The Waterfall That Revealed the Bottleneck: Diagnostic Analysis in the cuzk SNARK Proving Engine

Introduction

In the high-stakes world of Filecoin storage proving, every second of proof generation time translates directly into operational cost. The cuzk project—a custom SNARK proving engine built to replace the standard Filecoin supraseal pipeline—had already achieved remarkable gains through eight optimization phases, reducing per-proof generation time from over 60 seconds to around 38 seconds. But further progress had stalled. A bold Phase 10 redesign, which attempted to split a single GPU mutex into two locks to allow overlapping GPU work, had failed catastrophically due to fundamental CUDA device-global synchronization conflicts and VRAM constraints. The assistant had reverted to the proven Phase 9 single-lock architecture and was now engaged in a systematic diagnostic effort to understand why throughput had plateaued.

Message 2682 represents a pivotal moment in this investigation. It is not a message of implementation or design—it is a message of discovery. In it, the assistant reviews preliminary benchmark data, identifies anomalies, recognizes data contamination, and builds a custom waterfall visualization tool on the fly to isolate the true bottleneck. This message captures the transition from surface-level observation to deep causal analysis, and it lays the groundwork for Phase 11's three-intervention strategy to address DDR5 memory bandwidth contention.

Context: The Wreckage of Phase 10

To understand message 2682, one must understand what preceded it. Phase 10 had been an ambitious attempt to improve GPU utilization by splitting the single GPU mutex into two locks: one for GPU kernel execution and one for PCIe transfer staging. The theory was that while one worker held the GPU for compute, another worker could be pre-staging data for the next partition. This "two-lock overlap" design promised to hide PCIe transfer latency and increase overall throughput.

The reality was brutal. The 16 GB VRAM on the target GPU could not simultaneously accommodate pre-staged buffers from multiple workers. Worse, CUDA memory management APIs like cudaDeviceSynchronize and cudaMemPoolTrimTo operate at device-global scope, meaning they synchronize all streams on the device, not just the calling worker's stream. This defeated the entire purpose of splitting the lock—the device-global synchronization meant that one worker's memory operations would stall the other worker's kernels anyway. The Phase 10 code was reverted, and the assistant returned to Phase 9's single-lock design.

But the assistant did not simply give up. Instead, it recognized that the path forward required deeper understanding. It launched a comprehensive benchmark sweep across concurrency levels (c=5 through c=20) using the Phase 9 codebase, gathering both throughput metrics and TIMELINE instrumentation data. Message 2681 had just computed GPU utilization statistics from this sweep, revealing 84.9% GPU utilization at low concurrency and 90.8% at high concurrency, with throughput plateauing around 38 seconds per proof. Message 2682 is the direct response to those numbers—the assistant's attempt to make sense of what it was seeing.

The Message: Three Observations and a Pivot

The message opens with the assistant reviewing the data from the previous analysis and identifying three anomalies:

  1. Job #2 (729e118f) only has 9 partitions instead of 10 — one partition was likely lost or failed to complete. This is a data integrity concern: if the TIMELINE instrumentation is missing events, the utilization calculations could be undercounting GPU work.
  2. GPU idle gaps of 7–10 seconds between runs — these correspond to synthesis lead time for the first proof in each benchmark batch. When a new batch starts, the first proof must complete its CPU-side synthesis before any GPU work can begin, creating a visible gap in the GPU timeline.
  3. Average GPU per partition degrades from 5.0s to 6.0s as concurrency increases — this is the critical finding. As more proofs are in flight simultaneously, each individual GPU partition takes longer to complete. Since the GPU kernel code itself is unchanged, the degradation must come from contention elsewhere in the system. The assistant immediately identifies the root cause: DDR5 memory bandwidth contention. This third observation is the key insight that will drive all subsequent optimization work. The GPU kernels are not compute-bound—they are memory-bandwidth-bound. When multiple CPU threads are simultaneously performing synthesis (which involves heavy memory allocation and vector manipulation) and the GPU is performing its MSM (multi-scalar multiplication) and NTT (number-theoretic transform) kernels, they compete for the same DDR5 memory channels. This competition inflates kernel execution times because the GPU must wait longer for data to arrive from host memory. However, the assistant immediately recognizes a problem with its own analysis: "the per-run throughput numbers here don't match the bench output because these runs include cross-benchmark boundary contamination." The TIMELINE data spans the entire 33-minute benchmark sweep, and the simple run-splitting algorithm used in message 2681 may have included events from adjacent runs at the boundaries. To get clean data, the assistant decides to focus exclusively on the last run (c=20 j=15) and build a more precise waterfall visualization.

