The Deliberate Stress Test: Deploying a Budget-Based Memory Manager to Production

Introduction

In the high-stakes world of GPU-accelerated proof generation for Filecoin, memory is the most precious and dangerous resource. Too little, and proofs stall; too much, and the operating system's OOM killer terminates the process. Message 2315 in this opencode session captures a pivotal moment: the deployment of a newly implemented budget-based memory manager to a remote production machine, deliberately configured with a tight memory constraint to verify that the system actually enforces its limits. This message is not merely a deployment step—it is a controlled experiment designed to validate weeks of architectural work against the unforgiving reality of a real GPU proving server.

The Context: A New Memory Architecture

The message arrives at the culmination of a major engineering effort. The assistant had just implemented a comprehensive unified memory manager for the cuzk GPU proving engine, replacing a fragile static concurrency limit (partition_workers) with a byte-level budget system that tracks all major memory consumers: SRS pinned GPU memory (~44 GiB for 32 GiB PoRep proofs), PCE heap allocations (~26 GiB), and synthesis working sets (~14 GiB per partition). The system was designed to auto-detect system RAM, support LRU eviction for SRS and PCE caches, and implement two-phase working memory release (partial release after prove_start, full release after prove_finish).

The code had been committed with a descriptive message (see [msg 2296]), a Docker binary had been built and uploaded to the remote machine at 141.0.85.211, and the assistant had verified that the machine had 755 GiB RAM, an RTX 5090 with 32 GiB VRAM, and 64 CPU cores. The existing production config at /tmp/cuzk-run-config.toml still used the deprecated preload and partition_workers fields, confirming that this deployment would be the first real-world test of the new system.

The Message: Writing the Experiment

The subject message ([msg 2315]) consists of two parts: a reasoning preamble and a bash command. Here is the message in full:

Good, there's c1 test data. Now let me write a memory-constrained config and deploy. The key test: on a 755 GiB machine, set a tight budget to verify memory constraints actually limit concurrency.

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[bash] ssh -p 40612 root@141.0.85.211 'cat > /tmp/cuzk-memtest-config.toml << 'EOF' [daemon] listen = "0.0.0.0:9820"

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[srs] param_cache = "/var/tmp/filecoin-proof-parameters" # No preload — on-demand loading

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[memory] # Set a tight budget: 100 GiB total # SRS ~44 GiB + PCE ~26 GiB = 70 GiB baseline # That leaves ~30 GiB for working set = ~2 partitions at 14 GiB each total_budget = "100GiB" safety_margin = "0GiB" eviction_min_idle = "5m"

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[synthesis] # Keep default concurrency threads = 0

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[gpus] gpu_workers_per_device = 2 gpu_threads = 32

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[pipeline] enabled = true synthesis_concurrency = 2 EOF cat /tmp/cuzk-memtest-config.toml'

The config is then echoed back, confirming it was written correctly.

The Reasoning: Why a Tight Budget?

The assistant's thinking is explicit and deliberate. The preamble states the goal: "set a tight budget to verify memory constraints actually limit concurrency." This is a stress test, not a production configuration. The assistant is intentionally creating a scenario where the memory manager must throttle work, because only by observing throttling can one confirm the system is working as designed.

The reasoning continues in the config comments themselves, which read like a worked example:

Decisions Made in This Message

Several deliberate design choices are embedded in this config:

1. Zero safety margin. The safety_margin = &#34;0GiB&#34; setting is aggressive. In production, the safety margin is meant to account for memory used by other processes (Curio, the OS, monitoring agents) and for fragmentation or allocation overhead. Setting it to zero means the assistant is testing the pure budget logic without any buffer—if the system works with zero margin, it will certainly work with a reasonable one.

2. No preload. The config explicitly disables SRS preloading with the comment "No preload — on-demand loading." This tests the new on-demand loading path, which is a key feature of the memory manager. Previously, SRS was preloaded at startup, consuming a fixed amount of memory regardless of actual need. The new system loads SRS on demand and can evict it under memory pressure. By disabling preload, the assistant ensures that SRS loading happens during proof processing, which exercises the eviction and admission control paths.

3. synthesis_concurrency = 2. This setting limits the number of concurrent synthesis operations to 2, which aligns with the calculated ~2 partitions that fit in the remaining budget. This is a deliberate choice to avoid overwhelming the system with too many concurrent requests that would all compete for the same limited budget.

4. eviction_min_idle = &#34;5m&#34;. This sets a five-minute idle threshold before eviction candidates are considered. This is long enough to avoid thrashing during a single benchmark run but short enough to eventually release memory if the daemon becomes idle.

Assumptions Embedded in the Message

The assistant makes several assumptions, some explicit and some implicit:

Explicit assumption: The memory estimates (44 GiB SRS, 26 GiB PCE, 14 GiB per partition working set) are assumed to be accurate for the 32 GiB PoRep proof type on this hardware. These numbers came from earlier analysis and benchmark runs, but they have not been verified on this specific machine with this specific binary.

