The Evidence Imperative: From Priority Queues to Precision Instrumentation in the cuzk GPU Utilization Investigation

Introduction

In the life of a complex distributed system, there is no final victory. Every fix reveals a new problem, every optimization uncovers a new bottleneck, and every moment of satisfaction is followed by a fresh puzzle demanding attention. This is the rhythm of systems engineering—a rhythm that the cuzk proving pipeline development session embodies with unusual clarity.

This article examines a pivotal transition in the cuzk CUDA ZK proving daemon development, spanning the moment when a highly successful scheduling fix was confirmed working to the moment when precise timing instrumentation was added to the GPU worker hot path and finalizer. In between lies a masterclass in evidence-driven debugging: the user's insistence on measurement before action, the assistant's graceful pivot from fixing to instrumenting, and the systematic addition of timing probes designed to reveal—with microsecond precision—where the GPU's multi-second idle gaps were actually being spent.

The segment covers two distinct but connected phases. In the first phase, the assistant completed and deployed a priority queue scheduling system that delivered a 24% throughput improvement by ensuring that partitions from older pipelines were processed before partitions from newer ones. In the second phase, the team turned to a persistent performance mystery: despite a large backlog of synthesized partitions waiting for GPU proving, GPU compute utilization hovered around 50% with multi-second idle gaps. The investigation of this mystery—and the methodological pivot from speculation to measurement—is the central narrative of this article.

Part I: The Priority Queue Victory

Before the GPU utilization investigation began, the session opened with a significant achievement. The synthesis ordering problem—a fundamental scheduling flaw in which partitions from multiple jobs were dispatched as independent tokio::spawn tasks racing on a Notify-based semaphore—had been replaced with a priority queue system that enforced strict (job_seq, partition_idx) ordering for both synthesis and GPU dispatch.

The problem had been subtle but devastating. The original implementation used a thundering-herd wakeup pattern: when budget became available, every waiting partition would wake up, contend, and only one would succeed while the others went back to sleep. The result was random partition selection across multiple pipelines, causing all jobs to stall together instead of completing sequentially. The user had articulated the desired policy with characteristic precision: "lowest partition in oldest pipeline."

The assistant's implementation replaced both mpsc channels with priority queues using BTreeMap wrapped in a Mutex, keyed on (job_seq ASC, partition_idx ASC). The change was substantial—298 insertions and 225 deletions in engine.rs alone—and required adding the job_seq field to both PartitionWorkItem and SynthesizedJob.

When the priority queue system was finally running and tested with two concurrent PoRep proofs, the results were unambiguous. The assistant's measurement message reported: "Both PoRep proofs completed. Throughput improved too: 0.602 proofs/min (99.7s/proof) vs the previous 0.485 proofs/min (123.7s/proof)—that's a 24% improvement from ordered scheduling, because Job A finished faster without GPU contention from Job B."

The key observation was even more revealing: "Job A took 114.4s (GPU had it all to itself), Job B took 199.5s (waited for Job A to finish first). Previously both took ~245s because they were interleaving randomly." This decomposition of wall time into individual job times demonstrated the mechanism of the improvement: by eliminating random GPU interleaving, the first job finished in less than half its previous time, and the total throughput increased by 24%.

But even this victory was incomplete. Immediately after the priority queue commit, the user sent another observation: "Commit; GPU seem now in order, synth still not quite." A screenshot from the real-time status dashboard revealed that synthesis workers were still processing partitions in a scrambled order within individual pipelines. One pipeline showed P0 through P6 synthesized, but then P9, P11, and P12 completed while P7, P8, and P10 were still pending.

The root cause was that the synthesis worker pool operated on a decoupled pop-and-acquire pattern: workers would pop the highest-priority item from the BTreeMap queue, then race for budget acquisition. The Notify-based budget mechanism did not guarantee fairness or ordering, so a worker holding a low-priority item could acquire budget before a worker holding a high-priority one. The priority queue ensured items were popped in order, but it could not control the order in which they were executed.

The insight that emerged was that ordering must be enforced at the resource acquisition layer, not just the queue layer. The correct design requires a single dispatcher that serializes both the queue pop and the budget acquisition: peek at the highest-priority item, determine its memory requirement, acquire the necessary budget, pop the item, and hand it to a worker through a bounded channel. This ensures that budget is acquired before the item is popped, and that only one item is in flight at the dispatch level.

