Reading the Remaining Targets: A Research-Driven Approach to Curio-cuzk Integration

Message Overview

In message 3371 of a lengthy coding session spanning dozens of segments and thousands of messages, the assistant issues three parallel read tool calls to examine source files from the Curio Filecoin proving node codebase. The files being read are:

  1. lib/ffi/sdr_funcs.go — the SDR (Synthetic Data Replication) functions file containing the PoRepSnark method and other proof-generation entry points
  2. tasks/window/compute_do.go — the WindowPoSt compute execution file containing DoPartition
  3. tasks/proofshare/task_prove.go — the proofshare task implementation At first glance, this appears to be a routine research action: the assistant is reading source code. But in the context of the broader conversation, this message represents a critical transition point — the shift from architectural planning to concrete implementation of a complex distributed proving system. The three files selected are not random; they are the remaining task types that must be wired into the cuzk remote proving daemon, completing the integration across all four proof-related task types in Curio.

The Context: A Multi-Phase Optimization Journey

To understand why this message exists, we must step back and appreciate the arc of the entire session. The conversation began as a deep-dive investigation into the SUPRASEAL_C2 Groth16 proof generation pipeline for Filecoin Proofs-of-Replication (PoRep). The initial work produced a comprehensive background reference document mapping the entire 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. This investigation identified nine structural bottlenecks and produced three optimization proposals.

The session then shifted into an iterative implementation phase, progressing through multiple numbered phases (Phase 10, 11, 12) of the cuzk proving daemon — a standalone GPU proving service designed to offload SNARK computation from Curio nodes. Phase 12, completed just before this message, implemented a split GPU proving API with memory backpressure mechanisms, achieving 37.7 seconds per proof with 12 partition workers. The architecture was documented, benchmarked across nine configurations, and committed as 9bb657e5.

Now, in segment 33 (the current segment), the focus has shifted to the final mile: integrating the cuzk daemon into Curio's task orchestrator so that real Filecoin tasks — PoRep sealing, SnapDeals updates, WindowPoSt, WinningPoSt, and proofshare tasks — can delegate their SNARK computation to the remote daemon rather than running it locally.

What the Message Actually Contains

The message is structurally simple: it contains three read tool calls, each requesting a file from the Curio repository. The assistant does not issue any commands, make any edits, or produce any analysis within this message. It is purely an information-gathering action. The tool calls are:

[read] /home/theuser/curio/lib/ffi/sdr_funcs.go
[read] /home/theuser/curio/tasks/window/compute_do.go
[read] /home/theuser/curio/tasks/proofshare/task_prove.go

The file contents returned show specific line ranges. For sdr_funcs.go, lines 429–439 are displayed, which show a switch statement handling different RegisteredSealProof variants for configuring phase1 output labels. For compute_do.go, the first 18 lines (the package declaration and imports) are shown. For task_prove.go, the first 15 lines (package declaration and imports) are shown.

These are deliberately truncated views — the assistant is not reading entire files but sampling specific regions to understand structure, imports, and patterns.

Why These Three Files? The Reasoning Behind the Selection

The assistant's research strategy, visible across messages 3368–3371, follows a systematic pattern. In message 3369, the assistant read the four primary task files: tasks/seal/task_porep.go, tasks/snap/task_prove.go, tasks/window/compute_task.go, and tasks/winning/winning_task.go. It also read the harmony task framework (harmonytask.go) and the resources package. In message 3370, it read lib/ffi/sdr_funcs.go (partially) and tasks/window/compute_do.go (partially).

Now in message 3371, the assistant returns to three files with specific purposes:

