Architecting the Future of Filecoin Proof Generation: From 200 GiB Peak Memory to a Continuous, Cost-Effective Proving Pipeline

Introduction

In the world of decentralized storage, Filecoin's Proof-of-Replication (PoRep) consensus mechanism stands as a critical pillar — and a formidable computational challenge. The SUPRASEAL_C2 Groth16 proof generation pipeline, responsible for producing the cryptographic proofs that underpin Filecoin's storage verification, consumes approximately 200 GiB of peak memory per proof and requires roughly 360 seconds to complete on specialized hardware. These numbers translate directly into operational costs: at cloud rental rates, each proof costs around $0.083, making large-scale proving economically prohibitive.

This article synthesizes a comprehensive deep-dive investigation into the SUPRASEAL_C2 pipeline, tracing its architecture from Curio's Go task layer through Rust FFI into C++/CUDA kernels, accounting for every gigabyte of memory, and ultimately producing a suite of five composable optimization proposals that project a path to 35-45 second proofs on 96 GiB machines at approximately $0.004 per proof — a roughly 10× throughput improvement and 20× cost reduction.

The Architecture: Mapping the Full Call Chain

The investigation began with a fundamental question: where does the memory go? The answer required tracing the entire proof generation pipeline from its highest-level orchestration to its lowest-level kernel executions.

From Curio to CUDA

At the top of the stack sits Curio, Filecoin's Go-based storage mining software. Curio's task layer dispatches proof generation jobs through a Rust FFI boundary into the bellperson library, which implements the Groth16 proving protocol. Bellperson, in turn, delegates computation to supraseal-c2, a C++/CUDA library that performs the heavy lifting: number-theoretic transforms (NTTs), multi-scalar multiplications (MSMs), and field arithmetic on GPU hardware.

The call chain is not a simple linear path. The C2 prover operates on 10 partitions in parallel, each partition processing a portion of the circuit's constraints. This parallel design is the single largest driver of peak memory consumption.

Memory Accounting: Where the 200 GiB Goes

The investigation produced a detailed memory accounting that pinpoints exactly where each gigabyte is consumed:

Nine Structural Bottlenecks

The background reference document produced during the investigation catalogued nine distinct structural bottlenecks in the pipeline:

  1. SRS loading overhead: Each proof generation reloads the ~48 GiB SRS from disk, consuming approximately 60 seconds of wall-clock time.
  2. Partition parallelism memory blowup: The 10× parallel partitions multiply memory without proportional throughput gain.
  3. Repeated constraint synthesis: Every proof re-executes the full circuit synthesis, including 780 million heap allocations from enforce() calls.
  4. SHA-256 constraint dominance: The SHA-256 labeling circuit accounts for 88% of all constraints but lacks a witness-generator fast path.
  5. Boolean witness materialization: 99% of witness variables are boolean (0 or 1), yet they are stored and processed as full field elements.
  6. H-to-D transfer overhead: Host-to-device transfers of circuit data consume significant PCIe bandwidth.
  7. GPU kernel launch latency: The NTT and MSM kernel launch patterns incur overhead from frequent small-kernel invocations.
  8. CPU synthesis hotpath inefficiency: The enforce() loop on the CPU side performs redundant work that could be pre-computed.
  9. Per-proof process lifecycle: Each proof spawns a new process, losing all cached state and requiring re-initialization of GPU contexts.

The Optimization Proposals: Three Pillars of Transformation

Armed with this architectural understanding, the investigation produced three composable optimization proposals, each targeting a different dimension of the memory-throughput trade-off.

Proposal 1: Sequential Partition Synthesis

The most direct attack on peak memory is to break the 10-partitions-in-parallel model. Sequential Partition Synthesis streams partitions one at a time through the GPU, reusing the same memory buffer for each partition's circuit. Instead of allocating 10 × 16 GiB = 160 GiB for circuits, the pipeline allocates a single 16 GiB buffer plus minimal overhead for the current partition.

The impact is dramatic: peak memory drops from ~200 GiB to approximately 64-103 GiB, depending on whether the SRS can be partially evicted. The throughput impact is minimal because the GPU's NTT and MSM kernels are already the bottleneck — the CPU-side circuit synthesis for each partition completes faster than the GPU can process the previous partition's data.

Proposal 2: Persistent Prover Daemon

The SRS loading overhead of ~60 seconds per proof is pure waste — the SRS is identical for every proof of the same circuit. Persistent Prover Daemon eliminates this by keeping the proving process alive across multiple proof generations. Instead of spawning a new process (and a new GPU context) for each proof, a daemon process holds the SRS in pinned GPU memory and accepts new witness data via shared memory or IPC.

This proposal is architecturally simple but operationally transformative. It converts the per-proof SRS loading cost from O(n) to O(1), saving ~60 seconds per proof. Combined with Sequential Partition Synthesis, the daemon can also maintain a warm GPU context, eliminating context initialization overhead.