Building the Waterfall Visualization

The assistant writes a Python script that parses the TIMELINE CSV file, filters events from the fourth benchmark run (offset >= 1,296,000 ms), and constructs a detailed GPU waterfall timeline. The script performs several analytical steps:

First, it groups events by job ID and builds an ordered list of jobs to understand the sequencing of proofs through the system. Second, it filters for GPU_START and GPU_END events and parses the worker ID, partition number, and GPU kernel duration from the detail string. Third, it normalizes timestamps to the start of the fourth run for readability. Fourth, it computes inter-partition gaps per worker by tracking the time between each worker's GPU_END and its next GPU_START.

The output shows the first few GPU events of run 4:

   Time      Event      Job  W Part  GPU_ms
--------------------------------------------------
   0.0s  GPU_START fa8d8c54  0    3        
   0.2s  GPU_START fa8d8c54  1    8        
   4.4s    GPU_END fa8d8c54  0    3    4151
   4.8s  GPU_START fa8d8c54  0    1        
   7.3s    GPU_END fa8d8c54  1    8    5694
   7.3s  GPU_START fa8d8c54  1    6        
   9.6s    GPU_END fa8d8c54  0    1    4836
   9.6s  GPU_START fa8d8c54  0    7   ...

This waterfall reveals the dual-worker GPU interlock in action. At time 0.0s, worker 0 starts partition 3 on the GPU. At 0.2s, worker 1 starts partition 8—the two workers are running GPU kernels concurrently (the GPU can execute multiple streams). Worker 0 finishes at 4.4s (4,151 ms of GPU time) and immediately starts partition 1 at 4.8s—a gap of only 400ms, which is the time needed to release the lock, signal the next worker, and have that worker acquire the lock and begin its prestage operations. Worker 1 finishes at 7.3s (5,694 ms) and starts partition 6 at the same instant.

The inter-partition gaps per worker are computed next. The script tracks each worker's GPU_END to GPU_START transitions and reports average and maximum gaps, flagging any gap over 1 second with a <<< marker. This data will reveal whether the lock handoff is introducing significant idle time or whether the GPU is staying well-utilized.

The Thinking Process: From Data to Diagnosis

What makes message 2682 remarkable is not the script itself but the thinking process it reveals. The assistant is not merely executing a pre-planned analysis—it is discovering the shape of the problem in real time.

The initial three observations show the assistant applying pattern recognition to raw numbers. The 9-partition job is flagged immediately—an experienced engineer knows that 10 partitions are expected per proof, so 9 indicates either a dropped event or a partial failure. The 7-10s GPU idle gaps are immediately attributed to synthesis lead time, showing a mental model of the pipeline's critical path. And the degradation from 5.0s to 6.0s average GPU per partition is immediately diagnosed as DDR5 bandwidth contention—a diagnosis that will be validated by all subsequent analysis.

The decision to focus on the last run and build a waterfall visualization shows the assistant's commitment to clean data. Rather than working with contaminated aggregate numbers, it isolates the cleanest dataset and builds a custom tool to extract the signal. This is a hallmark of rigorous engineering: when the data doesn't make sense, don't force-fit a narrative—clean the data first.

The waterfall output itself is revealing. The two workers are clearly overlapping their GPU work—worker 0 starts partition 3 while worker 1 starts partition 8 just 200ms later. But the GPU times are asymmetric: partition 3 takes 4,151ms while partition 8 takes 5,694ms. This variation could be due to partition-level differences (some partitions have more work than others) or could reflect memory contention that varies over time as CPU threads perform synthesis for other proofs.

The assistant is building the case for Phase 11's diagnosis: the bottleneck is not GPU compute throughput but rather the host memory system's ability to feed data to the GPU. When multiple CPU threads are simultaneously allocating memory, performing synthesis computations, and managing deallocation (especially the async_dealloc path that unmaps memory with munmap), they generate TLB (Translation Lookaside Buffer) shootdown storms that stall all memory access, including the GPU's PCIe transfers. The GPU kernels themselves spend more time waiting for data than computing.

Knowledge Required and Created

To understand message 2682, the reader needs knowledge of the cuzk pipeline architecture: that proofs are divided into 10 partitions, each processed by a GPU worker; that two GPU workers share a single GPU via a mutex-based interlock; that TIMELINE events are emitted by the Rust engine at key pipeline stages; and that the benchmark sweep tested concurrency levels from 5 to 20 proofs in flight.

The reader also needs to understand the concept of DDR5 memory bandwidth contention in the context of heterogeneous CPU/GPU workloads. When CPU threads performing synthesis (which involves heavy use of rayon parallel iterators over large vectors) compete with GPU DMA transfers for the same memory channels, both sides slow down. The GPU's MSM kernels, which are memory-bandwidth-bound rather than compute-bound, are particularly sensitive to this contention.

