From 50 Seconds to 9: How PCE Disk Persistence, Daemon Integration, and a Slotted Pipeline Reshaped a 200 GiB Proving Engine

Introduction

In the high-stakes world of Filecoin proof generation, where a single Groth16 proof for a 32 GiB sector consumes over 200 GiB of peak memory and involves processing 722 million non-zero constraint entries across 130 million circuit rows, optimization is not a luxury — it is a necessity. This article examines a concentrated burst of engineering work that transformed the SUPRASEAL_C2 proving pipeline across three dimensions simultaneously: PCE disk persistence using a custom raw binary format that achieved a 5.4× load speedup, daemon integration that eliminated the first-proof penalty through automatic preloading and background extraction, and a Phase 6 slotted pipeline design that promises 1.7× better latency with 2.5× less memory.

What makes this work remarkable is not just the magnitude of the improvements — 9.2 seconds to load 25.7 GiB instead of 49.9, 41-second single-proof latency instead of 69.5, 54 GiB working set instead of 136 — but the engineering methodology behind them. Every decision was driven by empirical measurement. Every bottleneck was diagnosed quantitatively before being addressed. And every architectural choice was documented with enough precision that future engineers can understand not just what was built, but why.

The Pre-Compiled Constraint Evaluator: A Brief Refresher

Before diving into the advances of this chunk, it is worth understanding what the PCE is and why it matters. The Pre-Compiled Constraint Evaluator is a Phase 5 optimization in the cuzk proving engine that pre-computes the R1CS constraint structure of a Groth16 circuit. Instead of re-executing the full circuit synthesis — which calls enforce() for every constraint, building linear combinations dynamically — the PCE approach records the sparse matrix structure once and reuses it for every subsequent proof.

The PCE data structure is massive: approximately 25.7 GiB in memory, consisting of three CSR (Compressed Sparse Row) matrices (A, B, C) with 722 million non-zero entries, plus density bitmaps and dimension metadata. Each scalar is a 32-byte BLS12-381 field element, each column index is a 4-byte u32, and the row pointers for 130 million rows add another 1.6 GiB. This structure is deterministic — identical across all proofs for a given circuit — making it an ideal candidate for caching.

The problem was that extracting the PCE from a C1 circuit took 47 seconds. Every time the daemon restarted, the first proof paid this penalty. The solution was disk persistence: save the PCE after extraction, load it on startup, and never extract again. But as the team would discover, the naive approach to serialization introduced its own bottleneck.

The 50-Second Problem: When Bincode Betrays You

The initial implementation of PCE disk persistence used bincode, a popular Rust serialization framework. The code was clean, the types derived Serialize and Deserialize, and the implementation compiled without issue. The save and load functions were written, the --save-pce flag was wired into the benchmark tool, and everything appeared ready for production.

Then came the benchmark ([msg 1636]). The results were sobering:

| Operation | Time | Effective Bandwidth | |-----------|------|-------------------| | Save (to tmpfs) | 16.8s | 1.6 GB/s | | Save (to NVMe) | 30.6s | 0.9 GB/s | | Load (from tmpfs) | 49.9s | 0.6 GB/s |

The load time of 49.9 seconds was slower than the original PCE extraction (47 seconds). A cached artifact that takes longer to load than to regenerate is not a cache — it is a liability. The entire premise of disk persistence — that loading a pre-computed PCE would be faster than re-extracting it — was violated.

The assistant's diagnosis in [msg 1637] was immediate and precise: "The bottleneck is bincode's deserialize, not I/O." The asymmetry between save and load speeds was the tell. Save ran at 1.6 GB/s on tmpfs, close to the sequential write speed. Load ran at only 0.6 GB/s, despite reading from the same fast filesystem. If the bottleneck were I/O, save and load would have comparable throughput. The fact that load was 2.7× slower than save pointed squarely at CPU-bound deserialization overhead.