Proposal 3: Cross-Sector Batching

The memory freed by Sequential Partition Synthesis creates headroom for a different kind of parallelism: Cross-Sector Batching. Instead of processing one sector's proof at a time, the pipeline batches multiple sectors' circuits into a single GPU invocation. The NTT and MSM operations for multiple proofs can be fused, amortizing kernel launch overhead and improving GPU utilization.

The projections are striking: 2-3× throughput per GPU for the batching alone, and when combined with the other proposals, a 5-6× reduction in cost per proof. The key insight is that GPU compute is typically the bottleneck, not memory bandwidth — so adding more work to each kernel invocation improves utilization without proportionally increasing runtime.

The Centerpiece: Constraint-Shape-Aware Optimizations (Proposal 5)

While the first three proposals address architectural and operational inefficiencies, the fifth proposal targets the fundamental structure of the circuit itself. The Filecoin PoRep circuit has two remarkable properties that are almost entirely unexploited in the current pipeline, as revealed through a detailed source-code investigation that traced the is_witness_generator() mechanism across four Rust crates [7][13]:

  1. SHA-256 constraints dominate: 88% of all constraints come from the SHA-256 labeling circuit.
  2. Boolean witnesses dominate: 99% of all witness variables are boolean (0 or 1). Moreover, the R1CS constraint matrices (A, B, C) are deterministic and identical for every proof — only the witness values change. This means the constraint structure can be pre-computed once and reused across all proofs.

The Pre-Compiled Constraint Evaluator (PCE)

The PCE is the highest-impact single optimization in the entire suite. Its core idea is radical: instead of re-executing the entire circuit synthesis (including 780 million heap allocations from repeated enforce() calls) for every proof, pre-extract the constraint matrices once during parameter generation. At proving time, simply evaluate the pre-compiled constraints against the witness values.

The existing codebase already has a precedent for this pattern. The investigation's source-code verification (documented across messages 0-16 of the conversation) revealed that WitnessCS — a dedicated witness-generation constraint system — already no-ops its enforce() method and provides a fast-path API for direct witness assignment. The Neptune Poseidon hash circuit already exploits this with a poseidon_hash_allocated_witness fast path that bypasses constraint synthesis entirely.

The PCE extends this pattern to the entire circuit. By pre-extracting the CSR (Compressed Sparse Row) representation of the A, B, and C matrices during parameter generation, the prover can replace the per-constraint linear combination evaluation with a pre-computed matrix-vector product. The 780 million heap allocations vanish.

Specialized MatVec and Pre-Computed MSM Topology

Two complementary optimizations amplify the PCE's impact. Specialized MatVec exploits the coefficient distribution: 70% of coefficients are ±1 (requiring no multiplication, just addition or subtraction), and 99% of witnesses are boolean (0 or 1, enabling skip-or-copy operations). A naive matrix-vector multiply would waste cycles on these trivial operations; a specialized implementation can skip them entirely.

Pre-computed split MSM topology addresses the runtime cost of classifying variables into input and auxiliary sets for the Groth16 split MSM. Since the boolean index sets are static across all proofs, the classification can be pre-computed during parameter generation and stored as a fixed partitioning, eliminating the runtime scan entirely.

Micro-Optimization Analysis: The Instruction-Level View

Beyond the architectural proposals, the investigation conducted a micro-optimization analysis of the CPU and GPU hotpaths, examining the pipeline at the instruction level.

CPU Synthesis Hotpaths

The enforce() loop on the CPU side is the dominant hotpath during circuit synthesis. Each invocation evaluates three linear combinations (A, B, C), updates density trackers, and pushes results to evaluation vectors. The analysis identified:

GPU NTT/MSM Characteristics

On the GPU side, the NTT and MSM kernels exhibit:

H-to-D Transfer Patterns

The host-to-device transfer of circuit data is a significant bottleneck. Each partition's constraint data must be transferred from CPU memory to GPU memory before kernel execution. With 10 partitions, this creates a serialization bottleneck at the PCIe bus.

Recomputing a/b/c Vectors On-the-Fly

A particularly intriguing micro-optimization is the feasibility of recomputing the a/b/c vectors on-the-fly rather than materializing them in memory. The a/b/c vectors represent the A·w, B·w, and C·w products — the result of evaluating the constraint matrices against the witness. If these can be recomputed cheaply from the pre-compiled CSR matrices and the witness values, the memory for storing them can be eliminated entirely. The analysis concluded that this is feasible for the boolean-dominated witness, where the recomputation cost is low relative to the memory savings.

The Total Impact Assessment: 10× Throughput, 20× Cost Reduction

The investigation concluded with a comprehensive total impact assessment synthesizing across all five proposals. The combined analysis shows a path from the current baseline to an optimized pipeline:

| Metric | Baseline | Optimized | Improvement | |--------|----------|-----------|-------------| | Peak memory | ~200 GiB | ~96 GiB | 2× reduction | | Per-proof time | ~360 s | ~35-45 s | ~10× faster | | Cost per proof | ~$0.083 | ~$0.004 | ~20× cheaper | | Throughput per GPU | 1 proof/hr | ~10 proofs/hr | ~10× |

The assessment includes a phased 13-week implementation roadmap with clear dependency ordering, wave-based delivery, and marginal return analysis. The Pre-Compiled Constraint Evaluator (Proposal 5A) is identified as the highest-impact single item, with a 1.00× throughput multiplier per engineering-week — meaning every week invested in the PCE yields a proportional throughput improvement across the entire pipeline.

