Building pinned3: Deploying the GPU Queue Depth Throttle

In the high-stakes world of GPU-accelerated zero-knowledge proving, memory pressure can silently sabotage performance. Message 3295 captures a pivotal moment in a debugging odyssey: the build step for "pinned3," the third iteration of a fix designed to eliminate GPU underutilization in the cuzk proving engine. The message itself is deceptively simple — a Docker build command and its compilation output — but it represents the culmination of a deep diagnostic chain that traced a performance collapse from a saturated memory budget all the way to a missing throttle on synthesis dispatch.

The Message

[assistant] [bash] DOCKER_BUILDKIT=1 docker build --no-cache -f Dockerfile.cuzk-rebuild -t cuzk-rebuild:pinned3 . 2>&1 | tail -15
#15 101.8    Compiling cuzk-server v0.1.0 (/build/extern/cuzk/cuzk-server)
#15 103.2 warning: `cuzk-core` (lib) generated 4 warnings (run `cargo fix --lib -p cuzk-core` to apply 1 suggestion)
#15 103.2    Compiling cuzk-daemon v0.1.0 (/build/extern/cuzk/cuzk-daemon)
#15 108.7     Finished `release` profile [optimized] target(s) in 1m 47s
#15 DONE 109.0s

#16 [stage-1 1/1] COPY --from=builder /build/extern/cuzk/target/release/cuzk-daemon /cuzk
#16 DONE 0.1s

#17 exporting to image
#17 exporting l...

At first glance, this is routine: a Rust project compiling successfully in a Docker multi-stage build, producing a release binary in under two minutes. But the context transforms this ordinary build log into a milestone. The tag pinned3 tells a story of iteration — pinned1, pinned2, and now pinned3 — each representing a hypothesis tested, a bug fixed, and a new understanding of the system's behavior under load.

Why This Message Was Written

The message exists because a cascade of failures had to be addressed. The team had implemented a pinned memory pool (PinnedPool) to eliminate costly host-to-device (H2D) memory transfers that were causing GPU underutilization. The idea was elegant: pre-allocate pinned (page-locked) memory on the CPU side so that GPU transfers could happen without the usual staging through pageable memory. But when deployed, the fix didn't work. Logs showed is_pinned=false on every completion — the pool was silently falling back to heap allocations.

The root cause was a budget double-counting bug. The PinnedAbcBuffers::checkout() method called budget.try_acquire() for memory that was already accounted for in per-partition budget reservations. With five jobs dispatching sixteen partitions each, roughly 362 GiB of the 400 GiB total budget was consumed by partition reservations alone. The pinned pool's additional try_acquire calls were denied, and every synthesis completed without pinned memory. The fix (pinned2) removed budget from the pool entirely, and logs confirmed pinned prover created and is_pinned=true.

But a second, more insidious problem emerged. The Pre-Compiled Constraint Evaluator (PCE) cache — a critical optimization that replaces slow constraint evaluation with a fast lookup — was stuck in an infinite retry loop. The insert_blocking method called budget.try_acquire(15.8 GiB) for each new PCE entry, but with only 5 GiB of budget remaining after partition reservations, every call returned None. The PCE was never cached, forcing all synthesis through the slow enforce() path, which in turn consumed even more memory and CPU bandwidth. The system was caught in a vicious cycle: memory pressure prevented PCE caching, and the absence of PCE caching worsened memory pressure.

The user's observation, captured in [msg 3272], was the key insight: a screenshot of the vast-manager UI showed dozens of partitions in "purple" state — post-synthesis, waiting for GPU. The system was synthesizing far more partitions than the GPU could consume, flooding memory with synthesized data that just sat in a queue. The user proposed a mechanism to "stop adding new synth jobs once more than N (configurable, let's say 8) partitions are post synth waiting for a gpu."

This suggestion was the turning point. The assistant recognized in [msg 3273] that throttling synthesis dispatch based on GPU queue depth would "kill multiple birds with one stone": it would free budget for PCE caching, reduce memory pressure, and reduce memory bandwidth contention. The GPU had only two workers (one per device), so a queue depth of eight was more than sufficient to keep it fed while preventing the memory blowout that was starving the PCE cache.

How the Decision Was Made

The implementation decisions were driven by the architecture of the existing pipeline. The assistant traced through the codebase in messages 3274–3282, identifying the critical control points. The synthesis dispatcher — a single-threaded loop that pops work items from a priority queue, acquires budget, and sends them to worker threads — was the natural insertion point. By checking gpu_work_queue.len() before acquiring budget, the throttle would prevent new synthesis work from consuming memory when the GPU queue was already deep. This ordering was deliberate: by blocking before budget acquisition, the throttle ensured that budget remained available for PCE caching rather than being locked up by partitions that would just sit in the GPU queue.