The mechanism was clear: bincode deserializes each element individually. For a Vec<Scalar> containing 722 million elements, this means 722 million calls through the serde deserialization machinery, each one parsing a 32-byte scalar from the binary stream, validating it, and constructing a Rust object. At 722 million iterations, even nanosecond-scale overhead accumulates to tens of seconds.

The Raw Binary Breakthrough: A Python One-Liner That Changed Everything

In [msg 1638], the assistant performed a back-of-the-envelope calculation that would reshape the entire persistence strategy. Using a Python one-liner, it computed the theoretical minimum load time for a raw binary format:

Designing the Raw Binary Format

With the diagnostic complete and the feasibility confirmed, the assistant designed the new format in [msg 1640]. The format is deceptively simple:

  1. 32-byte header: magic bytes for file identification, version number, circuit dimensions (num_inputs, num_aux, num_constraints, total_nnz), and reserved bytes. This header enables quick validation before committing to load 25+ GiB of data.
  2. Per CSR matrix (A, B, C): Each matrix contains three vectors — row_ptrs (offsets into the column array for each row), cols (column indices of non-zero entries), and vals (the scalar values at those positions). Each vector is stored as a u64 length prefix followed by raw bytes.
  3. Density bitmap: A compact bitmap indicating which rows are dense vs. sparse, stored as raw bytes.
  4. All vectors as u64 length + raw bytes: A uniform encoding scheme where every vector is prefixed by its length in bytes (not element count), enabling bulk read/write without element-by-element iteration. The key insight is that the format is self-describing enough to be parseable but simple enough to be fast. The length prefixes allow the loader to pre-allocate the exact capacity needed before reading, avoiding reallocation overhead. The raw byte dumps bypass serde entirely, reducing the load path to essentially: read header → validate → read length → allocate → read bytes → cast. The implementation also included atomic writes via a .tmp file rename pattern, preventing corruption from partial writes. The save function writes to a temporary file, then atomically renames it to the final path. This is a standard systems technique that ensures the file on disk is always either the complete old version or the complete new version — never a partially-written fragment.

Validating the Raw Format: 5.4× Faster

The benchmark results in [msg 1648] validated the design:

| Operation | Time | Effective Bandwidth | |-----------|------|-------------------| | Save (to tmpfs) | 7.7s | 3.6 GB/s | | Save (to NVMe) | 22.3s | 1.2 GB/s | | Load (from tmpfs) | 9.2s | 3.0 GB/s |

The load time dropped from 49.9 seconds to 9.2 seconds — a 5.4× improvement. The assistant noted: "The load from tmpfs at 9.2s is now bottlenecked by memory allocation (25.7 GiB of Vec allocations + read)." This is a healthy bottleneck — it means the format is I/O-bound on NVMe and memory-bound on tmpfs, not CPU-bound on deserialization logic.

The save time also improved dramatically, from 16.8s to 7.7s on tmpfs, because the raw format eliminated bincode's per-element serialization overhead in both directions.

Wiring It All Together: Daemon Integration

With disk persistence working, the next challenge was integrating it into the daemon so that PCE preloading happened automatically. The assistant implemented three behaviors across a series of edits ([msg 1603] through [msg 1612]):

Preload at startup: A preload_pce_from_disk() function is called during Engine::start(), right after SRS preloading and before GPU detection. This ensures the PCE is available in memory before any proof request arrives. The load path reads the raw binary file from a known directory, validates the header, and populates the global OnceLock cache.

Save after extraction: The extract_and_cache_pce() function now writes to disk automatically after extracting a PCE from a C1 circuit. This means that the first time a circuit is encountered, the extraction cost is paid once, and subsequent daemon restarts benefit from the cached file.

Background auto-extraction: After the first "old-path" synthesis (which triggers PCE extraction), the daemon kicks off background PCE extraction so that future proof requests can use the pre-compiled form. This is triggered in the engine's process_batch method, ensuring that the first proof doesn't block on extraction while subsequent proofs benefit from it.

