The Budget That Bound: Diagnosing Concurrency Collapse in a Memory-Managed GPU Proving Engine

Introduction

In the high-stakes world of GPU-accelerated zero-knowledge proof generation, memory is the ultimate scarce resource. Every partition of a 32 GiB PoRep proof demands roughly 14 GiB of working memory, the Structured Reference String (SRS) consumes 44 GiB, and the Pre-Compiled Constraint Evaluator (PCE) adds another 26 GiB. Juggling these demands across concurrent proofs while avoiding out-of-memory (OOM) kills is a delicate balancing act. In message [msg 2356] of an opencode coding session, an AI assistant confronts the sobering reality of a brand-new budget-based memory manager: instead of enabling the promised concurrency, the system has collapsed to running only one partition at a time.

This message is a pivotal diagnostic moment. The assistant has just deployed a unified memory manager to a production-grade remote machine (755 GiB RAM, RTX 5090, 64 cores), replacing a fragile static concurrency limit with a dynamic, budget-gated admission control system. But the user's feedback is stark: "Seems there is only one synthesis running at a time, previously the machine was handling 10 or 8" ([msg 2353]). The assistant must now parse the telemetry, reconcile the budget math with reality, and decide whether the problem is a configuration error, a race condition, or a fundamental design flaw.

The Message: A Diagnostic Crossroads

The subject message, [msg 2356], is the assistant's response to the user's observation. It contains no tool calls—no bash commands, no file edits, no greps. It is pure analysis: a reasoning-heavy interlude where the assistant synthesizes data from multiple sources (RSS traces, daemon logs, budget accounting) into a coherent diagnosis. The message is structured as an internal monologue, beginning with "## Agent Reasoning" and proceeding through a chain of logical deductions.

The assistant opens by confirming the RSS data:

Good data. The RSS is oscillating between ~100G and ~114G, which confirms: - Baseline: ~70G (SRS 44 + PCE 26) - Each partition adds ~14G, bringing it to ~84-98G - But we see peaks of ~114G, which means occasionally 2-3 partitions overlap

This is a critical sanity check. The RSS trace (collected over several minutes in [msg 2355]) shows the daemon's memory footprint oscillating in a sawtooth pattern: climbing to ~114 GiB, dropping to ~98 GiB, climbing again. The assistant correctly interprets this as the budget system working—RSS stays close to the configured 100 GiB cap. But the oscillation also reveals that only 2-3 partitions ever overlap, not the 8-10 the user expects.

The assistant then articulates the core problem with crystalline clarity:

So the budget IS constraining things — RSS stays around 100-114G which is close to our 100 GiB budget. But the issue is only 1 partition synthesizes at a time because: 1. Budget = 100 GiB 2. 30 GiB remains after accounting for the permanent 70G baseline, which only allows ~2 partitions max to fit simultaneously

This is the moment of recognition. The budget system is functioning exactly as designed—it is limiting concurrency to prevent OOM. But the limit was set too aggressively. The 100 GiB budget was chosen for testing, but on a machine with 755 GiB of RAM, it is artificially throttling throughput.

The Reasoning Chain: Unpacking the Budget Math

The assistant's reasoning proceeds through several layers of analysis, each building on the previous.

Layer 1: Confirming the RSS envelope. The oscillation between ~100 GiB and ~114 GiB confirms that the budget ceiling is being respected. RSS never exceeds ~114 GiB, which is within the 100 GiB budget plus some overshoot from CUDA pinned memory and transient allocations that the budget doesn't perfectly track.

Layer 2: Decomposing the baseline. The assistant estimates a permanent baseline of ~70 GiB: 44 GiB for the SRS (loaded on demand for the first proof) plus 26 GiB for the PCE (extracted and cached during the first proof). This leaves ~30 GiB of working budget for partition synthesis.

Layer 3: Partition sizing. Each partition requires ~14 GiB of working memory. With ~30 GiB available, the system can accommodate at most 2 partitions simultaneously. This explains why the user sees only one synthesis running at a time—the budget allows at most two, and scheduling artifacts (one slot consumed by background PCE extraction) reduce effective concurrency to one.

