The Negative Result: Searching for Large-Sector Constraint Counts in SnapDeals Circuits

Message Summary

In message 88 of this coding session, the assistant executed the following bash command:

grep -rn "num_constraints_expected\|constraint.*32.*GiB\|constraint.*64.*GiB\|large_sector\|test.*apex.*large\|test.*circuit.*large" /home/theuser/.cargo/registry/src/index.crates.io-6f17d22bba15001f/storage-proofs-update-18.1.0/src/ 2>&1 | head -10

The command returned only two lines, both from gadgets.rs:

/home/theuser/.cargo/registry/src/index.crates.io-6f17d22bba15001f/storage-proofs-update-18.1.0/src/gadgets.rs:299:        let num_constraints_expected = [568, 568, 568, 568, 568, 568, 568, 568, 4544, 5680, 5680];
/home/theuser/.cargo/registry/src/index.crates.io-6f17d22bba15001f/storage-proofs-update-18.1.0/src/gadgets.rs:304:            .zip(num_constraints_expected.iter().copied())

These results correspond to a test for small sector sizes (1 KiB through 32 KiB), not the large 32 GiB or 64 GiB sectors the assistant was seeking. The search for "large_sector," "test.circuit.large," and constraint patterns mentioning "32 GiB" or "64 GiB" returned nothing. This was a negative result — the assistant discovered that the expected constraint counts for large-sector SnapDeals (EmptySectorUpdate) circuits are not explicitly enumerated as numeric literals in the source code, at least not in the locations searched.

Why This Message Was Written: The Reasoning and Motivation

This message sits at a critical juncture in a broader investigation into the SUPRASEAL_C2 Groth16 proof generation pipeline for Filecoin's Proof-of-Replication (PoRep) and related proof types. The overarching goal of the session is to understand why the pipeline exhibits a ~200 GiB peak memory footprint and to identify optimization opportunities. The subagent's specific task was to "Explore SnapDeals/PoSt circuit sizes."

The assistant had already established several foundational pieces of knowledge in preceding messages:

  1. Feature flag propagation (messages 62–69): How cuda-supraseal features flow from filecoin-proofs-api through filecoin-proofs down to bellperson and storage-proofs-* packages, determining whether GPU acceleration or the custom Supraseal prover is used.
  2. PoSt circuit parameters (messages 70–81): Winning PoSt uses 66 challenges with 1 sector; Window PoSt uses 10 challenges with a sector count that varies by sector size. These are defined in filecoin-proofs/src/constants.rs.
  3. SnapDeals/Update circuit structure (messages 82–87): The storage-proofs-update package defines partition_count() and challenge_count() functions that scale with sector size, and contains test data showing constraint counts for small sectors (1 KiB through 32 KiB). The assistant's motivation in message 88 was to close the gap between the small-sector test data already found and the large-sector (32 GiB, 64 GiB) constraint counts needed to understand the full memory picture. Without knowing how many constraints the EmptySectorUpdate circuit generates for production sector sizes, it is impossible to estimate the memory required for synthesis, FFT, and MSM operations — the three main memory consumers in Groth16 proof generation. The specific trigger was the previous message (87), where the assistant read a test function test_apex_por_gadget_small_sector_sizes that contained constraint expectations only for sectors up to 32 KiB. The natural next question was: where are the equivalent numbers for 32 GiB and 64 GiB sectors? Message 88 represents the assistant's attempt to answer that question by searching for any test or constant that might contain those larger numbers.## How Decisions Were Made: The Search Strategy The assistant's decision to use this particular grep command reveals a methodical search strategy. The command was constructed with six search patterns, each targeting a different potential location for large-sector constraint data: - num_constraints_expected: This pattern directly matches the variable name used in the small-sector test seen in message 87. If the developers followed the same naming convention for large-sector tests, this would find them. - constraint.*32.*GiB and constraint.*64.*GiB: These patterns attempt to find any literal mention of "32 GiB" or "64 GiB" near the word "constraint." This is a heuristic for finding documentation, comments, or configuration that maps sector sizes to constraint counts. - large_sector: A pattern targeting test function names or variables that might contain large-sector test data. - test.*apex.*large and test.*circuit.*large: Patterns targeting test function names related to the apex PoR (Proof-of-Replication) gadget or circuit tests for large configurations. The choice to search only within storage-proofs-update-18.1.0/src/ was deliberate. The assistant had already established that the EmptySectorUpdate circuit (SnapDeals) lives in this package, and the small-sector test data was found in gadgets.rs within this same directory. The head -10 flag indicates the assistant expected at most a handful of results — either a few test functions or nothing at all. This was a targeted, low-noise search designed to find explicit numeric constraint data quickly. The assistant could have chosen to run the actual test to compute the constraint counts dynamically, or to trace through the circuit synthesis code to derive them algebraically. But those approaches would require significantly more time and computational resources. The grep approach was the fastest path to determine whether the data existed in a readily consumable form.