The integration required careful attention to Rust's ownership semantics. The OnceLock pattern used for the global PCE cache meant that save_to_disk had to operate on a reference obtained after the lock was set — a subtle ownership dance. The daemon integration had to be placed after SRS preloading (which is blocking I/O) but before GPU detection, ensuring the PCE was available before any proof request arrived.

The result: the first-proof penalty was eliminated entirely. On daemon startup, PCE loads in ~13-15 seconds from NVMe (estimated) or ~9 seconds from tmpfs. The first proof request uses the preloaded PCE, paying only the WitnessCS and MatVec costs (~35 seconds total) rather than the full 47-second extraction plus synthesis.

The Phase 6 Slotted Pipeline: A New Architecture

While PCE persistence addressed the startup cost, the team also faced a fundamental architectural problem: the batch pipeline's memory usage. The existing approach synthesized all 10 partitions of a PoRep proof in parallel, consuming ~136 GiB of peak memory, then sent the entire batch to the GPU for proving. This was wasteful in two ways: memory was tied up holding all synthesized partitions simultaneously, and the GPU sat idle during synthesis.

The slotted pipeline concept emerged from a user question in [msg 1559]: could partitions be pipelined more finely? The assistant investigated systematically, launching subagent tasks to understand the GPU proving interface, gathering timing data, and verifying that GPU fixed overhead was negligible.

The critical discovery came in [msg 1561]: the GPU per-circuit cost was approximately 3.4 seconds with near-zero fixed overhead. This single data point unlocked the entire slotted pipeline concept. If the GPU could start proving individual partitions as soon as they were synthesized, rather than waiting for all 10, then synthesis and GPU work could overlap at partition granularity.

The assistant produced a detailed design document, c2-optimization-proposal-6.md, analyzing the trade-offs. The key findings:

| Metric | Batch Pipeline | Slotted (slot_size=2) | Improvement | |--------|---------------|----------------------|-------------| | Single-proof latency | 69.5s | 41s | 1.7× | | Peak working set | 136 GiB | 54 GiB | 2.5× | | GPU utilization (steady state) | ~96% | ~96% | Same | | Memory per concurrent proof | 136 GiB | 54 GiB | 2.5× |

The sweet spot was slot_size=2: two partitions in flight at once. With this configuration, the synthesis thread stays ahead of the GPU thread (synthesis of 2 partitions takes ~7s, GPU proving takes ~6.8s), the GPU never stalls, and memory stays bounded at 54 GiB instead of 136 GiB.

The design also analyzed multi-proof steady-state behavior. With multiple proofs queued, GPU utilization stays at ~96% while memory stays bounded — a critical property for production deployments where throughput matters more than single-proof latency.

From Design to Implementation: The Slotted Pipeline Code

With the design document complete and committed at 6b0121fa ([msg 1652]), the assistant turned to implementation. The plan in [msg 1654] enumerated five components:

  1. A prove_porep_c2_slotted() function in pipeline.rs that orchestrates the internal synth→GPU pipeline
  2. Reuse of synthesize_porep_c2_partition() for per-slot synthesis
  3. Reuse of gpu_prove() for per-slot GPU proving (already works with any circuit count)
  4. A ProofAssembler to collect partition proofs in order
  5. A bench subcommand to test it The key architectural decision was to use std::thread::scope with a bounded channel between a synthesis thread and a GPU thread. This is a self-contained pipeline inside one function call — no external state, no long-lived threads, no complex lifecycle management. The bounded channel naturally limits memory: the synthesis thread can only get a few partitions ahead of the GPU thread before the channel fills up and blocks. But the implementation immediately revealed a hidden inefficiency. In [msg 1655], while examining the synthesize_porep_c2_partition function signature, the assistant discovered that it deserializes the C1 JSON from scratch on every call — a 51 MB parse with base64 decoding. For slot_size=1, that's 10 redundant deserializations of the same data. This is a classic example of how context changes performance characteristics. In the batch pipeline, the function was called once per partition in parallel, and each call naturally had its own JSON. The redundant deserialization was hidden because it was amortized across parallel threads. But in the slotted pipeline, where partitions are synthesized sequentially, the redundancy becomes a serial bottleneck. The assistant's proposed solution was to refactor: separate the parsing phase from the synthesis phase, deserialize the C1 JSON once, and pass the parsed data to a synthesis-only inner function. This refactoring was identified as a necessary prerequisite before the slotted pipeline could achieve its full performance potential.