From Optimization to Architecture: A New Paradigm

Perhaps the most significant outcome of this investigation is not the individual proposals but the paradigm shift they represent. The current SUPRASEAL_C2 pipeline treats each proof generation as an isolated, batch-oriented computation: load SRS, synthesize circuit, evaluate constraints, compute MSMs, produce proof, exit. This model is simple but wasteful.

The optimized pipeline envisions a continuous, memory-efficient proving process:

Conclusion

The SUPRASEAL_C2 Groth16 proof generation pipeline, once a monolithic 200 GiB memory consumer requiring specialized hardware, has been systematically decomposed, analyzed, and re-architected. The investigation mapped the full call chain from Curio to CUDA, accounted for every gigabyte of memory, identified nine structural bottlenecks, and produced five composable optimization proposals that together project a 10× throughput improvement and 20× cost reduction.

The journey from a 200 GiB peak memory footprint to a 96 GiB continuous proving pipeline is not merely an optimization exercise — it is a fundamental rethinking of how cryptographic proof generation should be architected for the cloud era. By exploiting the deterministic structure of the circuit, the boolean nature of the witnesses, and the continuous-operation model of modern GPU infrastructure, the proposals chart a path from expensive, batch-oriented proof generation to a cost-effective, streaming paradigm that makes Filecoin storage proving economically viable at scale.## References

The analysis synthesized in this article draws on the following source-code investigations and architectural studies from the conversation:

[1] "The Pivot Point: When a Source Code Search Encounters Access Boundaries" — Documents the critical moment when the assistant recognized that cargo registry source files were inaccessible and pivoted to alternative strategies. [[msg 10]]

[2] "When the Source Code Is Locked: A Study in Adaptive Problem-Solving During a Technical Deep-Dive" — Examines the pivot to GitHub raw URL fetching after all local file access was blocked. [[msg 12]]

[3] "When Tools Fail: Navigating Access Restrictions in a Deep-Dive Code Analysis Session" — Traces the seven-message sequence of blocked attempts to read source files. [[msg 8]]

[4] "The Hunt for Source Code: Navigating Restricted Environments in Systems Analysis" — Analyzes the systematic exploration of the project's file system topology. [[msg 9]]

[5] "When Globs Fail: Navigating the Cargo Registry in a Code Analysis Session" — Documents the failed glob-based search and pivot to direct filesystem exploration. [[msg 2]]

[6] "Discovering the Cargo Registry: A Pivotal Debugging Step in the SUPRASEAL_C2 Investigation" — Chronicles the discovery of the two cargo registry directories and the exact package versions. [[msg 3]]

[7] "Verifying the Witness Generator Mode: A Targeted Source-Code Investigation for the Pre-Compiled Constraint Evaluator" — The user's original five-part query that drove the entire source-code investigation. [[msg 0]]

[8] "When Globs Go Silent: A Debugging Moment in the SUPRASEAL_C2 Pipeline Investigation" — Analyzes the first failed glob attempt and its diagnostic value. [[msg 1]]

[9] "The Pivot: How a Blocked File Access Sparked a Strategic Shift in Source Code Investigation" — Documents the pivot to using the grep tool with different permissions. [[msg 11]]

[10] "Pivoting to Source: The Art of Persistence in Open-Source Code Archaeology" — Examines the final webfetch calls that completed the source-code collection. [[msg 14]]

[11] "Pivoting Under Constraint: How an Agent Adapted Its Source-Gathering Strategy in a Groth16 Deep-Dive" — Analyzes the strategic pivot to GitHub raw URLs after local access was exhausted. [[msg 13]]

[12] "The Final Fetch: How a Single Data-Gathering Message Reveals the Anatomy of Systematic Code Analysis" — Documents the final source-code fetch and the "one more thing" heuristic. [[msg 15]]

[13] "The Witness Generator Verification: A Deep Dive into Filecoin's Groth16 Constraint System Architecture" — The comprehensive technical report that answered all five verification questions with exact code snippets and line numbers. [[msg 16]]

[14] "When the Read Tool Hits a Wall: A Subagent's Pivot Point in Source Code Verification" — Documents the moment when the Read tool was blocked for external directories. [[msg 5]]

[15] "Pivoting Under Constraint: Tool Adaptation in a Deep Code Investigation" — Analyzes the pivot to bash cat after the Read tool failed. [[msg 6]]

[16] "Navigating Tool Boundaries: A Pivot Point in Source Code Analysis" — Documents the failed cp workaround and the growing understanding of sandbox restrictions. [[msg 7]]

[17] "The Scaffolding of Discovery: How a Search Infrastructure Message Reveals the Architecture of Open-Source Code Analysis" — Analyzes the file enumeration step that mapped all source files across the four crates. [[msg 4]]