The assistant implemented this fix, and the user confirmed in message [msg 2954]: "Looking good on pipelines now!" The scheduling saga was complete. But the screenshot that accompanied this message contained the seed of the next investigation.

Part II: The GPU Utilization Mystery

The user's message [msg 2954] opened with congratulations but immediately pivoted to a new concern: "one remaining issue or mystery, GPUs seem mostly idle, compute only occasionally overlaps on them. Not clear what the bottleneck is, it might be fundamental like memory bandwidth or something silly like some lock being held in a racy way somewhere for way too long."

The attached screenshot told a stark story. The GPU utilization graph, captured at 0.2-second resolution as the user later clarified in [msg 2960], showed rectangular blocks of GPU activity separated by multi-second idle gaps. Despite a large backlog of synthesized partitions waiting for GPU proving—27 "provers in flight" at one point—GPU compute utilization hovered around 50%. The GPU was spending as much time doing nothing as it was computing, even though work was queued and ready.

The user framed the puzzle precisely: "Observed is that we have a big synth backlog, and sub-50% gpu compute utilization, and only 5 actual synths running." This was an invitation to investigate—but also a warning against premature conclusions.

The numbers were puzzling. With 27 partitions in the proving stage and only 2 GPU workers available, 25 partitions were queued waiting for GPU access. With that many items backed up in the GPU queue, the GPU should theoretically never be idle—when one worker finished, the next should immediately pick up the next partition. Yet the utilization graph showed multi-second gaps between GPU activity blocks. Something between partition proves was creating idle time, and that something was the target of the investigation.

Part III: The Spiral of Speculation

The assistant's first response in [msg 2955] was a tour de force of systems reasoning—and a cautionary example of how reasoning without data can lead in circles. Over thousands of words of internal monologue, the assistant cycled through hypothesis after hypothesis, each internally consistent, each backed by careful tracing through the code, but none confirmable without measurement.

The dispatcher as bottleneck: Perhaps the serial budget acquisition was starving the GPU queue. But the user corrected this in [msg 2956]: "It's not the dispatcher, we had this before." This single sentence eliminated an entire branch of analysis based on temporal reasoning—the GPU utilization problem predated the dispatcher changes, so it could not be caused by them.

The two-phase prove flow: Perhaps the C++ mutex inside prove_start/prove_finish was serializing GPU access, forcing workers to queue up. The assistant traced through the flow: the GPU worker calls the blocking CUDA function, releases budget, spawns the finalizer task, then loops back to grab the next job. If the C++ mutex serializes CUDA execution across both workers, each worker blocks for roughly 7 seconds while the other waits, creating the appearance of alternating utilization.

The blocking thread pool: Perhaps Tokio's spawn_blocking pool was saturated by synthesis and finalizer tasks. The assistant reasoned through this: with 6 synthesis tasks running at a time and Tokio's pool growing up to 512 threads by default, there should be plenty of capacity. But the synthesis tasks are CPU-bound and take ~30s each, potentially pinning cores while running on blocking threads.

CPU starvation from rayon: Perhaps six concurrent synthesis tasks were consuming all CPU cores, starving the GPU worker's preprocessing phase. The assistant considered this but then realized that Rayon uses a shared global thread pool with 30 total worker threads, not 30 per task, so CPU contention might not be as severe as initially thought.

PCIe data transfer: Perhaps moving ~9 GiB of synthesized data per partition was bandwidth-limited. The user ruled this out in [msg 2963] with a single data point: "Data tx is at 50GB/s pcie gen5 x16"—making transfer time ~180ms, far too fast to explain multi-second gaps.

Tracker lock contention: This hypothesis gained the most traction. The assistant traced through the GPU worker loop and discovered that the GPU worker acquires tracker.lock().await twice between partition proves—once to check if the job failed, once to mark the worker as busy. Meanwhile, finalizer tasks also hold this same lock for process_partition_result, which calls malloc_trim(0) on every single partition completion while holding the lock.

The malloc_trim(0) call was the smoking gun. This function walks the entire heap and returns free memory to the OS. On a 400+ GiB heap, this operation can take hundreds of milliseconds or even seconds. With 27 provers in flight, multiple finalizers could be queued up holding the tracker lock while calling malloc_trim, and the GPU worker's two lock acquisitions would get interleaved with these finalizer tasks, each costing potentially hundreds of milliseconds of idle time.