The Commit That Tied It Together

All of this work was captured in a single git commit at 6b0121fa ([msg 1652]). The commit message reads:

feat(cuzk): PCE disk persistence + auto-extraction + Phase 6 design doc

PCE disk persistence (raw binary format):
- New cuzk-pce::disk module with save_to_disk/load_from_disk
- Raw binary format (v2): 32-byte header + bulk byte dumps of CSR vectors
- 5.4x faster than bincode: 9.2s load vs 49.9s (from tmpfs, 25.7 GiB)
- Atomic writes (tmp + rename) to prevent corruption
- Header with magic/version/dimensions for quick validation

Daemon integration:
- preload_pce_from_disk() called at engine startup (loads all PCE files)
- extract_and_cache_pce() now saves to disk after extraction
- Background PCE auto-extraction triggered after first old-path synthesis
- get_pce() made public for engine-level cache checking

Phase 6 design document (c2-optimization-proposal-6.md):
- Slotted partition pipeline: overlap synth/GPU at partition granularity
- slot_size=2 sweet spot: 41s latency (vs 69.5s batch), 54 GiB RAM (vs 136 GiB)
- Steady-state throughput unchanged (35.5s/proof, synthesis-bound)
- Multi-sector and multi-GPU extension paths documented

The commit touched 8 files with 1252 insertions and 5 deletions, creating two new files: the disk.rs module and the Phase 6 design document. It represents a milestone where three parallel threads of work — persistence, integration, and architecture — converged into a coherent whole.

The Engineering Methodology: What Made This Work

Looking across the entire chunk, several patterns in the engineering methodology stand out:

Measurement-driven optimization. Every decision was based on empirical data. The bincode bottleneck was discovered through benchmarking, not speculation. The raw format's performance was validated with actual load times. The slotted pipeline's projections were grounded in measured GPU per-circuit costs.

Iterative refinement. The implementation went through multiple iterations: first bincode (v1), then raw binary (v2). Each iteration was tested, measured, and either accepted or rejected. The assistant did not attempt to design the perfect format upfront; it started with the simplest working approach and optimized when measurements revealed a bottleneck.

Systems-level thinking. The daemon integration ensured that PCE persistence was not a separate manual step but a transparent cache. The Phase 6 design considered the interaction between CPU synthesis and GPU proving, identifying the pipeline architecture itself as the next optimization target.

Intellectual honesty. The assistant documented limitations honestly: steady-state throughput is unchanged because the bottleneck remains CPU synthesis, not GPU proving. The NVMe load time was labeled as an estimate, not a measured result. The redundant deserialization problem in synthesize_porep_c2_partition was acknowledged and scheduled for refactoring.

Structured concurrency. The choice of std::thread::scope with a bounded channel for the slotted pipeline reflects a deep understanding of Rust's ownership model. Scoped threads guarantee that all spawned threads join before the scope exits, eliminating the need for manual join handling or Arc-based reference counting. The bounded channel naturally limits memory pressure.

Lessons for Systems Engineering

The work in this chunk offers several lessons that generalize beyond the specific context of Filecoin proof generation:

Serialization frameworks optimized for developer convenience are often catastrophically slow for bulk numeric data. Bincode, serde_json, Protocol Buffers — all impose per-element overhead that becomes dominant at scale. When dealing with millions of homogeneous fixed-size elements, a raw binary format with bulk byte reads is almost always the right choice.

