The Full Picture: Why the Standard Pipeline Already Achieves Near-100% GPU Utilization

Introduction

In the course of optimizing the cuzk SNARK proving engine for Filecoin PoRep proofs, a critical moment of synthesis arrived. After weeks of implementing the Pre-Compiled Constraint Evaluator (PCE), designing a slotted partition pipeline, and running extensive end-to-end benchmarks, the assistant arrived at a realization that reframed the entire optimization strategy. Message [msg 1805] captures this moment: the assistant declares "I now have the full picture" and proceeds to articulate why the standard engine pipeline already achieves near-100% GPU utilization, why the partitioned path cannot compete on throughput, and what the real value proposition of the partitioned approach actually is.

This message is not merely a summary of benchmark results—it is a decision point. It represents the moment when the assistant steps back from the implementation details, synthesizes the empirical data, and reorients the optimization effort toward the correct target. Understanding this message requires tracing the reasoning that led to it, examining the architectural assumptions that were challenged, and appreciating the trade-offs between throughput and memory that define the proving pipeline's design space.

The Context: A Journey Through Pipeline Optimization

To understand message [msg 1805], one must first understand the journey that preceded it. The cuzk proving engine is a sophisticated piece of infrastructure designed to generate Groth16 proofs for Filecoin's Proof-of-Replication (PoRep) protocol. These proofs require processing ten partitions of circuit constraints, each involving large-scale multi-scalar multiplication (MSM) and number-theoretic transform (NTT) operations on GPUs. The peak memory footprint of a single proof was approximately 200 GiB, making it a significant operational concern.

The optimization effort had proceeded through multiple phases. Phase 4 focused on synthesis optimizations, achieving a 13.2% end-to-end improvement through techniques like optimizing Boolean::add_to_lc and implementing async deallocation for large vectors. Phase 5 introduced the Pre-Compiled Constraint Evaluator (PCE), which pre-computes constraint evaluations to eliminate redundant work during synthesis. Phase 6 designed a slotted partition pipeline that aimed to overlap synthesis and GPU work within a single proof, reducing peak memory by streaming partitions sequentially rather than processing all ten at once.

The slotted pipeline was implemented in prove_porep_c2_slotted, which used a channel-based architecture to buffer synthesized partitions and feed them to the GPU as slots became available. The promise was twofold: lower peak memory (by not holding all synthesized partitions simultaneously) and potentially higher throughput (by overlapping synthesis of one partition with GPU proving of another). The implementation was validated with in-process benchmarks showing promising memory reduction from ~228 GiB to ~71 GiB.

But the critical question remained: how would this perform in the real deployment scenario—the cuzk proving daemon, which handles multiple concurrent proof requests through a gRPC interface? The assistant had wired the partitioned pipeline into the daemon's process_batch method and was now running end-to-end benchmarks to find out.

The Benchmark Results That Changed Everything

The assistant ran a comprehensive benchmark comparing the standard pipeline path (slot_size=0, which processes all ten partitions as a batch) against the partitioned path (slot_size=1 through slot_size=10, which uses the slotted channel architecture). The results were stark and unambiguous.

The standard path achieved 1.257 proofs per minute with an average of 47.7 seconds per proof. The partitioned path, across all slot sizes, achieved only ~0.84 proofs per minute with an average of ~72 seconds per proof. The standard path was approximately 50% faster.

More importantly, the timing breakdown revealed why. In the standard path, the first proof took 63.3 seconds (cold start, no overlap), but subsequent proofs showed a beautiful pipelining pattern: synthesis of proof N+1 (approximately 37 seconds) overlapped with GPU proving of proof N (approximately 27 seconds). Because synthesis was consistently longer than GPU time, the GPU was never idle after the first proof. The steady-state throughput was approximately 37 seconds per proof—essentially synthesis-bound.

The partitioned path, by contrast, showed no inter-proof overlap whatsoever. Each proof took its full ~72 seconds from start to finish, with the next proof beginning only after the previous one completed. The synthesis task was blocked for the entire duration of each proof, preventing the engine from starting synthesis of the next proof while the current one was on the GPU.

The Architectural Root Cause

The assistant's analysis in message [msg 1805] identifies the root cause with precision: "The partitioned path can't compete on throughput because it blocks the synthesis task for the entire proof duration (~72s)."

The mechanism is worth understanding in detail. The cuzk engine architecture has two main components that run concurrently:

  1. The synthesis task: A single async task that processes proof requests sequentially. For each request, it calls spawn_blocking to run the CPU-heavy synthesis work (constraint evaluation, witness generation, circuit construction).
  2. The GPU worker: A separate async task that receives synthesized proofs via a channel and performs the GPU-heavy proving work (MSM, NTT, Groth16 proof assembly). In the standard path, the synthesis task synthesizes a proof and sends it through a channel to the GPU worker. The synthesis task then immediately starts working on the next proof—it does not wait for the GPU to finish. This is the synthesis_lookahead mechanism: the synthesis task stays one proof ahead of the GPU worker, ensuring the GPU always has work ready. In the partitioned path, prove_porep_c2_slotted is called via spawn_blocking inside the synthesis task. This function does not return until the entire proof—including all GPU work—is complete. The synthesis task is blocked for the full ~72 seconds, unable to start work on the next proof. The inter-proof pipeline is completely broken. This is a classic architectural issue: a synchronous call chain that blocks an async task, preventing the overlapping execution that the engine was designed to provide. The fix would require restructuring the partitioned path so that GPU work is sent through the engine's GPU channel rather than being performed inline, allowing the synthesis task to proceed to the next proof.