  1. lib/ffi/sdr_funcs.go — This file contains the PoRepSnark function (visible at line 360 in the earlier read), which is the current local implementation of Groth16 proof generation for PoRep. The assistant needs to understand this function's signature and behavior to create the cuzk equivalent. The lines shown (429–439) reveal how different seal proof types are configured — knowledge needed to properly serialize proof requests for the cuzk daemon.
  2. tasks/window/compute_do.go — This file contains the DoPartition method, which is the core execution logic for WindowPoSt partitions. Earlier reads of compute_task.go showed the task structure but not the actual proof execution. The assistant needs to understand how WindowPoSt currently calls the FFI to generate proofs, so it can intercept that call and route it to cuzk instead.
  3. tasks/proofshare/task_prove.go — This is a newer addition to the codebase. The proofshare system allows Curio nodes to share proof computation resources. The assistant needs to understand how this task type works, as it represents a third integration target alongside PoRep and SnapDeals. The selection reveals a deliberate research strategy: the assistant is systematically mapping every code path that currently performs local SNARK computation, so it can intercept each one and redirect it to the cuzk daemon. This is not exploratory reading — it is targeted reconnaissance before surgical modification.## The Assumptions Underlying This Research The assistant's research in message 3371 operates on several key assumptions, some explicit and some implicit: Assumption 1: The cuzk daemon's gRPC API is sufficient for all proof types. The assistant assumes that the SubmitProofRequest / AwaitProof / Prove RPCs defined in the protobuf service (visible in message 3363) can accommodate PoRep C2 proofs, SnapDeals update proofs, WindowPoSt partition proofs, WinningPoSt proofs, and proofshare proofs. This is a significant architectural assumption — if any proof type requires different data or produces different output formats, the integration will need adaptation. Assumption 2: Vanilla proof generation remains local. Throughout the planning (message 3366), the assistant has maintained a clear separation: vanilla proof generation (the computationally lighter pre-processing step) stays local, while only the Groth16 SNARK computation (the heavy GPU-bound step) is offloaded to cuzk. This assumption is visible in the plan's language: "SNARK computation is delegated to the cuzk daemon while vanilla proof generation remains local." Assumption 3: The existing task lifecycle patterns are consistent across all task types. The assistant assumes that PoRep, SnapDeals, WindowPoSt, WinningPoSt, and proofshare tasks all follow the same harmonytask.TaskInterface pattern with Do(), CanAccept(), and TypeDetails() methods. This is a reasonable assumption given the harmony framework's design, but each task type may have idiosyncrasies. Assumption 4: The lib/ffi package provides the right abstraction boundary. By reading sdr_funcs.go and understanding the SealCalls struct and its PoRepSnark method, the assistant assumes that the FFI layer is the correct interception point. The plan calls for creating lib/ffi/cuzk_funcs.go to provide new methods that generate vanilla proofs locally and then submit them to cuzk via gRPC.

Input Knowledge Required

To fully understand message 3371, a reader needs substantial context from earlier in the session:

  1. The cuzk daemon architecture: Knowledge that cuzk is a standalone GPU proving service that communicates via gRPC, manages its own SRS memory, and exposes a queue-based API with SubmitProof, AwaitProof, Prove, and GetStatus RPCs.
  2. The Curio harmony task framework: Understanding that Curio uses a task orchestrator called "harmony" where tasks implement TaskInterface with methods like Do() (execute the task), CanAccept() (check if the node can take more work), and TypeDetails() (report resource requirements). The scheduler uses these to dispatch tasks across workers.
  3. The Phase 12 split API: Knowledge that the cuzk daemon now supports a split proving API where the GPU-critical path is decoupled from CPU post-processing, enabling memory backpressure and efficient resource utilization.
  4. The existing FFI layer: Understanding that lib/ffi wraps CGo FFI calls to the Filecoin FFI library, providing Go-friendly methods like PoRepSnark, SnapProve, and partition proving functions.
  5. The four task types: PoRep (sealing new sectors), SnapDeals (proving replica updates), WindowPoSt (proving sector health in windows), WinningPoSt (proving for block rewards), and proofshare (a newer system for sharing proof computation).

Output Knowledge Created

This message does not directly produce new knowledge — it is a research action. However, the act of reading these files generates knowledge that will be used in subsequent messages:

