The Art of the Small Edit: Zeroing Resources in a Remote Proving Architecture

At first glance, message [msg 3454] appears to be one of the most mundane events in a software engineering session: a single edit to a single file, reported in a single line:

First, update TypeDetails() in PSProve to zero GPU/RAM when cuzk is enabled: [edit] /home/theuser/curio/tasks/proofshare/task_prove.go Edit applied successfully.

Yet within this deceptively simple action lies the culmination of an architectural shift that had been building across dozens of messages, multiple codebases, and three distinct task types. This edit — the modification of a resource accounting method in a Go task file — represents the final integration point where a remote GPU proving daemon (cuzk) is fully recognized by Curio's harmony task scheduler as an external resource provider rather than a local computation. Understanding why this one-line change matters requires tracing the reasoning, assumptions, and architectural decisions that led to it.

The Architectural Context: Remote Proving as a Design Principle

The cuzk proving daemon is a persistent, GPU-resident SNARK proving engine that operates independently from Curio. Rather than embedding GPU proof generation inside Curio's process — which would require the scheduler to manage GPU memory, CUDA context lifetimes, and the ~200 GiB peak memory footprint of Groth16 proofs — the team chose a fundamentally different architecture: Curio delegates proof computations to cuzk over gRPC, treating it as a networked service.

This architectural decision had profound implications for Curio's harmony task scheduler. The scheduler normally uses a resource accounting system where each task type declares its GPU and RAM requirements via the TypeDetails() method. The scheduler then uses these declarations to decide how many tasks of each type can run concurrently without exhausting system resources. But when cuzk is enabled, the GPU computation doesn't happen locally — it happens on the remote daemon. The local Curio process only performs the CPU-bound vanilla proof generation (reading sector data from disk, computing Merkle proofs) and then sends the result to cuzk for the heavy SNARK computation.

This creates a fundamental tension: if the scheduler continues to account for GPU and large RAM resources locally, it will severely underutilize the system. The local machine doesn't need 200 GiB of RAM or a GPU for these tasks — it only needs CPU and disk I/O to prepare vanilla proofs. The real resource constraints are on the cuzk daemon's side: its GPU memory, its queue depth, its processing throughput.

The Pattern Established: PoRep and Snap as Precedent

The decision to zero GPU and RAM in TypeDetails() was not made in isolation. By the time the assistant reached the PSProve task in [msg 3454], the pattern had already been established in two other task types. In earlier messages ([msg 3446]), the assistant had already modified tasks/seal/task_porep.go and tasks/snap/task_prove.go to implement exactly this pattern: when a cuzkClient is present (indicating cuzk is enabled), TypeDetails() returns zero for GPU and large RAM resources.

The assistant's TODO list from [msg 3453] shows this clearly:

1. PSProve TypeDetails/CanAccept: Need to update TypeDetails() to zero GPU/RAM when cuzk is enabled, and update CanAccept() with cuzk backpressure (similar to PoRep/Snap patterns).

The phrase "similar to PoRep/Snap patterns" reveals the reasoning: this is a systematic application of a proven design. The assistant had already read both the PoRep and Snap task files ([msg 3450], [msg 3451]) to understand the exact implementation pattern before applying it to PSProve. This is not creative design work — it is disciplined, consistent application of an architectural decision already made.

The Reasoning Behind Zeroing Resources

Why zero GPU and RAM specifically? The answer lies in how the harmony scheduler works. Each task type implements TaskTypeDetails(), which returns a Resources struct containing CPU, GPU, RAM, and other resource estimates. The scheduler uses these estimates to decide whether a task can be accepted — if accepting a task would exceed available resources, the task is deferred.

When cuzk is enabled, the local Curio process does not perform GPU computation. The Do() method for PSProve (already modified in earlier messages [msg 3438]–[msg 3444]) branches on whether cuzkClient is non-nil. If it is, the method calls computeProof with the cuzk client, which in turn calls the remote daemon for SNARK computation. The local machine's GPU and RAM are irrelevant to this workflow.

However, there is a subtlety: the local machine still needs some resources. It needs CPU time to generate vanilla proofs and memory to hold sector data read from disk. Zeroing GPU and RAM does not mean the task requires no resources — it means those specific resource categories are managed externally. The CPU and disk resources are still accounted for by the scheduler's default mechanisms.