The assistant added three changes in [msg 3283] and [msg 3286]: a len() method to the PriorityWorkQueue struct, a max_gpu_queue_depth field to the PipelineConfig with a default of 8, and the throttle logic in the dispatcher loop. The choice of a polling loop with tokio::time::sleep(Duration::from_millis(100)) was pragmatic — it avoided the complexity of an async wait mechanism while being responsive enough to react to GPU consumption. The config default of 8 was chosen because with two GPU workers each processing one partition at a time, a queue of eight provides enough buffer to keep the GPU fed without allowing memory to balloon.

Assumptions and Their Validity

Several assumptions underpin this message. The first is that the Docker build environment has all necessary dependencies — CUDA toolkits, Rust cross-compilation targets, system libraries — to produce a working binary. The successful compilation confirms this assumption held. The second is that the release profile with optimizations is appropriate for deployment; the 1m 47s build time is acceptable for a development iteration. The third, more subtly, is that the throttle will actually solve the PCE caching problem rather than just shift the bottleneck elsewhere. This assumption would be tested in the next deployment cycle.

The assistant also assumed that a polling-based throttle with a 100ms sleep is responsive enough. In practice, this proved partially correct: the throttle did allow PCE to be cached (385 GiB budget used, 15 GiB for PCE), but the initial batch of 80 partitions was already dispatched before PCE was available, so the pipeline had to drain those slow unpinned partitions first. The user later observed that when 20+ syntheses ran simultaneously, GPU activity dropped to near zero — likely due to contention on cudaHostAlloc calls from the pinned pool. This led to a further refinement in chunk 1 of segment 24, where a semaphore-based reactive dispatch replaced the polling approach.

Input Knowledge Required

To understand this message, one must grasp the layered architecture of the cuzk proving engine. The pipeline has three stages: synthesis (constraint evaluation on CPU), GPU proving (the compute-intensive work on GPU), and finalization. Between synthesis and GPU sits the gpu_work_queue, a priority queue implemented as a BTreeMap behind a Mutex. The synthesis dispatcher is a single-threaded loop that pulls from a synth_work_queue, acquires budget from a MemoryBudget system, and sends work to parallel synthesis workers. The PCE cache is a budget-integrated hash map that stores pre-compiled circuits to avoid re-evaluating constraints. The pinned memory pool (PinnedPool) pre-allocates page-locked host memory to eliminate H2D transfer overhead.

One must also understand the budget system: a MemoryBudget with a configurable total (400 GiB) that tracks allocations and reservations. Every partition reservation, SRS load, PCE cache entry, and pinned pool allocation draws from this budget. When it's exhausted, try_acquire returns None, and the caller must retry or fall back.

Output Knowledge Created

This message produces a Docker image tagged cuzk-rebuild:pinned3 containing the compiled cuzk-daemon binary. The binary includes: the pinned memory pool with corrected budget integration (no double-counting), the GPU queue depth throttle (blocking synthesis dispatch when gpu_work_queue.len() >= max_gpu_queue_depth), and the configurable max_gpu_queue_depth parameter (default 8). The build output also reveals that cuzk-core generated four warnings, one of which the compiler suggests can be auto-fixed — a minor concern that doesn't affect correctness.

The downstream impact of this build is significant. Once deployed with a config update setting max_gpu_queue_depth = 8, the throttle successfully allowed PCE to be cached. The budget logs showed 385 GiB used, with 15 GiB allocated to PCE — the first time the PCE cache had been populated since the pinned pool changes were introduced. However, as the chunk summary notes, the initial batch of 80 partitions had already been dispatched before PCE was available, so the pipeline had to drain those slow unpinned partitions first. The throttle was a necessary but not sufficient fix; it would later be refined into a semaphore-based reactive dispatch in pinned4.

The Thinking Process

The assistant's reasoning, visible in the surrounding messages, follows a clear diagnostic arc. It begins with observation: logs show no "PCE cached" messages, and insert_blocking is stuck in a retry loop. It formulates a hypothesis: budget exhaustion from too many concurrent partitions is preventing PCE allocation. It tests this hypothesis by examining budget logs, confirming that 80 partitions consume 362 GiB, leaving only 5 GiB. It then accepts the user's suggestion of GPU queue depth throttling and traces through the code to find the right insertion point. The choice to throttle before budget acquisition is a subtle but critical insight — it ensures that budget freed by the throttle is available for PCE rather than being immediately consumed by a new partition.

The message itself is the execution of that reasoning: compile the fix, package it, deploy it. The build succeeds, and the next messages show the extraction and deployment steps. The story doesn't end here — pinned3's polling throttle would later be replaced by a semaphore-based reactive dispatch in pinned4, which produced dramatic results: H2D transfer time dropped from 1,300–12,000 ms to 0 ms, and per-partition GPU time reduced to ~935 ms of pure compute. But pinned3 was the step that proved the concept: throttling synthesis dispatch based on GPU queue depth was the key to breaking the memory pressure cycle that had been starving the PCE cache.