The assistant's reasoning built a compelling case. The tracker lock was a tokio::sync::Mutex, which is fair (FIFO). If a finalizer held the lock while calling malloc_trim(0), the GPU worker would have to wait its turn in the queue. With multiple finalizers competing for the same lock, the GPU worker could be blocked for seconds at a time—precisely the multi-second idle gaps visible in the utilization graph.

But compelling as this hypothesis was, it remained a hypothesis. The assistant acknowledged the circularity of the reasoning: "I'm going in circles analyzing this" ([msg 2961]).

Part IV: The User's Methodological Intervention

The user's response came in a series of messages that together reframed the entire investigation. Each message was brief, precise, and devastating to the assistant's speculative approach.

First, in [msg 2956], a crisp correction: "It's not the dispatcher, we had this before." This eliminated an entire branch of analysis.

Second, in [msg 2957], a methodological directive: "We should understand exactly what GPU workers are doing." This was not a suggestion to think harder—it was a directive to look. The emphasis on "exactly" rejected approximation, inference, and indirect reasoning.

Third, in [msg 2958], a crucial observation: "Note there are times when 3-5 partitions are chained on GPUs with 'perfect' utilization too, tho rare." This single sentence eliminated any hypothesis that posited a fixed hardware limitation. If the GPU could achieve perfect utilization, the bottleneck was not fundamental—it was intermittent and context-dependent.

Then came the decisive question in [msg 2962]: "Should/Can we first add and gather logs with real evidence?"

This question cut through the speculation with surgical precision. It did not challenge the assistant's hypotheses—it challenged the methodology. The assistant had been building increasingly elaborate theories about what might be causing the gaps. The user insisted on knowing what was causing them.

The question is remarkable for what it implicitly communicates. It does not say "you're wrong." It does not say "fix this instead." It says, in effect: "before we act on any of these theories, let's make sure we're acting on the right one." This is the essence of evidence-driven engineering—the willingness to delay gratification, to resist the urge to fix, and to invest in measurement infrastructure before applying a fix.

Part V: The Pivot to Instrumentation

The assistant's response in [msg 2964] was immediate and unequivocal: "Good call — let me instrument rather than guess. I'll add timing logs around each step in the GPU worker hot path so we can see exactly where the seconds go."

This single sentence represents a profound methodological pivot. The assistant's todo list, which had previously prioritized "Fix malloc_trim inside tracker lock" and "Merge/eliminate tracker.lock() calls," was rewritten. The new top priority was "Add timing instrumentation to GPU worker hot path." The fix tasks were pushed to "pending." The investigation was no longer about fixing the suspected bottleneck—it was about measuring it.

The user reinforced this direction in [msg 2967] with a message that balanced validation with discipline: "The malloc trim is a good guess tho." The word "tho" (though) was the key—it acknowledged the hypothesis while maintaining the commitment to evidence before action. The assistant understood: "The user is saying malloc_trim is a good guess but we should still gather data first" ([msg 2968]).

The instrumentation plan, laid out in [msg 2968], was comprehensive. The goal was to measure every significant step in the gap between consecutive partition proves, using Instant::now() for high-resolution timing and distinctive log prefixes for easy grep analysis.

The GPU worker hot path would be instrumented to measure:

Part VI: Executing the Instrumentation

The execution followed the plan with methodical precision. In [msg 2965], the assistant read the GPU worker loop starting at line 2484 of engine.rs to find the exact instrumentation points. In [msg 2966], it read the split GPU proving phase at lines 2625-2631. In [msg 2969], a grep for malloc_trim revealed two calls at lines 205 and 222 of engine.rs—both unsafe { libc::malloc_trim(0); } inside process_partition_result. In [msg 2970], the assistant read the function signature and control flow to understand exactly where to insert timing probes.

Then came the edits. In [msg 2971], the assistant instrumented the GPU worker hot path: "Now I'll instrument both the GPU worker hot path and the finalizer. Let me do the GPU worker first—add timing between each step." In [msg 2972], the assistant instrumented inside the spawn_blocking call: "Now instrument inside the spawn_blocking for gpu_prove_start—time the mutex wait vs actual compute." In [msg 2973], the assistant instrumented the finalizer: "Now instrument the finalizer—time prove_finish, reservation drop, tracker lock wait, process_result, and malloc_trim."

Each edit was applied to engine.rs and confirmed successful. The instrumentation was complete.