Layer 4: The SRS double-acquisition hypothesis. The assistant notices a puzzling data point: budget_used_gib=88 appears in the logs shortly after SRS loading. If SRS is 44 GiB, why is the budget reporting 88 GiB used? The assistant hypothesizes a race condition:

There's also a potential double-counting issue where SRS is being pre-acquired twice (once per proof), which would explain why the budget jumps to 88 GiB right after the first SRS load

The theory is that when three proofs are submitted simultaneously, each one independently tries to acquire 44 GiB for SRS loading. The first proof succeeds and converts its reservation to permanent. But the second and third proofs also attempt the same acquisition, temporarily reserving another 44 GiB each. During this race window, the budget could see 2 × 44 GiB = 88 GiB reserved for SRS alone, starving partition synthesis.

Layer 5: The temporal dimension. The assistant notes that PCE enters the budget partway through execution, not from the start. Early partitions might have more breathing room before the full 26 GiB PCE footprint is established. This temporal nuance means the concurrency collapse is progressive: early partitions may overlap, but as the PCE cache fills, the available budget shrinks.

Assumptions Under the Microscope

Every diagnostic rests on assumptions, and this message is no exception. Several are worth examining.

Assumption 1: The baseline is exactly 70 GiB. The assistant assumes SRS = 44 GiB and PCE = 26 GiB, summing to 70 GiB. These figures come from earlier measurements and configuration, but real memory consumption is rarely so clean. CUDA driver overhead, pinned memory pools, kernel launch buffers, and the Rust runtime itself all consume memory outside the budget's accounting. The actual baseline could be higher, which would further reduce the working budget.

Assumption 2: The SRS double-acquisition race is real. The assistant infers a race from the budget_used_gib=88 log line, but there are alternative explanations. The SRS loading code might report its reservation in a way that double-counts the SRS size (e.g., reporting both the file size and the in-memory allocation). Or the 88 GiB might include the first partition's working set alongside the SRS. The assistant acknowledges this uncertainty by planning to "check the SRS pre-acquisition code" in the next step.

Assumption 3: The budget system's accounting is accurate. The assistant treats the budget's reported budget_used_gib as ground truth. But the budget system is itself new code, freshly implemented in this segment. There could be accounting bugs—reservations not released, double-counting, or race conditions in the budget's own internal state. The assistant implicitly trusts the budget's numbers while simultaneously questioning the SRS accounting, an interesting tension.

Assumption 4: The user's previous throughput (8-10 partitions) was safe. The user reports that the machine previously handled 8-10 partitions concurrently without issues. The assistant accepts this as the target performance baseline. But it's worth asking: was the previous configuration actually safe, or was it running dangerously close to OOM? The old partition_workers = 16 setting had no memory awareness—it could have been overcommitting. The new budget system might be correctly identifying that 8-10 partitions shouldn't fit in the available memory, and the previous configuration was simply lucky.

Input Knowledge: What the Reader Must Understand

To fully grasp this message, one needs familiarity with several concepts:

The GPU proving pipeline. Zero-knowledge proofs in systems like Filecoin's proving ecosystem involve multiple stages: loading the SRS (a large cryptographic structured reference string), extracting the PCE (pre-compiled constraint evaluator), synthesizing partition constraints, and running GPU-based Groth16 proving. Each stage has distinct memory characteristics.

The budget-based memory manager. This is the system under test. It replaces a static concurrency limit (partition_workers) with a dynamic admission control system. The MemoryBudget tracks reservations and releases, allowing the engine to admit new partitions only when sufficient memory is available. The budget is configured with a total_budget (e.g., 100 GiB) and a safety_margin to account for non-budgeted memory consumers.

RSS vs. budget accounting. RSS (Resident Set Size) is the operating system's measure of physical memory used by a process. The budget system maintains its own accounting of memory reservations, which may not perfectly align with RSS due to CUDA pinned memory, page cache, and kernel allocations.

SRS and PCE caching. The SRS is loaded once and shared across all proofs for a given circuit type. The PCE is extracted per-circuit and cached. Both are "permanent" reservations that persist for the lifetime of the daemon, reducing the budget available for working sets.

The tokio async runtime. The assistant's earlier fix (replacing blocking_lock() with try_lock() in the evictor callback) was necessitated by tokio's requirement that blocking operations not be performed on async worker threads. This context is relevant because the budget system's evictor runs inside the async acquire() loop.

Output Knowledge: What This Message Creates

This message produces several valuable outputs:

A confirmed diagnosis. The assistant establishes that the budget system is working correctly but is configured with an overly restrictive cap. The 100 GiB budget leaves only ~30 GiB for working sets, limiting concurrency to 1-2 partitions.