A cached artifact that is slower to load than to regenerate is not a cache. The 49.9-second bincode load time was a fundamental failure of the caching premise. The benchmark that revealed this was not a setback but a critical discovery that prevented shipping a net regression.

Per-element overhead scales with element count, not data size. This is the key insight that motivated the raw binary format. Bincode's overhead is proportional to 722 million elements, not 25.7 GiB of data. A format that treats the data as bulk bytes pays overhead proportional to the number of vectors (a handful), not the number of elements.

Pipeline architecture matters as much as stage optimization. The slotted pipeline achieves 1.7× latency improvement and 2.5× memory reduction without changing the speed of any individual stage. It simply rearranges the order of operations to overlap synthesis and GPU work. This is a reminder that architectural changes often yield larger gains than micro-optimizations.

Context changes performance characteristics. The synthesize_porep_c2_partition function was perfectly reasonable in the batch pipeline but became a bottleneck in the slotted pipeline. The same code, called in a different pattern, revealed hidden inefficiencies. This is why profiling under realistic workloads is essential.

Conclusion

This chunk of work represents a remarkable convergence of three optimization threads. PCE disk persistence transformed a 50-second liability into a 9-second asset through the insight that raw byte dumps outperform structured serialization by 5.4× for bulk cryptographic data. Daemon integration wove this capability into the startup and proof-processing flow, eliminating the first-proof penalty entirely. And the Phase 6 slotted pipeline design laid out a path to 1.7× better latency with 2.5× less memory, grounded in the discovery that GPU per-circuit cost is ~3.4 seconds with near-zero fixed overhead.

What makes this work exemplary is not just the magnitude of the improvements but the methodology behind them. Every assumption was tested. Every bottleneck was measured. Every design decision was documented with enough precision that future engineers can understand the reasoning. The commit at 6b0121fa is not just a checkpoint in the git history — it is a snapshot of a proving engine that has been transformed from a collection of experimental optimizations into a coherent, designed system with persistent state, automatic cache management, and a clear architectural roadmap.

The slotted pipeline implementation still faces challenges — the redundant C1 JSON deserialization must be refactored, the ProofAssembler must be built, and the bench subcommand must validate the projected numbers. But the foundation is solid. The PCE loads in 9 seconds. The daemon preloads it automatically. And the architecture is ready for the next phase of optimization.## References

[1] "The Architecture of Knowledge: How a Single Status Message Became the Blueprint for a 200 GiB Proving Pipeline" — Article analyzing message 1527, the comprehensive status report that preceded the work in this chunk.

[2] "The Art of the Green Light: How a Two-Sentence 'Continue' Unlocked a Multi-Gigabyte Optimization Pipeline" — Article analyzing the user's delegation signal that authorized the assistant to proceed with the action plan.

[56] "A Status Update That Speaks Volumes: The Phase 6 Slotted Pipeline Milestone" — Article analyzing the todo-list update marking the design document as complete.

[110] "The Moment of Truth: Validating PCE Disk Persistence at 25.7 GiB" — Article analyzing the first end-to-end benchmark of PCE save-and-load.

[112] "The 50-Second Bottleneck: How a Python One-Liner Revealed the Path to 5.4× Faster PCE Loading" — Article analyzing the diagnostic calculation that motivated the raw binary format.

[114] "The 10× Speedup That Almost Wasn't: Diagnosing and Fixing Bincode's 50-Second Deserialization Bottleneck" — Article analyzing the decision to abandon bincode for a raw binary format.

[126] "The Commit That Tied It Together: PCE Persistence, Daemon Integration, and the Phase 6 Slotted Pipeline" — Article analyzing the commit at 6b0121fa.

[128] "The Architecture of a Slotted Pipeline: Planning the Phase 6 Partition Prover" — Article analyzing the implementation plan for the slotted pipeline.

[129] "The Moment of Recognition: Catching Redundant Deserialization in the Slotted Pipeline" — Article analyzing the discovery that synthesize_porep_c2_partition redundantly deserializes C1 JSON.