Architecting a Continuous Proving Pipeline: Three Proposals to Slash C2 Memory and Boost Throughput for Filecoin PoRep

Introduction

In the world of decentralized storage, Filecoin's proof-of-replication (PoRep) mechanism is the backbone of trust—a cryptographic guarantee that storage providers are honestly keeping the data they claim to store. But this guarantee comes at a staggering computational cost. A single 32 GiB sector's C2 proof (the second and most expensive phase of PoRep) consumes nearly 200 GiB of RAM, demands a high-end GPU for minutes of compute, and forces operators to provision machines with 256 GiB or more of memory—machines whose rental cost is dominated by RAM, not by the GPU or CPU doing the actual work.

For a Proofshare marketplace operator running a heterogeneous fleet of proving machines, this memory wall is the primary constraint on profitability. RAM is expensive, limits the pool of viable cloud instances, and leaves the GPU idle for most of the proof's duration. The problem isn't just that proofs are expensive—it's that the architecture is fundamentally batch-oriented, loading everything into memory at once and discarding it after a single use.

Message 10 in this coding session represents a turning point. After a deep investigation into the SUPRASEAL_C2 Groth16 proof generation pipeline—tracing the call chain from Curio's Go task scheduler through Rust FFI into C++/CUDA kernels, analyzing circuit value distributions, and characterizing computational hotpaths—the assistant synthesizes everything into a coherent, three-part optimization plan. The proposals are not isolated patches but a composable architectural vision: break the all-at-once batch model, keep the proving infrastructure alive across proofs, and exploit freed memory headroom to batch across sectors.

This article examines that message in depth: the reasoning behind each proposal, the assumptions and trade-offs, the thinking process visible in the assistant's analysis, and the knowledge that flows into and out of this pivotal message.

Context: The Investigation That Led Here

To understand why message 10 is so significant, we must first understand what came before it. The conversation began with a seemingly straightforward question: how does the SUPRASEAL_C2 pipeline work, and where does all the memory go? The assistant embarked on a multi-threaded investigation, using a tool that allows it to spawn concurrent research tasks. These tasks explored three parallel tracks:

  1. Curio orchestration ([msg 7]): How does Curio's task system schedule C2 proofs? The investigation revealed that Curio spawns a child process per proof via exec.CommandContext, meaning every proof loads the ~48 GiB SRS from scratch, deserializes it, computes one proof, and exits. The SRS cache in groth16_srs.cuh is effectively useless because the process dies after each proof.
  2. Circuit value distribution ([msg 7]): What fraction of witness values are boolean? The analysis of the 32 GiB PoRep circuit showed that approximately 99% of aux_assignment values are 0 or 1—they're SHA-256 internal bits, Boolean wire states, and comparison results. Storing each as a 32-byte field element represents a 256× waste.
  3. Bellperson synthesis internals ([msg 7]): How does the ProvingAssignment synthesis process work? The investigation traced the entire data flow from circuit synthesis through NTT, H computation, and MSM, revealing that all 10 partition circuits are synthesized in parallel, creating a ~160 GiB memory spike. A critical verification task ([msg 8]) corrected a major misunderstanding: the aux_assignment is ~130 million elements (~4 GiB per circuit), not the ~6 million initially assumed. This changed the memory picture dramatically and informed everything that followed. The user then challenged the assistant to "think bigger" ([msg 6]), asking for 2-3 high-impact improvements that consider the whole picture of a constant proving pipeline, optimized for maximum proof throughput per dollar of system cost. The assistant responded by asking about the target deployment model ([msg 9]), and the user answered: "Proofshare marketplace"—a heterogeneous fleet where all dimensions matter, with RAM as the top constraint. Message 10 is the assistant's response to that answer. It is the synthesis of everything learned across the investigation, distilled into a concrete, actionable plan.

The Problem in Numbers

The message opens with a stark memory breakdown that crystallizes the problem:

| Component | Memory | Why | |---|---|---| | 10 × ProvingAssignment (a,b,c) | ~120 GiB | 10 partitions × 3 vectors × ~130M × 32B | | 10 × aux_assignment | ~40 GiB | 10 partitions × ~130M × 32B | | SRS in CUDA pinned memory | ~30-48 GiB | All proving key points deserialized into RAM | | C++ split vectors / temp | ~5-10 GiB | Bitmaps, tail MSM scalars, GPU staging | | Total host peak | ~195-218 GiB | |

This breakdown is itself a piece of output knowledge—a clear, quantified model of where memory goes. Before this analysis, the memory consumption was a vague "~200 GiB" without attribution. Now each component is identified, sized, and explained.