A prioritized action plan. The assistant identifies two concrete issues to address: (1) increase the budget to match available memory, and (2) investigate the SRS double-acquisition race. The message ends with a grep command to examine the SRS pre-acquisition code.

A framework for reasoning about memory budgets. The assistant's decomposition—baseline + working set = total budget—provides a mental model that can be applied to any memory-constrained system. The insight that "early partitions have more breathing room before PCE caching" adds a temporal dimension to what is often treated as a static allocation problem.

A validation of the budget system's correctness. Despite the concurrency collapse, the assistant repeatedly notes that "the budget IS working." RSS stays within the configured envelope. The evictor's try_lock() fix (from the previous message) prevents panics. The system is stable and predictable—just configured wrong.

The Thinking Process: A Window into Debugging Methodology

The assistant's reasoning in this message exemplifies a structured debugging methodology that is worth examining in detail.

Step 1: Validate the data. Before drawing conclusions, the assistant confirms that the RSS trace is consistent with expectations. The oscillation pattern tells a story: memory is allocated, used, and released in cycles corresponding to partition completion.

Step 2: Build a quantitative model. The assistant constructs a simple model: baseline (SRS + PCE) + partitions × working_set = total. This model allows the assistant to compute the expected concurrency (2 partitions) and compare it to the observed behavior (1 partition at a time).

Step 3: Identify the discrepancy. The model predicts 2 partitions, but the user observes 1. The assistant doesn't dismiss this as measurement error—it takes the discrepancy seriously and looks for explanations (the SRS double-acquisition race, PCE timing effects).

Step 4: Formulate testable hypotheses. The SRS double-acquisition hypothesis is testable: examine the SRS pre-acquisition code to see if concurrent proofs can each reserve the full SRS size. The budget-too-tight hypothesis is testable: increase the budget and observe whether concurrency improves.

Step 5: Plan the next investigation. The message ends with a concrete next step: "Let me check the SRS pre-acquisition code and fix the race, then test with a realistic budget." This transitions from analysis to action.

Mistakes and Blind Spots

No analysis is perfect, and this message has several limitations.

Over-reliance on budget accounting. The assistant treats budget_used_gib=88 as a signal of SRS double-acquisition, but the budget system is new and untested. The 88 GiB could equally be a bug in the budget's own accounting—a reservation that wasn't properly released, or a double-count in the reporting code.

Underestimation of non-budgeted memory. The assistant's model accounts for SRS, PCE, and partition working sets, but ignores CUDA driver overhead, kernel memory, file page cache, and the Rust allocator's internal fragmentation. These could easily add 10-20 GiB to the baseline, further reducing the working budget.

Confirmation bias toward the budget system. The assistant repeatedly states that "the budget IS working" and "the budget IS constraining things." While technically true (RSS stays within the cap), this framing subtly deflects attention from the possibility that the budget system itself has design flaws. A more skeptical framing would be: "The budget system is successfully limiting concurrency, but it may be limiting it too much due to accounting errors or overly conservative estimates."

Missing the user's implicit question. The user asks why only one synthesis runs at a time. The assistant answers with budget math. But the user's deeper question might be: "Why is the new system worse than the old one?" The assistant's answer—"the old system was unconstrained and lucky"—is plausible but not proven. A more thorough answer would compare RSS under the old configuration to confirm that the machine was indeed using more memory before.

Conclusion

Message [msg 2356] is a masterclass in diagnostic reasoning under uncertainty. The assistant takes noisy telemetry data, constructs a quantitative model, identifies a discrepancy, and formulates testable hypotheses—all without running a single command. The message reveals the fundamental tension at the heart of any resource management system: the tradeoff between safety and utilization. A budget that is too tight guarantees stability but starves throughput. A budget that is too loose risks OOM kills. Finding the right balance requires not just good code, but good reasoning.

The assistant's next steps—checking the SRS pre-acquisition code and increasing the budget—will determine whether the memory manager can deliver on its promise of safe, concurrent proof generation. But regardless of what those steps reveal, this message stands as a clear-eyed moment of diagnosis, where the assistant refuses to blame the system and instead asks the harder question: "What is the budget actually doing, and is it the right thing?"

In the end, the budget system passes its first real test: it is predictable, it is stable, and it is wrong in a way that can be fixed with a configuration change. That is far better than being unpredictable, unstable, and inexplicable—a fate that the assistant's careful reasoning has helped the team avoid.