The Assumptions Embedded in This Edit

This single edit carries several assumptions, some explicit and some implicit:

First assumption: The cuzk daemon manages its own resources. The edit assumes that the remote daemon has its own resource accounting and that backpressure from the daemon's queue (via GetStatus in CanAccept()) is sufficient to prevent overload. This is a delegation of responsibility — Curio trusts cuzk to tell it when it's too busy.

Second assumption: Zeroing resources is safe for scheduler correctness. The harmony scheduler might have edge cases where a task with zero declared resources behaves unexpectedly. For example, if the scheduler uses resource declarations to prioritize tasks or to detect starvation, zeroing resources could mask a task's true requirements. The assistant implicitly assumes that the scheduler treats zero resources as "don't account for this" rather than "this task needs nothing."

Third assumption: The PSProve task follows the same pattern as PoRep and Snap. This is the core design assumption — that all three task types have sufficiently similar resource profiles that the same TypeDetails() modification applies uniformly. The assistant verified this by reading the existing code, but the assumption is structural: if PSProve had different resource accounting needs (e.g., if it used GPU for something other than SNARK proving), the pattern would not apply.

Fourth assumption: The cuzk client is already wired into the task struct. This edit only modifies TypeDetails(). It depends on the cuzkClient field already being present in the TaskProvideSnark struct and being set correctly during task construction. Earlier messages ([msg 3438]–[msg 3444]) had already added this field and threaded it through the constructor and compute functions.

Input Knowledge Required

To understand this edit, one must understand several layers of the system:

  1. The harmony task scheduler model: How Curio's scheduler uses TypeDetails() for resource accounting, and how CanAccept() provides additional admission control.
  2. The cuzk daemon architecture: That it is a separate process with its own GPU and memory, accessed via gRPC, and that it has a queue-based backpressure mechanism via GetStatus().
  3. The three task types: PoRep C2 (seal), SnapDeals Prove (snap), and PSProve (proofshare) — each generates Groth16 SNARK proofs but with different vanilla proof preparation steps.
  4. The existing patterns: The PoRep and Snap task files had already been modified with the same pattern, establishing a precedent.
  5. Go and the Curio codebase: The specific structure of tasks/proofshare/task_prove.go, the TaskProvideSnark type, and the TypeDetails() method signature.

Output Knowledge Created

This edit produces a single, specific change: when cuzk is enabled, the PSProve task type reports zero GPU and large RAM requirements to the harmony scheduler. This has cascading effects:

The Thinking Process Visible

The assistant's thinking process is visible in the sequence of messages leading up to [msg 3454]. In [msg 3453], the assistant reads the CuzkConfig section of the config file, confirming the configuration structure. In [msg 3450] and [msg 3451], it reads the PoRep and Snap task files to verify the pattern. The TODO list in [msg 3453] shows the explicit prioritization: "Update PSProve TypeDetails/CanAccept with cuzk backpressure" is marked as high priority and in progress.

The assistant is working methodically through a checklist. It does not rush to implement — it reads the existing code, confirms the pattern, and then applies the change. The edit itself is reported as a single line because the tool call succeeded, but the preparation involved reading multiple files across multiple packages.

The Broader Significance

This edit is the final piece of a three-task integration pattern. With this change, all three task types — PoRep C2, SnapDeals Prove, and PSProve — now correctly report zero local GPU and RAM resources when cuzk is enabled. The architectural shift from local to remote proving is complete at the resource accounting level.

But the work is not done. As the TODO list shows, the CanAccept() method still needs to be updated to provide cuzk-based backpressure, and the cuzk client initialization needs to be wired into the task construction in cmd/curio/tasks/tasks.go. The TypeDetails() edit is a necessary precondition for those changes — without it, the scheduler would continue to reserve local resources for tasks that don't need them, and the CanAccept() backpressure would be fighting against the scheduler's own resource accounting.

In the end, this small edit is a testament to the power of consistent architectural patterns. By establishing a pattern in two task types and then applying it to the third, the assistant ensures that the cuzk integration behaves uniformly across all proof generation workflows. The edit itself is trivial; the reasoning behind it is anything but.