The implications are immediate and profound:

The Key Insight: Partition Circuits Are Structurally Identical

The assistant identifies a crucial insight that underpins Proposal 1: the 10 partition circuits are identical in structure (same R1CS, same constraint system) with different witness values. The current code synthesizes all 10 in parallel, creating a ~160 GiB memory spike, then hands them all to the GPU in one batch. But there is no mathematical reason they must all exist simultaneously.

This insight is non-obvious to someone unfamiliar with Groth16 proof generation. Each partition is a separate proof fragment—they could, in principle, be independent. The decision to batch them was a design choice in supraseal-c2, likely made for GPU efficiency (amortizing kernel launches and memory transfers). But the memory cost of that choice is enormous.

The assistant also notes that ~99% of aux_assignment values are boolean (0 or 1)—they're SHA-256 internal bits. Storing each as a 32-byte field element is a 256× waste. This observation hints at further optimization potential (dense encoding of boolean vectors) but is not pursued in the current proposals, which focus on architectural changes rather than data representation.

Proposal 1: Sequential Partition Processing with Streaming

The first and most foundational proposal breaks the all-at-once batch model. Instead of synthesizing all 10 partitions, batching them, and sending them to the GPU together, the assistant proposes processing them one at a time in a pipeline:

Partition 0: Synthesize → NTT+H on GPU → extract aux → free a,b,c
Partition 1: Synthesize → NTT+H on GPU → extract aux → free a,b,c  
...
Partition 9: Synthesize → NTT+H on GPU → extract aux → free a,b,c
Then: Run all MSMs (L, A, B) using stored aux assignments
Then: Assemble 10 proofs

The memory savings are dramatic. Instead of 10 partitions' a,b,c vectors coexisting (~120 GiB), only one partition's vectors exist at a time (~12 GiB). The aux_assignments must still be accumulated (they're needed for the MSM phase), but at ~4 GiB each, 10 of them cost ~40 GiB—much less than 120 GiB.

The peak memory during synthesis drops from ~160 GiB to ~20 GiB (one partition's a,b,c + aux). Adding the SRS (~48 GiB) brings the total to ~68 GiB—fitting comfortably in a 128 GiB machine.

Performance Analysis

The assistant is careful to analyze the performance impact, not just the memory savings. The key insight is that the GPU can start working on partition 0's NTT+H as soon as it finishes synthesis, while the CPU moves on to synthesizing partition 1. This creates a pipeline overlap:

CPU: [Synth P0] [Synth P1] [Synth P2] ... [Synth P9] [prep_msm + B_G2 MSM]
GPU:            [NTT+H P0] [NTT+H P1] ... [NTT+H P9] [batch_add] [tail MSMs]

If synthesis takes ~30s per partition and NTT+H takes ~20s, the GPU is never idle after the first partition. Today, the GPU is idle for the entire ~60-120s synthesis phase. This is a strict improvement in GPU utilization.

The only serialization is that the batch-add/tail-MSM phase cannot start until all partitions are synthesized (because it needs the cross-circuit bitmap union for the split-MSM optimization). But that's the same as today—the MSM phase has always waited for all partitions.

What Would Need to Change

The assistant identifies three areas of code change:

  1. bellperson (prover/supraseal.rs): Replace synthesize_circuits_batch() (parallel over all circuits) with a sequential loop that synthesizes one circuit, sends a,b,c to GPU for NTT+H, gets back the H MSM result, then drops a,b,c before proceeding.
  2. supraseal-c2 (groth16_cuda.cu): Split generate_groth16_proofs_c() into three phases—ntt_h_phase(), prep_and_msm_phase(), and assemble_proofs()—to allow interleaving with synthesis.
  3. Split-MSM adaptation: The bitmap merging (OR across circuits) for the split decision now happens during the MSM phase after all aux_assignments are collected, rather than during synthesis. These are non-trivial changes, but they are well-scoped. The assistant has done the work of identifying the specific files and functions that need modification.

Proposal 2: Persistent SRS + Long-Lived Prover Process

The second proposal addresses a different inefficiency: the per-proof SRS loading overhead. Today, each C2 proof spawns a child process (curio ffi) that loads the ~48 GiB SRS from disk, deserializes it into pinned memory, computes one proof, and exits. For a proofshare node processing 50+ proofs per day, this means loading 48 GiB hundreds of times—a waste of both time and energy.

The proposal is to replace the child-process-per-proof model with a long-lived proving daemon that:

The SRS Cache Irony

The assistant notes a particularly galling detail: the SRS cache is already implemented in groth16_srs.cuh (an LRU cache with max 3 entries), but it's useless today because the process dies after each proof. A persistent process would actually benefit from this cache. This is a classic example of an optimization that was designed for a different deployment model and is wasted in the current architecture.

