systematic low-memory benchmark sweep, and established deployment guidance for various system sizes.

Segment 33, where message 3424 resides, marks the transition from daemon implementation to Curio integration. The cuzk daemon exists and works; now it must be wired into Curio's task orchestrator so that real Filecoin mining tasks — PoRep (seal), SnapDeals prove, and proofshare — can delegate their SNARK computation to it.

The Four-Step Plan

In message 3420, the assistant laid out a clear four-step plan for wiring cuzk into the PoRep task:

  1. Add cuzkClient *cuzk.Client field to PoRepTask
  2. In TypeDetails(), when cuzk is enabled, zero out GPU/RAM requirements
  3. In CanAccept(), when cuzk is enabled, use backpressure from daemon
  4. In Do(), when cuzk is enabled, call PoRepSnarkCuzk instead of PoRepSnark Messages 3422 and 3423 handled step 1: adding the import, the struct field, and updating the constructor. Message 3424 handles step 4 — the Do() method. Messages 3425 and 3426 would then handle steps 3 and 2 respectively. This sequencing is deliberate: the Do() method is the heart of the task, where the actual proof computation occurs. Without modifying Do(), the other changes are meaningless scaffolding.

What the Edit Actually Changes

The edit to Do() is conceptually straightforward but architecturally significant. The existing Do() method called sb.PoRepSnark(...) — a method on SealCalls that performed both vanilla proof generation (CPU-bound, requires sector data) and the Groth16 SNARK computation (GPU-bound, memory-intensive). The new Do() method, when cuzkClient != nil, calls sb.PoRepSnarkCuzk(...) instead — a method written in message 3416 that splits the workflow: it generates the vanilla proof locally (because it needs access to the sector data on local storage) and then sends only the SNARK computation to the cuzk daemon via gRPC.

This split is the core architectural insight. The vanilla proof generation is inherently tied to the machine that holds the sealed sector data — you cannot offload it. But the SNARK computation, which consumes ~200 GiB of peak memory and requires expensive GPU time, can be centralized on dedicated proving machines. By splitting these two phases, Curio can use cheap, low-resource machines for storage and polling, while directing the expensive GPU work to a pool of cuzk daemons.

The Reasoning Behind the Design

The assistant's decision to check cuzkClient != nil as the branching condition reflects a deliberate design philosophy: the cuzk integration is opt-in and backward-compatible. If no cuzk daemon is configured, the task falls through to the existing local proving path. This is essential for a production system where operators may gradually migrate from local to remote proving.

The choice to create PoRepSnarkCuzk as a separate method on SealCalls (rather than modifying PoRepSnark itself) preserves the existing API and avoids introducing gRPC dependencies into the core FFI layer. The new method lives in a dedicated file lib/ffi/cuzk_funcs.go, keeping the concern isolated. This is a textbook application of the Open/Closed Principle: the existing code is closed for modification but open for extension.

Assumptions Embedded in the Edit

This edit makes several assumptions that are worth examining:

  1. The cuzkClient field exists and is properly initialized. Message 3423 had just added this field and updated the constructor. The assistant assumes that edit succeeded and that the field will be non-nil when cuzk is enabled.
  2. PoRepSnarkCuzk is correctly implemented. The method was written in message 3416 but never explicitly tested in isolation. The assistant assumes it correctly generates the vanilla proof locally, serializes it, sends it to cuzk via gRPC, receives the SNARK proof back, and verifies it.
  3. The gRPC client is thread-safe. The cuzkClient is shared across potentially many task instances. The assistant assumes the underlying gRPC connection pool handles concurrent requests correctly.
  4. The cuzk daemon is reachable and responsive. The Do() method does not implement retry logic or fallback to local proving if the daemon is unreachable. If cuzk is configured but unavailable, the task will fail.
  5. The vanilla proof generation does not require GPU resources. This is true by design — GeneratePoRepVanillaProof is CPU-only — but it means the local machine still needs access to the sector data and sufficient CPU capacity.

Potential Mistakes and Risks

The most significant risk in this edit is the lack of a fallback path. If the cuzk daemon is down or the gRPC call fails, the task fails. In a production mining operation, missing a PoRep deadline can result in lost collateral. A more robust design might include a timeout with fallback to local proving, or at least exponential backoff with alerting.

Another subtle issue is the interaction with Curio's resource accounting. In step 2 (message 3426), TypeDetails() will zero out GPU and RAM requirements when cuzk is enabled. This means Curio's scheduler will treat cuzk-enabled tasks as consuming no local resources, potentially scheduling many of them concurrently. If the vanilla proof generation (which still runs locally) has significant CPU or memory costs, this could overload the machine. The assistant's assumption is that vanilla proof generation is lightweight enough to be ignored, but this should be validated.

Input Knowledge Required

To fully understand this edit, one needs:

Output Knowledge Created

This edit produces a concrete behavioral change: PoRep tasks running on a Curio instance configured with a cuzk daemon will now delegate their SNARK computation to that daemon. The local machine handles only vanilla proof generation and gRPC communication. This has several downstream effects:

The Broader Integration Pattern

Message 3424 is the first of three similar edits in this chunk. After PoRep, the assistant will wire SnapDeals prove (messages 3428-3432) and proofshare (messages 3433-3445) using the same pattern. Each task type has its own Do() method, its own vanilla proof generation path, and its own cuzk RPC. But the pattern is consistent: add a client field, modify the constructor, update Do() to branch on client availability, update CanAccept() for backpressure, and update TypeDetails() to zero resources.

This consistency is a hallmark of well-architected systems. The harmony task framework provides a uniform interface, and the cuzk integration slots into it with minimal friction. The assistant's methodical, edit-by-edit approach — one change per message, verify as you go — reflects an understanding that complex integrations are best built incrementally, with each small step building on the last.

Conclusion

Message 3424, in isolation, is a single edit to a single method. But it is the culmination of hundreds of preceding messages spanning architecture analysis, memory optimization, daemon implementation, benchmarking, and integration planning. It represents the moment when the theoretical architecture of remote GPU proving becomes concrete code. The edit is small because the groundwork was thorough. The reasoning is invisible in the edit itself — it lives in the chain of decisions that made this three-line change the obvious, correct next step.