The Missing Cache: How Budget Exhaustion Silently Broke PCE in a GPU Proving Pipeline

The Message

In a single, deceptively concise assistant message at index 3270 of a long debugging session, the AI agent delivered a devastating diagnosis:

No PCE cached message ever appears. The insert_blocking is stuck in its retry loop because budget.try_acquire(15.8 GiB) keeps returning None. But SRS only uses 32 GiB of 400 GiB, leaving 368 GiB free — way more than enough.

>

Let me check the budget state: [bash] ssh -p 40612 root@[REDACTED] 'grep -E "budget" /data/cuzk-pinned2.log | head -20' ... memory budget initialized total_budget_gib=400 starting pipeline: synthesis task + GPU workers (budget-gated) configured_lookahead=1 max_partitions_in_budget=28 effective_lookahead=28 synth_worker_count=28 num_gpus=2

This message is a turning point. It is the moment the assistant realizes that the Pre-Compiled Circuit Evaluator (PCE) cache—the optimization that makes synthesis 3–5× faster—has been silently dead since deployment. The cache's insert_blocking function is spinning forever, unable to acquire 15.8 GiB of memory budget despite 368 GiB appearing to be free. The contradiction is the key: if 368 GiB is free, why does a 15.8 GiB allocation fail? The answer, which the assistant is about to uncover, reveals a fundamental flaw in how the memory budget system interacts with the pipeline dispatch architecture.

Context: The Battle Against GPU Underutilization

To understand why this message matters, one must appreciate the journey that led to it. The team had been fighting GPU underutilization in the cuzk proving engine—a system that generates zk-SNARK proofs for Filecoin storage proofs. The GPU was spending most of its time idle while CPU-bound synthesis churned through partitions using the slow enforce() path, each partition taking 40–50 seconds and consuming enormous memory.

The pinned memory pool (PinnedPool) was supposed to fix this by eliminating costly host-to-device (H2D) memory transfers. By pre-allocating pinned (page-locked) buffers that CUDA can DMA directly from, the team expected H2D transfer time to drop from seconds to milliseconds. The first deployment (pinned2) showed that pinned buffers were being allocated and used—logs confirmed pinned prover created and is_pinned=true—yet the NTT kernel timings remained abysmal: 2,838 ms to 14,601 ms instead of the expected ~250 ms.

Something was still wrong. The user's observation that "PCE for snap not being cached" ([msg 3263]) was the breadcrumb that led the assistant down the right path. PCE is a technique that pre-evaluates the circuit constraints once, producing a compact representation that can be reused across all partitions of the same circuit type. Without it, every partition runs the full enforce() path, which is both CPU-intensive and memory-hungry. The assistant had assumed PCE caching was working—after all, the logs showed "background PCE extraction starting" and "extraction complete" messages. But extraction without caching is useless.

The Reasoning: Connecting the Dots

The assistant's reasoning in the messages leading up to [msg 3270] shows a methodical diagnostic process. In [msg 3266], the assistant discovered the smoking gun: "Every single partition is using 'standard synthesis path (synthesize_with_hint)' — no partition ever uses the PCE path! Even though PCE extraction completes successfully (4 times!), subsequent jobs keep using the standard path."

This was the first breakthrough. The assistant then traced the code path and found insert_blocking at line 448–491 of pipeline.rs:

loop {
    if let Some(reservation) = self.budget.try_acquire(size) {
        // ... insert PCE
        return;
    }
    std::thread::sleep(Duration::from_secs(1));
}

The loop is an infinite retry: if try_acquire returns None, it sleeps one second and tries again, forever. The assistant initially reasoned that the budget must be saturated by concurrent synthesis partitions. But a quick calculation showed 400 GiB total budget, minus 32 GiB for SRS, minus ~36 GiB for working memory and ~14 GiB for pinned buffers, should leave plenty of headroom.

This is where the assistant's reasoning in [msg 3269] becomes crucial. The assistant considered whether the partition budget reservation mechanism was the culprit: "When jobs dispatch their partitions, each one reserves memory at dispatch time, not just when synthesis runs. If each partition reserves around 14 GiB as a slot, then 80 partitions would need 1120 GiB total, which far exceeds the 400 GiB budget." But then noted that slot_size=0 in the logs, which seemed to contradict this theory.

The assistant then speculated about multiple PCE extraction threads competing for budget simultaneously. But the definitive answer came in [msg 3270] itself: the assistant ran the budget grep and saw the initialization values, but the critical realization was still forming.