Memory and Performance Impact

The SRS (~48 GiB pinned) is loaded once and shared across all proofs. Combined with Proposal 1, peak per-proof memory drops from ~200 GiB to:

Architectural Implications

This proposal is more than a memory optimization—it's a fundamental change to Curio's FFI subprocess model. The assistant identifies the specific file (lib/ffiselect/ffiselect.go) where the exec.CommandContext call is made and proposes replacing it with a connection to a persistent daemon. Communication via protobuf-over-unix-socket or shared memory for the witness data.

This is a significant architectural change that affects the reliability model. If the daemon crashes, all in-flight proofs are lost. The assistant doesn't address failure modes in this message, but the implication is that the daemon must be carefully managed—perhaps with a watchdog process or health-check endpoint.

Proposal 3: Cross-Sector Proof Batching

The third proposal exploits the freed memory headroom from Proposals 1 and 2 to batch partitions across sectors. The supraseal generate_groth16_proofs_c() already handles batches of circuits—today it batches the 10 partitions of one sector. But there's nothing stopping it from batching partitions across sectors.

If three sectors are waiting for C2, instead of processing them sequentially, the system could do:

Mega-batch: 30 partitions → GPU (3 sectors × 10 partitions each)

Why This Improves Throughput

The GPU's MSM throughput scales sub-linearly with batch size. More circuits amortize fixed costs (SRS transfer, kernel launch overhead, memory allocation). The split-MSM bitmap union across 30 circuits identifies an even better split—more circuits mean more chance a given position is "significant" in at least one circuit, but the per-circuit savings increase.

The assistant also notes that the B_G2 CPU MSM (currently a bottleneck) processes all circuits' B_G2 in one pass when batched, improving CPU efficiency as well.

Combined with Proposal 1

The real power of Proposal 3 is in combination with Proposal 1's sequential synthesis. The pipeline becomes:

CPU: [Synth S0P0] [Synth S0P1] ... [Synth S0P9] [Synth S1P0] ... [Synth S2P9]
GPU:              [NTT+H S0P0] ... [NTT+H S0P9] [NTT+H S1P0] ... [NTT+H S2P9]
                                                                    [batch_add + MSM for all 30]

Peak memory with a 3-sector batch + sequential synthesis:

What Would Need to Change

  1. Curio task system: Introduce a C2 batch collector that accumulates multiple sectors' C1 outputs before triggering a single batched C2 computation. Configurable batch size and max wait time.
  2. bellperson: Accept circuits from multiple sectors in a single create_proof_batch() call. The circuits are identical in structure (same R1CS), just different witnesses—this is already supported.
  3. supraseal-c2: The generate_groth16_proofs_c() function already handles variable num_circuits. The max_num_circuits = 10 constant in groth16_srs.cuh only affects thread reservation for SRS loading, easily bumped. The changes are relatively contained, especially compared to Proposal 1's restructuring of the synthesis-GPU interaction.

Composability and Synergy

A key strength of the proposals is their composability. Each builds on the previous:

Assumptions and Potential Issues

The assistant's proposals are well-reasoned, but they rest on several assumptions that deserve scrutiny:

Assumption 1: Synthesis time dominates GPU idle time

The pipeline overlap in Proposal 1 assumes that synthesis takes longer than NTT+H per partition (~30s vs ~20s). If synthesis is faster than NTT+H, the GPU would still have idle periods waiting for the next partition. The assistant's numbers come from the earlier investigation, but they should be validated on target hardware.

Assumption 2: The split-MSM optimization adapts gracefully

The assistant asserts that the bitmap merging for the split decision can happen during the MSM phase after all aux_assignments are collected. This is true in principle, but the implementation details matter. The current code might have implicit assumptions about when the bitmap is computed.

Assumption 3: The daemon model is reliable

Proposal 2 introduces a persistent daemon that holds ~48 GiB of SRS in pinned memory. If the daemon crashes, all in-flight proofs are lost, and the SRS must be reloaded. The assistant doesn't address failure modes, error recovery, or the complexity of IPC communication.

Assumption 4: Cross-sector batching doesn't increase latency

Proposal 3 introduces a batch collector that accumulates sectors before triggering C2. This could increase latency for individual proofs if the batch size is large or the wait time is long. The assistant mentions "configurable batch size and max wait time," but doesn't discuss the latency-throughput trade-off.

Assumption 5: The GPU MSM scales sub-linearly with batch size

The assistant claims that GPU MSM throughput scales sub-linearly with batch size, but doesn't provide evidence for this claim. It's plausible (amortizing fixed costs), but the actual scaling factor depends on the specific GPU architecture and MSM implementation.

These assumptions are not flaws in the proposals—they are areas that would need validation during implementation. The assistant has done the intellectual work of identifying the key uncertainties.