Implicit assumption: The remote machine has sufficient disk I/O bandwidth to load SRS and PCE on demand without causing excessive latency. On-demand loading trades memory for I/O, and if the disk is slow, the system could become CPU-starved while waiting for data to load.

Implicit assumption: The cuzk binary built in the Docker container (using CUDA 13.0.2-devel-ubuntu24.04) is compatible with the remote machine's kernel (5.15.0-170-generic) and NVIDIA driver. The Docker build environment may have different CUDA runtime libraries than what is installed on the remote machine.

Implicit assumption: The config file path (/tmp/cuzk-memtest-config.toml) is accessible and the daemon will be able to read it. The assistant uses /tmp/ which is world-readable on most Linux systems.

Implicit assumption: No other memory-intensive processes will interfere with the test. The assistant knows Curio is running (cordoned) but does not account for its memory footprint in the budget calculation.

Input Knowledge Required

To understand this message fully, one needs:

  1. The memory manager architecture: Knowledge of the new MemoryBudget, MemoryReservation, PceCache, and SrsManager components, and how they interact to control memory usage.
  2. The proof pipeline: Understanding that a 32 GiB PoRep proof requires SRS parameters (~44 GiB), a PCE (~26 GiB), and working memory per partition (~14 GiB), and that these numbers are not arbitrary but derived from the mathematical structure of the proof system.
  3. The deployment environment: The remote machine has 755 GiB RAM, an RTX 5090 with 32 GiB VRAM, 64 CPU cores, and is running Curio (cordoned). The SRS and PCE parameter files are already cached at /var/tmp/filecoin-proof-parameters/.
  4. The config format: The TOML configuration schema for cuzk, including the new [memory] section with total_budget, safety_margin, and eviction_min_idle fields.
  5. The previous conversation: The assistant had just committed the memory manager changes, built a Docker binary, uploaded it, and verified the remote machine's state in the preceding messages ([msg 2294] through [msg 2314]).

Output Knowledge Created

This message creates several pieces of knowledge:

  1. A test configuration that deliberately constrains memory to 100 GiB, designed to validate the memory manager's admission control logic.
  2. A documented hypothesis about expected behavior: approximately 2 partitions should be able to run concurrently given the budget arithmetic.
  3. A deployment artifact on the remote machine at /tmp/cuzk-memtest-config.toml that can be used to start the daemon with the new memory manager.
  4. A test plan: The assistant implicitly defines the success criteria—if the memory manager limits concurrency to ~2 partitions, the budget system is working. If it allows more, there is a bug.

The Thinking Process: A Window Into Debugging Methodology

The assistant's reasoning in this message reveals a sophisticated debugging methodology. Rather than simply deploying the new binary with the existing config and hoping it works, the assistant designs a controlled experiment with a specific, falsifiable hypothesis. This is the scientific method applied to systems engineering: define the expected behavior, create conditions that test it, observe the outcome, and iterate.

The preamble "Good, there's c1 test data" shows that the assistant first verified the necessary inputs exist before designing the experiment. The c1.json file (50 MB) is the circuit output that serves as input to the proving pipeline—without it, no test is possible.

The config comments serve a dual purpose: they document the reasoning for the human reader (or future self) and they serve as a real-time calculation of expected behavior. The arithmetic "SRS ~44 GiB + PCE ~26 GiB = 70 GiB baseline... ~30 GiB for working set = ~2 partitions at 14 GiB each" is essentially a proof that the config is internally consistent with the system's known memory requirements.

What Happened Next

The chunk summary reveals that this experiment did not go as planned. When the daemon was started with this tight budget, two problems emerged:

  1. A runtime panic: The evictor callback used blocking_lock() on a tokio Mutex from within an async context, causing a "Cannot block the current thread from within a runtime" panic at engine.rs:913. This was fixed by switching to try_lock().
  2. Concurrency starvation: With the 100 GiB budget, only one partition synthesized at a time because the 44 GiB SRS + 26 GiB PCE baseline left only ~30 GiB for working sets, and a race condition in SRS pre-acquisition caused three concurrent proofs to each reserve 44 GiB simultaneously, further starving the budget. When the budget was increased to auto (750 GiB) with a 5 GiB safety margin, all 30 partitions started within one second, but the daemon was then OOM-killed as RSS hit ~500 GiB while Curio and other processes consumed the remaining memory. The conclusion: the budget system works correctly but requires a properly sized safety margin (e.g., 250 GiB) to account for co-located processes on machines with significant background memory usage.

Conclusion

Message 2315 is a masterclass in deliberate testing. It is not a naive deployment but a carefully designed experiment with a clear hypothesis, controlled variables, and measurable outcomes. The assistant's reasoning reveals an understanding that a memory manager cannot be trusted until it has been seen to fail gracefully under pressure—that the true test of a constraint system is not that it works when resources are abundant, but that it correctly limits work when resources are scarce. The fact that the experiment revealed real bugs (the async-blocking panic, the SRS reservation race) and real insights (the need for a larger safety margin) validates the methodology. This message is the pivot point between theory and practice, where architectural design meets the messy reality of production deployment.