The user's response in [msg 2974] was empty—just the structural wrapper of a conversation data tag with nothing inside. But this emptiness was itself a signal. It communicated trust: the user had initiated the evidence-gathering approach, the assistant had executed it correctly, and no further direction was needed. The silence ratified the plan and cleared the path for the next phase: deployment and data collection.

This is a form of implicit trust earned through demonstrated competence. Across the preceding 2900+ messages, the assistant had committed and deployed seven major features—a memory manager, a status API, a monitoring UI, priority scheduling, and multiple bug fixes. The user's empty response acknowledged that the assistant understood the task and executed it correctly.

Part VII: The Broader Significance

This sequence of messages—from confident hypothesis, through circular reasoning, to user-led redirection toward evidence, to systematic instrumentation—illustrates a recurring challenge in performance debugging. Complex distributed systems resist purely deductive analysis. The interaction graph is too large, the timing dependencies too subtle, and the human capacity for reasoning about concurrent systems too limited. The correct move, as the user recognized, is to instrument first and theorize second.

The assistant's willingness to abandon its elaborate hypotheses and pivot to instrumentation is a strength. The reasoning in [msg 2961] shows genuine intellectual exploration—weighing evidence, discarding weak theories, refining questions. But without data, that exploration cannot converge. The instrumentation edits in messages 2971-2973 are where the exploration ends and the measurement begins.

The approach also demonstrates a key principle of performance debugging: instrument before you fix. Even when both the user and assistant agreed that malloc_trim was a strong suspect, the discipline of measurement protected against confirmation bias. The logs might show that malloc_trim takes only a few milliseconds, while the real bottleneck is something else entirely—perhaps the GPU mutex serialization, perhaps the spawn_blocking scheduling delay, perhaps something no one had considered. By instrumenting broadly—covering the entire hot path from prove_start return to the next prove_start entry—the assistant ensured that whatever the bottleneck turned out to be, the logs would capture it.

The instrumentation design itself reflects a sophisticated understanding of what makes timing data useful. The use of Instant::now() provides microsecond precision. The distinctive log prefixes (GPU_TIMING, FIN_TIMING) enable simple grep-based extraction. The separation of the spawn_blocking call into mutex-wait and compute phases allows the team to distinguish between contention and computation. The instrumentation of the finalizer separately from the GPU worker allows correlation analysis—do GPU idle gaps coincide with finalizer activity?

Part VIII: What the Instrumentation Will Reveal

The instrumented binary is ready for deployment. The next steps, as outlined in the assistant's todo list, are clear: build the instrumented binary, deploy it to the remote machine, gather logs from a live workload, and analyze the timing data to pinpoint the actual cause of the multi-second idle gaps.

The instrumentation will answer several questions definitively:

Conclusion

The segment spanning messages 2954 through 2974 captures a pivotal transition in the cuzk proving engine development: from speculation to measurement, from hypothesis to evidence, from guessing to knowing. The user's insistence on instrumentation before action—"Should/Can we first add and gather logs with real evidence?"—redirected an investigation that had been spiraling in increasingly circular reasoning. The assistant's graceful pivot—"Good call — let me instrument rather than guess"—demonstrated the intellectual discipline that separates effective debugging from wheel-spinning.

The instrumentation itself is a model of systematic measurement: three edits targeting the GPU worker hot path, the spawn_blocking GPU prove call, and the finalizer, each with precise timing probes and distinctive log prefixes for easy analysis. Whether malloc_trim turns out to be the culprit or an innocent bystander, the instrumentation ensures that the eventual fix will be applied to the right problem.

In a session full of complex code changes and architectural decisions, this sequence stands out because it is not about code at all. It is about methodology. It is about the willingness to say "I don't know yet, let me measure." And that, ultimately, is what separates guesswork from engineering.

The priority queue scheduling fix that preceded this investigation delivered a 24% throughput improvement and solved a fundamental scheduling flaw. But the GPU utilization investigation that followed represents a different kind of achievement—not a code change, but a methodological commitment. The team chose to understand before acting, to measure before fixing, to gather evidence before forming conclusions. In the long arc of systems engineering, that commitment may prove more valuable than any single optimization.

The next chapter of this story will be written when the instrumented binary is deployed and the logs are analyzed. The data will reveal the truth about the multi-second idle gaps, and the team will apply a fix based on evidence, not speculation. That is the promise of the evidence imperative—and the cuzk proving pipeline is about to collect its reward.