The Thinking Process Visible in the Message

One of the most valuable aspects of this message is the thinking process it reveals. The assistant doesn't just present the proposals—it shows how it arrived at them.

From Problem to Insight

The message begins with a quantified problem statement, then immediately pivots to the key insight: "The 10 partition circuits are identical in structure (same R1CS, same constraint system) with different witness values." This insight is the seed from which Proposal 1 grows. The assistant recognized that the current code's parallelism was a design choice, not a mathematical necessity.

Pipeline Thinking

The assistant thinks in terms of pipelines, not individual operations. The pipeline diagram for Proposal 1 shows a sophisticated understanding of how CPU and GPU work can be overlapped. This is not obvious—it requires understanding the dependencies between synthesis, NTT+H, and MSM, and recognizing that the GPU can start working before the CPU finishes.

Composability as a Design Principle

The proposals are designed to be composable. Each one builds on the previous, and the combined effect is greater than the sum of the parts. This is a deliberate design choice, not an accident. The assistant explicitly says "The proposals are composable—each builds on the previous."

Quantitative Thinking

Throughout the message, the assistant quantifies everything: memory in GiB, time in seconds, throughput in proofs per hour, cost in relative dollars. This quantitative approach makes the proposals concrete and testable. It also reveals the assistant's understanding that in a marketplace context, the bottom line is $/proof.

Awareness of Existing Code

The assistant references specific files and functions throughout the message: prover/supraseal.rs, groth16_cuda.cu, groth16_srs.cuh, lib/ffiselect/ffiselect.go. This shows a deep engagement with the actual codebase, not just abstract reasoning. The observation that the SRS cache is "already implemented but useless today" is a particularly sharp piece of code archaeology.

Input Knowledge Required

To understand this message, the reader needs knowledge spanning multiple domains:

  1. Groth16 proof generation: Understanding of the NTT, MSM, and H computation phases, and how they relate to the a/b/c vectors and aux_assignment.
  2. Filecoin PoRep: Knowledge of the C1 and C2 phases, partition structure, and the role of the SRS (Structured Reference String).
  3. GPU programming: Understanding of CUDA pinned memory, kernel launch overhead, and GPU utilization concepts.
  4. Systems architecture: Familiarity with child process models, IPC mechanisms, and daemon processes.
  5. Curio internals: Knowledge of the task system, FFI interface, and how proofs are orchestrated. The assistant provides enough context that a reader with general knowledge of these domains can follow the reasoning, but deep understanding requires familiarity with the specific codebase.

Output Knowledge Created

This message creates significant output knowledge:

  1. A quantified memory model for C2 proof generation, attributing ~200 GiB to specific components.
  2. Three composable optimization proposals with detailed implementation sketches.
  3. A pipeline model showing how CPU and GPU work can be overlapped.
  4. Throughput projections showing the expected impact of each proposal.
  5. A cost model ($/proof) that ties technical changes to business outcomes.
  6. Identification of specific code changes needed, down to the file and function level.
  7. A summary comparison table that makes the trade-offs immediately visible. This knowledge transforms a vague problem ("RAM too expensive") into a concrete engineering roadmap with measurable targets.

Conclusion

Message 10 is a masterclass in systems-level optimization thinking. It takes a complex, multi-layered problem—reducing memory and improving throughput for Groth16 proof generation—and decomposes it into three composable proposals, each with clear rationale, quantified impact, and specific implementation guidance.

The message's power lies not in any single insight but in the synthesis of multiple insights: the structural identity of partition circuits, the wasted SRS cache, the pipeline overlap opportunity, and the cross-sector batching potential. Each insight alone would be a useful optimization. Together, they form a coherent architectural vision for a continuous, memory-efficient proving pipeline.

For a Proofshare marketplace operator, the bottom line is compelling: a 4× improvement in $/proof, enabling the use of cheaper 128 GiB machines, and a 2.4-3.6× increase in throughput per GPU. These numbers transform the economics of Filecoin proof generation.

But the message's value extends beyond its immediate proposals. It demonstrates a way of thinking about optimization problems: quantify the problem, identify the key insight, design composable solutions, and validate assumptions through quantitative analysis. This approach is applicable far beyond the specific context of Filecoin PoRep.

The assistant's closing question—"Should I elaborate on any of these, or shall we discuss implementation sequencing and which to tackle first?"—invites the next phase of the conversation: moving from plan to execution. The proposals are detailed enough to guide implementation but leave room for discussion of priorities, trade-offs, and practical considerations.

In the broader context of the coding session, this message represents the culmination of the investigative phase and the beginning of the design phase. It is the moment when analysis crystallizes into action—a turning point that transforms understanding into engineering.