The message creates new knowledge in several forms. First, it confirms that GPU per-partition time degrades under load, ruling out GPU compute capacity as the bottleneck. Second, it establishes that the dual-worker interlock is functioning correctly—workers overlap their GPU time and hand off the lock with minimal gaps. Third, it identifies the need for per-worker gap analysis to quantify lock handoff efficiency. Fourth, it creates a reusable waterfall analysis script that can be applied to any future benchmark run.

Perhaps most importantly, the message establishes the diagnostic methodology that will underpin Phase 11. The three observations directly motivate the three interventions: bounding async_dealloc to a single thread (to eliminate TLB shootdown storms), reducing the groth16_pool thread count (to limit memory footprint and L3 cache competition), and adding a lightweight semaphore interlock (to briefly pause synthesis workers during the b_g2_msm window when all 192 groth16_pool threads run Pippenger simultaneously with 192 rayon synthesis threads).

Assumptions and Potential Pitfalls

The assistant makes several assumptions in this message. It assumes that the TIMELINE events are accurate and complete—the 9-partition job suggests this may not always be true. It assumes that the benchmark runs are cleanly separable by looking for gaps >30 seconds, which works for this dataset but may not generalize. It assumes that the degradation from 5.0s to 6.0s is caused by DDR5 bandwidth contention rather than, say, GPU thermal throttling or PCIe link degradation under sustained load.

The most significant assumption is that the waterfall analysis of the last run is representative of all runs. If the system's behavior changes over time (e.g., due to thermal effects, memory fragmentation, or OS scheduling changes), the fourth run may not reflect the same dynamics as the first. The assistant partially addresses this by noting that the per-run numbers are contaminated and choosing the cleanest dataset, but the assumption of stationarity remains.

There is also a subtle assumption in the gap analysis: the script computes gaps as the time between a worker's GPU_END and its next GPU_START. But if a worker's next GPU_START is for a different job (because the worker was assigned to a new proof while waiting), the gap includes both the lock handoff time and any idle time between jobs. The script does not distinguish between these cases.

The Broader Significance

Message 2682 sits at a critical juncture in the cuzk optimization journey. The project had just suffered a major setback with Phase 10's failure. The natural temptation would be to declare victory at Phase 9's 38s/proof throughput and move on. Instead, the assistant chose to dig deeper, running systematic benchmarks and building custom analysis tools to understand why throughput had plateaued.

This message demonstrates the value of instrumentation-driven performance analysis. The TIMELINE system, which had been added in earlier phases as a lightweight CSV-based instrumentation, proved its worth here. Without it, the assistant would have only aggregate throughput numbers and could only guess at the bottleneck. With it, the assistant could construct a precise timeline of GPU activity, measure inter-partition gaps, and correlate degradation with concurrency level.

The waterfall visualization also reveals something encouraging: the dual-worker interlock is working. Workers are overlapping their GPU time, and the lock handoff gaps are small (hundreds of milliseconds, not seconds). This means the GPU is well-utilized at the macro level—the problem is that each individual GPU kernel takes longer under load, not that the GPU is sitting idle between kernels.

This distinction is crucial for Phase 11's design. If the GPU were idle, the solution would be to reduce lock contention or add more workers. But since the GPU is busy but slow, the solution must address the root cause of the slowness: memory bandwidth contention. The three interventions proposed in Phase 11—single-threaded deallocation, reduced thread pool size, and synthesis worker throttling—all target the same underlying problem from different angles.

Conclusion

Message 2682 is a masterclass in diagnostic engineering. Faced with confusing data after a failed optimization attempt, the assistant does not guess or speculate. It cleans the data, builds a custom visualization tool, and lets the numbers tell the story. The three observations—the missing partition, the synthesis lead-time gaps, and the concurrency-dependent GPU degradation—each point to a different aspect of the system's behavior, but together they converge on a single diagnosis: DDR5 memory bandwidth contention is the bottleneck.

This diagnosis will drive the next phase of optimization. Phase 11's three interventions are not random shots in the dark; they are targeted responses to specific mechanisms identified in this waterfall analysis. The single-threaded deallocation addresses TLB shootdowns. The reduced thread pool addresses L3 cache competition. The synthesis throttle addresses the brief but intense memory-phase overlap between b_g2_msm and synthesis.

In the broader narrative of the cuzk project, message 2682 represents the moment when the team stopped optimizing for GPU compute throughput and started optimizing for memory system efficiency. It is the pivot from "make the GPU go faster" to "stop slowing the GPU down." And it is a reminder that in complex systems, the most important optimization is often not adding more capability but removing the hidden conflicts that prevent existing capability from being fully utilized.