  1. Understanding of compute_do.go's structure: The assistant now knows that WindowPoSt's core execution logic lives in a separate file from the task definition. The DoPartition function is where the actual proof computation happens, and this is where the cuzk interception must be placed.
  2. Understanding of task_prove.go's imports and dependencies: The proofshare task imports ffi "github.com/filecoin-project/filecoin-ffi" and proof2 "github.com/filecoin-project/go-state-types/proof", revealing that it uses the standard FFI for proof generation and the proof types from go-state-types.
  3. Understanding of proof type configuration in sdr_funcs.go: The lines 429–439 show how different seal proof variants (StackedDrg2KiBV1, StackedDrg8MiBV1, etc.) are configured with different layer counts and sizes. This knowledge is essential for properly constructing proof requests to the cuzk daemon, which needs to know the proof type to select the right circuit parameters.
  4. Confirmation of the interception pattern: By examining these files alongside the earlier reads, the assistant confirms that all four task types follow a consistent pattern: they call into lib/ffi for proof generation, and the FFI layer is the correct interception point for routing to cuzk.## The Thinking Process: A Window into Deliberate Engineering While message 3371 itself contains no explicit reasoning text (it is purely tool calls), the thinking process is visible through the sequence of actions across messages 3368–3371. The assistant's reasoning unfolds in a clear pattern: Phase 1: Strategic Planning (Message 3366) — The assistant produces a comprehensive integration plan covering configuration, gRPC client generation, scheduler modification, and task wiring. This plan shows deep understanding of both the cuzk daemon's API and Curio's harmony framework. Phase 2: Research Triage (Message 3368) — The assistant creates a structured todo list with research priorities, explicitly marking "Research: Read existing task implementations" as in_progress. The list includes reading task implementations, the harmony framework, and the FFI layer — showing a methodical approach. Phase 3: Parallel File Reads (Messages 3369–3370) — The assistant reads the four primary task files plus the harmony framework and resources package. These reads are comprehensive, showing the full file contents (or substantial portions) to understand existing patterns. Phase 4: Targeted Deep Reads (Message 3371) — Now the assistant drills deeper into specific files that were partially read or not yet examined. The three files selected represent: - The FFI layer's proof configuration logic (sdr_funcs.go lines 429–439) - The WindowPoSt execution path (compute_do.go) - The proofshare task (task_prove.go) This progression reveals a classic "top-down then bottom-up" research strategy: first understand the high-level task structure, then drill into the specific execution paths that need modification. The choice to read sdr_funcs.go at lines 429–439 specifically is telling. The assistant is not reading the file from the beginning — it is jumping to the proof type configuration section. This suggests the assistant already knows the file's structure from the earlier partial read in message 3370 (which showed lines 350–361, including the PoRepSnark function signature). Now it wants to see how proof types are configured, which is essential for mapping proof types to cuzk's ProofKind enum.

Mistakes and Potential Pitfalls

While the assistant's research is thorough, several potential issues deserve examination:

1. The compute_do.go read is too shallow. The assistant only sees the first 18 lines (imports and package declaration). The actual DoPartition function — which is the critical interception point — is not shown. The assistant will need to read more of this file in a subsequent message to understand the full execution flow. This suggests the assistant is taking a "just enough to proceed" approach, reading only what it needs to confirm the file exists and understand its structure before moving on.

2. The proofshare task may have unique requirements. The proofshare system allows multiple Curio nodes to share proving resources. If a task is being executed on behalf of another node, the cuzk integration may need to pass additional metadata (the requesting node's identity, payment information, etc.) that is not present in the other task types. The assistant has not yet investigated this.

3. The assumption that CanAccept() can reliably query cuzk's queue status. If the cuzk daemon is unreachable or slow to respond, CanAccept() could become a bottleneck in the task scheduling loop. The plan does not address caching, timeouts, or fallback behavior for the status query.

4. Zeroing out resource costs in TypeDetails() may have unintended consequences. The harmony scheduler uses resource costs for task prioritization and worker allocation. If all cuzk-enabled tasks report zero GPU and RAM costs, the scheduler may over-allocate other resource-intensive tasks, causing local resource contention.

The Broader Significance

Message 3371 represents a critical inflection point in the integration effort. The assistant has moved from high-level planning (message 3366) through initial research (messages 3368–3370) to targeted deep research of the remaining integration targets. After this message, the assistant will have the complete picture of all code paths that need modification, and can begin the actual implementation.

The three files selected are the last unknowns. The PoRep task (task_porep.go) and SnapDeals task (task_prove.go) have already been read and understood. The WindowPoSt task definition (compute_task.go) has been read, but its execution logic (compute_do.go) is in a separate file. The proofshare task (task_prove.go) is entirely new territory. Once these three files are understood, the assistant can proceed with confidence to modify all five task types.

In the broader arc of the session, this message is the final research step before a wave of implementation. The next messages (as seen in the chunk summary for Chunk 1) will systematically wire the cuzk client into all three task types, resolving LSP errors and iterating on function signatures. Message 3371 is the calm before the storm — the last moment of pure understanding before the code changes begin.

Conclusion

Message 3371, despite its superficial simplicity as three file reads, reveals the methodical, research-driven approach of an experienced engineer integrating a complex distributed system. The assistant does not jump into implementation without understanding the existing code; it systematically reads every file that will be touched, building a mental model of the codebase before making changes. The three files selected — sdr_funcs.go, compute_do.go, and task_prove.go — represent the last pieces of the puzzle, completing the assistant's understanding of all proof-generation code paths in Curio.

This approach — research first, implement second — is characteristic of high-quality software engineering, especially when modifying production systems where mistakes can cause data loss or consensus failures. The assistant's deliberate, structured research in message 3371 sets the stage for the implementation that follows, ensuring that when the code changes begin, they will be grounded in a thorough understanding of the existing architecture.