The Reframing: Throughput vs. Memory

The most important contribution of message [msg 1805] is the reframing of the optimization's value proposition. The assistant writes:

"However, the real value proposition here is different. Let me reframe the approach: use the partitioned synthesis (parallel per-partition with PCE) but send through the standard GPU channel."

This reframing acknowledges a crucial insight: the partitioned path was designed for throughput improvement, but its actual strength is memory reduction. The in-process benchmarks had shown peak memory dropping from ~228 GiB to ~71 GiB when using the slotted pipeline. The daemon benchmarks confirmed this direction, though the daemon's peak RSS of ~265 GiB for partitioned modes (inflated by having two proofs' worth of data live due to -j 2 concurrency) suggested additional memory management work was needed.

The assistant then proposes a hybrid approach: use the partitioned synthesis (which processes partitions in parallel, keeping peak memory lower during synthesis) but send the synthesized partitions through the standard GPU channel as a single batch GPU call. This would preserve the inter-proof overlap while still achieving some memory benefit from the partitioned synthesis structure.

However, the assistant immediately identifies a complication: "synthesizing all 10 partitions in parallel with PCE already takes ~36s (same as the batch synthesis). The only advantage of partitioned synthesis is lower peak memory during synthesis." This is an important observation—the partitioned synthesis doesn't actually save time; it only saves memory. The PCE has already optimized synthesis to the point where the batch synthesis and the parallel partitioned synthesis take approximately the same amount of wall-clock time.

The Decision Point: Where to Go Next

The message concludes with a practical decision: "Let me actually focus on what matters: the slot_size=0 path is already the best for throughput. The user wants to find where GPU is 100% fed. Let me run that test properly with higher concurrency."

This decision is significant. It represents a shift from trying to improve the partitioned path's throughput to characterizing the standard path's behavior at higher concurrency levels. The assistant recognizes that the standard path is already near-optimal for throughput, and the remaining question is whether it can maintain that throughput under load—whether the GPU stays fully utilized when multiple proofs are queued.

The assistant updates the todo list to reflect this new focus, planning to run benchmarks with higher concurrency to measure GPU utilization at steady state. This is the natural next step: validate that the standard pipeline's inter-proof overlap mechanism scales correctly when the daemon is under load.

Assumptions and Knowledge

This message rests on several key assumptions and bodies of knowledge:

Input knowledge required to understand this message:

The Thinking Process

The reasoning visible in this message reveals a sophisticated analytical process. The assistant begins by stating "I now have the full picture," indicating that the preceding benchmark data has been fully digested. The key insight is presented as a bullet list, suggesting a structured synthesis of multiple observations.

The reasoning proceeds through several layers:

  1. Observation: Synthesis (37s) > GPU (27s), and with lookahead, the GPU channel always has a pre-synthesized proof ready.
  2. Inference: Steady-state throughput ≈ 37s/proof, GPU never idles.
  3. Comparison: The partitioned path blocks the synthesis task, preventing this overlap.
  4. Proposed solution: Restructure partitioned path to send GPU jobs through the engine channel.
  5. Reality check: But partitioned synthesis takes the same time as batch synthesis, so the only benefit is memory.
  6. Decision: Focus on characterizing the standard path's throughput at higher concurrency. This is a mature engineering thought process: observe, infer, compare, propose, validate against constraints, and decide. The assistant doesn't get attached to the partitioned path—when the data shows it's inferior for throughput, the assistant pivots to the standard path and identifies the real question to answer.

Conclusion

Message [msg 1805] is a pivotal moment in the cuzk optimization journey. It demonstrates the importance of end-to-end testing: the in-process benchmarks showed the partitioned path performing well, but the daemon benchmarks revealed a fundamental architectural limitation that the in-process tests couldn't capture. The message also shows the value of stepping back from implementation details to ask the right questions: not "how do we make the partitioned path faster?" but "what is the actual bottleneck, and which path best addresses it?"

The answer, as the assistant discovered, is that the standard pipeline already achieves near-optimal GPU utilization through its inter-proof overlap mechanism. The partitioned path's value is memory reduction for memory-constrained deployments, not throughput improvement. This reframing allows the team to focus their efforts where they will have the most impact: either optimizing the standard path further (by reducing synthesis time or adding parallel synthesis tasks) or optimizing the partitioned path for memory (by fixing the daemon's memory management to achieve the 71 GiB peak seen in-process).

The message also serves as a reminder that architectural decisions have consequences that may only become visible under realistic deployment conditions. The partitioned path's use of spawn_blocking to run the entire proof pipeline was a design choice that made sense for simplicity but broke the engine's inter-proof overlap mechanism. Fixing this would require a significant refactoring to make the partitioned path's GPU work go through the engine's channel—a change that would need to be weighed against the memory benefits it provides.