Systematic Elimination: Tracing Randomization in GPU Proof Generation

The Scene: A Deep Investigation into Intermittent Proof Failures

In the middle of an intensive debugging session spanning dozens of messages, the assistant reaches a quiet but pivotal moment. The investigation concerns an intermittent production failure in a GPU-accelerated proving system called "cuzk" — a custom proving daemon that generates SNARK proofs for Filecoin's Proof-of-Replication (PoRep) protocol. The symptom is stark: the cuzk system produces a proof that passes its own internal Rust-side verification, but when the Go caller receives the proof bytes and runs VerifySeal, it returns false. The proof is invalid. This means either the proof bytes are being corrupted in transit, or the Go and Rust sides are using subtly different verification inputs.

The assistant has been systematically tracing every link in the chain. Previous messages ([msg 1762] through [msg 1789]) established that the Go JSON serialization round-trip is not the culprit, that the 2KiB test-sector flakiness is a separate bellperson issue, and that the production failure specifically occurs on the cuzk GPU-accelerated path while the CPU-based FFI path succeeds. This points to something unique about the GPU proving backend — the cuda-supraseal feature in the forked bellperson library.

The Message: A Moment of Verification

Message 1790 captures a precise moment in the elimination process. The assistant writes:

Both native and supraseal use E::Fr::ZERO for r/s when no randomization is provided. OK so that's the same.

Now let me check the create_random_proof_batch function which is called from compound_proof.rs: [read] /tmp/czk/extern/bellperson/src/groth16/prover/mod.rs

This short message packs significant meaning. The assistant has just finished reading two files — the supraseal prover (supraseal.rs) and the native prover (native.rs) — and extracted a specific detail: the randomization values r and s used during proof generation. Both implementations fall back to E::Fr::ZERO when no randomization is provided (which is the default case). The assistant confirms this parity with a simple "OK so that's the same" and immediately pivots to the next candidate: the create_random_proof_batch function.

The message is not just a statement of fact; it is a decision node in a branching investigation. The randomization hypothesis has been ruled out, so the search continues elsewhere. The assistant issues a read tool call to inspect the module file that dispatches between the native and supraseal backends, looking for the next potential difference.

The Broader Investigation Context

To understand why this message matters, one must appreciate the architecture under scrutiny. The cuzk system uses a forked version of the bellperson library — a zk-SNARK proving framework. This fork adds a cuda-supraseal feature flag that enables a C++ GPU backend called SupraSeal for the computationally intensive Groth16 proof generation. The standard (native) prover uses pure Rust with CPU-based multiexponentiation.

The key architectural insight is in prover/mod.rs:

#[cfg(not(feature = "cuda-supraseal"))]
mod native;
#[cfg(feature = "cuda-supraseal")]
pub mod supraseal;

The module structure itself is conditionally compiled. When cuda-supraseal is enabled, the supraseal module is used; otherwise, the native module. Both modules expose the same interface — they implement create_random_proof_batch — but the internal implementation differs radically. The native prover uses CPU-based elliptic curve operations and multiexponentiation, while the supraseal prover delegates to C++ GPU kernels.

The production failure occurs specifically on the GPU path. The assistant's working hypothesis is that some difference between these two implementations causes the GPU path to occasionally produce proofs that fail verification. The message captures the moment when one potential difference — the randomization values — is ruled out.

The Reasoning Process: Systematic Elimination

The assistant's thinking process in this message exemplifies a rigorous debugging methodology. Rather than guessing at the root cause, the assistant is performing a systematic comparison of the two code paths, checking each component for differences.

The reasoning visible in the message follows this pattern:

Formulate a hypothesis: "Perhaps the native and supraseal provers use different randomization values for the Groth16 proof, which could cause different proofs to be generated."
Check the evidence: Read both supraseal.rs (lines 47-50) and native.rs to find the randomization logic. Both use E::Fr::ZERO as the default when no randomization is provided.
Draw a conclusion: "Both native and supraseal use E::Fr::ZERO for r/s when no randomization is provided. OK so that's the same." The hypothesis is rejected.
Pivot to the next hypothesis: "Now let me check the create_random_proof_batch function which is called from compound_proof.rs." The assistant moves up the call chain to examine how the batch proving function is invoked and whether there are differences in how the native and supraseal versions handle the batch. This is textbook debugging: isolate variables, test one at a time, document the result, and move to the next candidate. The assistant does not need to fix anything in this message — it is gathering information to narrow the search space.

Assumptions and Required Knowledge

The message operates on several layers of implicit knowledge that the reader must understand to fully appreciate its significance:

Input knowledge required:

Understanding of Groth16 zk-SNARKs and the role of randomization (r, s values) in the proof generation process. In Groth16, the prover uses random blinding factors to ensure zero-knowledge — the proof reveals no information about the private witness. If both implementations use ZERO for these factors, the proofs are deterministic (no zero-knowledge property), but at least they should be identical between implementations.
Familiarity with the bellperson library architecture and how create_random_proof_batch is the entry point for batch proof generation.
Knowledge of the cuda-supraseal feature flag and how it conditionally compiles different prover modules.
Understanding that compound_proof.rs in the storage-proofs-core crate calls create_random_proof_batch to generate the final Groth16 proof after circuit synthesis. Output knowledge created:
Confirmed that randomization values are not the source of the discrepancy between native and GPU proof generation.
Established that the investigation must move to other potential differences — perhaps in how the batch proving function is called, how constraints are routed, or how the GPU handles edge cases.
Documented a negative result that prevents future investigators from revisiting this hypothesis.

Mistakes and Correctness

Within the scope of this single message, there are no obvious mistakes. The assistant correctly reads the code, correctly identifies that both implementations use E::Fr::ZERO as the fallback randomization, and correctly concludes they are the same. The logic is sound.

However, there is a subtle assumption worth examining: the assistant assumes that "no randomization is provided" is the same condition in both implementations. In the supraseal prover, the randomization is unwrapped from an Option:

let (r_s, s_s) = randomization.unwrap_or((
    vec![E::Fr::ZERO; num_circuits],
    vec![E::Fr::ZERO; num_circuits],
));

In the native prover, the same pattern likely applies. But the assistant does not verify that the randomization parameter is indeed None in the calling context — it assumes that the default call path provides no randomization. This is a reasonable assumption given that create_random_proof_batch is typically called without explicit randomization in the Filecoin proof pipeline, but it is an assumption nonetheless.

The Significance of a Negative Result

In debugging, negative results — ruling out what is not the cause — are just as important as positive findings. Each eliminated hypothesis narrows the search space. By confirming that randomization is identical between native and supraseal paths, the assistant eliminates an entire class of potential bugs: those related to blinding factor mismatches, zero-knowledge property violations, or randomness source differences.

The message also demonstrates a key principle of complex system debugging: compare the two paths that work and don't work. The FFI path (native prover) works reliably; the cuzk path (supraseal prover) fails intermittently. By systematically comparing their implementations, the assistant hopes to find the exact difference that causes the failure. This message represents one step in that comparison — a step that closes one door and opens another.

Conclusion

Message 1790 is a small but essential piece of a larger investigative puzzle. It captures the assistant in the act of systematic elimination — reading code, drawing conclusions, and pivoting to the next hypothesis. The message is unremarkable in isolation but gains significance when viewed as part of a disciplined debugging methodology. It demonstrates that understanding a complex system often requires ruling out what is not wrong before you can find what is wrong. The randomization hypothesis is dead; the search continues into create_random_proof_batch and beyond, each step bringing the investigation closer to the root cause of the intermittent GPU proof failures.