Assumptions Made by the Assistant

The assistant operated under several assumptions, some of which proved incorrect:

  1. That large-sector constraint counts would be explicitly enumerated as test constants. This assumption was natural given the pattern seen for small sectors (1 KiB–32 KiB). However, the EmptySectorUpdate circuit's constraint count grows rapidly with sector size — for a 32 GiB sector, the number of constraints could be in the hundreds of millions. Hard-coding such large numbers in a test would be impractical, and the test infrastructure likely computes them dynamically or uses a different validation strategy.
  2. That the naming convention would be consistent. The assistant assumed that large-sector tests would use names containing "large_sector" or "large" in some form. In reality, the test infrastructure for large sectors may be structured differently — perhaps using parameterized tests, integration tests in a separate directory, or runtime computation rather than static expected values.
  3. That the data would be in the same file or directory. The small-sector data was in gadgets.rs, so the assistant searched the entire src/ tree. But large-sector constraint data might live in a completely different location — perhaps in benchmark code, integration tests, or even generated at build time.
  4. That the grep patterns would be sufficient. The patterns may have been too narrow. For example, a test function might be named test_apex_por_gadget_all_sector_sizes (not containing "large" specifically) and compute expected values dynamically from sector size parameters. The assistant's patterns would miss such a function.

Mistakes and Incorrect Assumptions

The primary "mistake" here is not an error in execution but in the underlying assumption that explicit numeric constraint counts for large sectors would exist in the source code. The negative result is itself valuable information — it tells the assistant that the data must be obtained through a different method.

A secondary issue is that the grep command, while thorough, may have been too restrictive. The assistant could have broadened the search to include patterns like fn test combined with apex or por to find all test functions in the file, then inspected them manually. Alternatively, searching for 32_GIB or sector_size in test code might have revealed parameterized tests.

However, it is important to note that the assistant did not make a critical mistake — it correctly executed a reasonable search and interpreted the negative result correctly. The message shows the raw output without commentary, but the context of the conversation makes it clear that the assistant recognized this as a dead end and would pivot to other approaches.

Input Knowledge Required

To understand this message, a reader needs knowledge of:

  1. The Filecoin proof system architecture: That there are multiple proof types (PoRep, Winning PoSt, Window PoSt, EmptySectorUpdate/SnapDeals), each with different circuits and constraint counts.
  2. The Groth16 proving pipeline: That proof generation involves circuit synthesis (producing constraints), FFT (for polynomial operations), and MSM (multi-scalar multiplication for the commitment), each consuming significant memory.
  3. The relationship between sector size and circuit complexity: That larger sectors produce more constraints because they require proving more Merkle tree openings, more challenges, and more partition operations.
  4. The codebase structure: That storage-proofs-update contains the SnapDeals/EmptySectorUpdate circuit, and that gadgets.rs contains the circuit gadget implementations and their tests.
  5. The prior conversation context: That the assistant had already found small-sector constraint counts and was now searching for large-sector equivalents.

Output Knowledge Created

The output of this message is a negative result — the knowledge that explicit large-sector constraint counts are not present in the source code in the locations and patterns searched. This is valuable because it:

  1. Saves future investigation time: Anyone searching for these numbers now knows not to look for hard-coded test constants in storage-proofs-update/src/.
  2. Redirects the investigation: The assistant must now pursue alternative strategies — perhaps running the circuit synthesis for a 32 GiB sector configuration, tracing through the partition_count() and challenge_count() functions to compute constraints algebraically, or examining the circuit struct's num_constraints() method if one exists.
  3. Reveals the testing methodology: The fact that large-sector constraint counts aren't hard-coded suggests that the Filecoin team validates large circuits through different means — perhaps integration tests that run against real parameters, or benchmarks that measure rather than assert exact counts.
  4. Documents a boundary: This result establishes that the codebase's explicit constraint documentation stops at 32 KiB sectors, and everything beyond that must be computed or measured dynamically.

The Thinking Process Visible in Reasoning

The assistant's reasoning process, while not explicitly shown in a separate thinking block, is visible in the sequence of commands across messages 58–88. The pattern is one of systematic narrowing:

  1. Broad exploration (messages 58–69): Understanding the feature flag system and how GPU/Supraseal selection works.
  2. Parameter discovery (messages 70–81): Finding the challenge counts and sector counts for PoSt proofs.
  3. Circuit structure analysis (messages 82–87): Examining the EmptySectorUpdate circuit's partition and challenge functions, and finding small-sector test data.
  4. Gap identification (message 88): Recognizing that the small-sector data doesn't cover production sizes, and searching for the missing data. The thinking behind message 88 specifically is: "I have constraint counts for 1 KiB through 32 KiB sectors. Production sectors are 32 GiB and 64 GiB. Where are those numbers? Let me search for any test or constant that mentions large sectors or specific GiB sizes." The negative result then triggers a cognitive pivot. The assistant now knows it must either: - Compute the constraint counts by running the circuit synthesis for a large sector configuration - Derive them from the partition_count() and challenge_count() functions combined with knowledge of the circuit's internal structure - Find a different source of truth (benchmarks, documentation, or the circuit's num_constraints() implementation) This pivot is evident in the subsequent messages of the conversation, where the assistant moves from searching for constants to analyzing the circuit synthesis code itself.

Conclusion

Message 88 is a textbook example of a productive negative result in systems investigation. The assistant asked a clear question — "what are the constraint counts for large-sector SnapDeals circuits?" — and received a clear answer: "not found in the expected locations." This is not a failure; it is a successful elimination of a hypothesis. The assistant now knows that the data must be obtained through computation rather than lookup, and the investigation can proceed with a more accurate understanding of the codebase's documentation boundaries.

The message also illustrates the importance of search strategy in code investigation. The assistant's carefully constructed grep command with multiple fallback patterns maximized the chance of finding the data while minimizing noise. When the result came back empty, the assistant could be confident that the data truly wasn't there, rather than doubting the search quality. This methodological rigor is essential when reverse-engineering complex systems like the Filecoin proof pipeline.