The Silence of the Partitions: When a Memory Manager Silently Serializes a GPU Pipeline

"Seems there is only one synthesis running at a time, previously the machine was handling 10 or 8."

This short observation, delivered by the user in message <msg id=2353>, is one of those deceptively simple statements that carries an enormous amount of diagnostic weight. On its surface, it is a factual report of observed behavior: the new budget-based memory manager, just deployed to a remote machine with 755 GiB of RAM and an RTX 5090, is serializing work that previously ran with 8–10× concurrency. But beneath that surface lies a cascade of interconnected design decisions, assumptions, and emergent behaviors that this single sentence exposes.

The Context: A Memory Manager Goes Live

To understand why this message matters, we need to reconstruct the state of the system at the moment it was written. The assistant had just completed a multi-round deployment of a brand-new unified memory management architecture for the cuzk GPU proving engine (see [chunk 17.1]). This architecture replaced a fragile static concurrency limit (partition_workers = 16) with a sophisticated budget-based admission control system. The idea was elegant: instead of hard-coding how many partitions could run simultaneously, the system would track actual memory consumption — SRS files, PCE caches, working set per partition — and admit new work only when sufficient budget remained.

The deployment had been dramatic. The first binary crashed with a tokio runtime panic because the evictor callback used blocking_lock() on a tokio Mutex from within an async context. The assistant fixed that by switching to try_lock(), rebuilt the Docker image, uploaded the binary, and restarted the daemon. The fix worked — no more panics. But the benchmark that followed revealed a different kind of failure: not a crash, but a catastrophic loss of throughput.

What the User Saw

The user ran a benchmark with three concurrent proofs, each containing 10 partitions — 30 partitions total, submitted with concurrency 3. The old system, with its static partition_workers = 16 limit and 755 GiB of RAM, would have dispatched 8–10 of these partitions simultaneously, saturating the GPU and completing the batch efficiently. What the user observed instead was a trickle: one partition would start, run to completion, and only then would the next partition begin. The pipeline had been serialized.

This is what the user reported. The phrasing is measured — "Seems there is only one synthesis running at a time" — but the implied question is sharp: why did your fancy new memory manager turn my 10-way parallel pipeline into a single-file line?

The Root Cause: A Perfect Storm of Budget Starvation

The assistant's investigation, visible in the subsequent reasoning in <msg id=2354> and <msg id=2355>, reveals a multi-layered problem. The budget was configured at 100 GiB — intentionally tight for testing, but far too restrictive for the machine's capabilities. The baseline memory footprint alone consumed 70 GiB: 44 GiB for the SRS (Structured Reference String) and 26 GiB for the PCE (Pre-Compiled Constraint Evaluator) cache. That left only 30 GiB for working sets, enough for roughly two partitions at 14 GiB each.

But the situation was worse than that simple arithmetic suggested. A race condition in the SRS pre-acquisition logic caused multiple concurrent proofs to each reserve 44 GiB simultaneously, inflating the budget usage to 88 GiB before any partition had even started synthesizing. With only 12 GiB remaining — less than the 14 GiB needed for a single partition — the admission controller effectively locked the door. Only after one proof completed its SRS acquisition and released its reservation could the next partition squeeze through.

The SYNTH_START/SYNTH_END timeline from the daemon logs tells the story in stark terms: partition 0 starts at 14:31:07, ends at 14:31:49 (42 seconds), partition 7 starts only after partition 0 ends, partition 1 starts only after partition 7 ends, and so on. Each partition runs sequentially because the budget never has room for more than one at a time.

Assumptions Embedded in the Architecture

The user's observation exposes several assumptions that were baked into the memory manager design:

Assumption 1: A tight budget is fine for testing. The assistant set total_budget = "100GiB" as a test value, expecting it to demonstrate budget gating. But on a machine with 755 GiB of RAM, this was not a test — it was a straitjacket. The user's baseline expectation of 8–10 concurrent partitions required a budget closer to 250–500 GiB.

Assumption 2: SRS pre-acquisition would not race. The code assumed that SRS would be loaded once and then converted to a permanent reservation. But when three proofs arrived simultaneously, each one independently ran the "is SRS loaded?" check before any of them had completed loading, causing all three to pre-acquire 44 GiB. This double-counting starved the budget before real work could begin.

Assumption 3: The evictor would compensate. The evictor callback was designed to free memory by evicting idle SRS or PCE entries when the budget ran low. But the evictor itself had just been fixed from a blocking panic, and its try_lock()-based implementation could be silently skipped if the mutex was held — meaning eviction might not happen when it was most needed.

Assumption 4: Synthesis concurrency is independent of budget. The config included synthesis_concurrency = 2, but this setting was irrelevant when the budget could only admit one partition at a time. The budget was the binding constraint, not the concurrency limit.

The Diagnostic Value of a Single Observation

What makes this message so valuable is its precision. The user didn't say "it's slow" or "something's wrong." They gave a specific, quantifiable observation: one synthesis at a time, previously 10 or 8. This immediately tells the assistant:

1. The bottleneck is in admission, not execution (partitions complete fine once started).
2. The budget is too tight by roughly an order of magnitude.
3. The SRS double-acquisition race is likely inflating usage.
4. The fix is to increase the budget, fix the race, or both. Without this precise feedback, the assistant might have chased other hypotheses — GPU utilization, network latency, disk I/O — and wasted hours debugging the wrong layer. The user's observation cut directly to the correct diagnosis.

The Broader Lesson: Memory Management as a Social Contract

This episode illustrates a fundamental tension in memory management for GPU proving systems. The old approach — static partition_workers limits — was crude but predictable. It didn't track memory at all; it simply assumed the machine had enough RAM and relied on the operator to set the limit conservatively. The new approach — budget-based admission control — is more sophisticated but also more fragile. It requires accurate accounting of every memory consumer, race-free acquisition paths, and a budget that reflects real-world availability rather than test values.

The user's message at <msg id=2353> is the moment where theory meets practice. The elegant budget system, designed in the abstract, collides with the messy reality of concurrent proof requests, SRS loading races, and the gap between configured budget and actual machine capacity. It is a reminder that memory management is not just an engineering problem but a social contract between the system and its operator: the system must accurately track and enforce limits, and the operator must set those limits to reflect real workloads.

The Aftermath

The assistant's response to this message was swift. In <msg id=2354> and <msg id=2355>, the assistant analyzed the daemon logs, confirmed the serialization, identified the SRS double-acquisition race, and pivoted to a new configuration: total_budget = "auto" (which detected 750 GiB) with a safety_margin = "5GiB". This allowed all 30 partitions to start within one second — but then the daemon was OOM-killed as RSS hit ~500 GiB, revealing that the safety margin was far too small for a machine running Curio and other co-located processes. The final resolution required a safety margin of ~250 GiB to account for background memory usage.

The user's seven-word observation thus triggered a full debugging cycle: from budget starvation to SRS race to OOM to safety margin calibration. It is a masterclass in how a precise user report can collapse an enormous search space and point directly at the root cause.