The Hidden Assumption

The message reveals a key assumption that turned out to be incorrect: the assistant assumed that because the total budget was 400 GiB and only 32 GiB was accounted for by SRS, the remaining 368 GiB should be available for PCE caching. This assumption overlooked the fact that the budget system tracks reservations made by in-flight partitions, not just current allocations. The 5 jobs that had already been dispatched had reserved budget for their 80 partitions, consuming approximately 362 GiB and leaving only ~5 GiB free. The PCE's 15.8 GiB request was denied not because memory was actually full, but because the budget accounting system had already committed those bytes to partitions that might not even be actively synthesizing yet.

This is a classic resource accounting pitfall: reserving resources optimistically for work that hasn't started yet can starve higher-value work that needs to complete first. The PCE cache, which would benefit all future partitions, was being blocked by reservations for partitions that hadn't even begun synthesis.

Input and Output Knowledge

Input knowledge required to understand this message includes: familiarity with the cuzk proving engine architecture (synthesis pipeline, GPU workers, budget system), understanding of PCE (Pre-Compiled Circuit Evaluator) as a circuit optimization technique, knowledge of the insert_blocking retry loop pattern, and awareness of the pinned memory pool deployment context. The reader also needs to understand that the budget system uses try_acquire as a non-blocking check that returns None when insufficient budget remains, and that the insert_blocking function's infinite loop is a design choice that assumes budget will eventually become available.

Output knowledge created by this message is the confirmed diagnosis that PCE caching is completely broken due to budget exhaustion. This is not a transient issue or a race condition—it is a systemic failure where the dispatch pipeline over-commits budget to in-flight partitions, permanently starving the PCE cache insertion. The message establishes that no amount of waiting will help because the budget is fully reserved by partitions that may take minutes to complete, and the PCE insertion will never succeed unless budget is explicitly freed or the dispatch pattern is changed.

The Thinking Process

The assistant's reasoning in this message is notable for its concision. The statement "No PCE cached message ever appears" is a definitive negative finding—the assistant searched the entire log and found zero occurrences of the success message. This is followed by a precise diagnosis of why: the insert_blocking retry loop is stuck because try_acquire keeps returning None. The assistant then presents the apparent contradiction (368 GiB free but 15.8 GiB denied) and immediately moves to verify the budget state with a targeted grep command.

The thinking reveals a pattern that recurs throughout the session: the assistant forms a hypothesis, identifies a specific log message or code path that would confirm or refute it, and executes a targeted query. The brevity of the message belies the depth of analysis that preceded it—the assistant had already read the insert_blocking source code, traced the path selection logic, confirmed that all partitions use the standard path, and calculated the budget arithmetic. The message is the culmination of that analysis, not its beginning.

The Resolution

The diagnosis in [msg 3270] directly led to the solution: a GPU queue depth throttle (max_gpu_queue_depth = 8) that pauses synthesis dispatch when too many partitions are queued waiting for GPU. By limiting how many partitions could be in-flight at once, the throttle freed budget for PCE caching. The subsequent deployment (pinned3) confirmed that PCE was finally cached (15 GiB used from budget), and the pipeline began using the fast path.

But the story didn't end there. The throttle introduced a new problem: a dispatch burst pattern where all ~20 syntheses fired at once when the GPU queue dropped below the threshold, causing a thundering herd of cudaHostAlloc calls that stalled the GPU. This was eventually solved with a semaphore-based reactive dispatch mechanism (pinned4) that ensures exactly one synthesis is dispatched per GPU completion, creating natural 1:1 modulation. The final result: ntt_kernels 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.

Significance

Message [msg 3270] is a masterclass in diagnostic reasoning. It demonstrates how a single negative finding—the absence of a log message—can overturn an entire set of assumptions about system behavior. The assistant resisted the temptation to blame the pinned memory pool or the GPU drivers, and instead traced the problem to its root: a budget accounting design that prioritized speculative dispatch over high-value caching. The fix was not about making the hardware faster or the code more efficient in isolation; it was about aligning the resource allocation policy with the actual dependencies of the pipeline.

The message also illustrates the importance of understanding the difference between available and unreserved resources in a concurrent system. The 368 GiB that appeared free was already committed to future work, and the budget system had no mechanism to prioritize the PCE cache over partition reservations. This is a lesson that extends far beyond GPU proving: any resource management system that allows lower-value work to reserve resources ahead of higher-value work will suffer from priority inversion, and the symptoms will look exactly like resource exhaustion even when plenty of capacity exists.