The Concurrency Correction: A Calculated Rebuild Decision in the OOM Wars

Introduction

In the high-stakes world of GPU proving on rented cloud infrastructure, memory is the invisible enemy. Message [msg 1142] captures a pivotal moment in a multi-session debugging saga where an AI assistant, having just corrected a flawed memory estimation formula, faces a tactical decision: let already-running instances proceed with a dangerously optimistic concurrency setting, or tear them down and redeploy with a safer configuration. This message, outwardly a simple Docker rebuild-and-push command, is actually the culmination of a deep investigative process into why GPU instances were crashing with Out-of-Memory (OOM) errors during benchmark proving. It represents the moment where theoretical modeling meets operational reality, and where the assistant chooses prudence over speed.

The Context: A System Plagued by OOM

To understand message [msg 1142], one must understand the war that preceded it. The assistant was building a distributed proving system for Filecoin's Curio/cuzk protocol, deployed on Vast.ai GPU instances. The system's lifecycle was: provision an instance → download proving parameters → run a benchmark → report results → enter supervisor mode. But instances kept dying.

The root cause was memory pressure during the benchmark phase. Each proof required multiple "partition workers" to synthesize circuit partitions, and each worker consumed significant RAM. When multiple proofs ran concurrently, the memory demand could exceed available RAM, triggering the Linux OOM killer. The assistant had already fixed the most acute problem—the warmup phase where PCE extraction caused a memory spike—by using fewer partition workers during warmup. But steady-state benchmark concurrency remained dangerous.

The entrypoint script (entrypoint.sh) had been refactored in <msg id=1124-1125> to auto-scale concurrency based on available RAM and GPU count. The formula was:

available_ram = total_ram - 100GB (overhead)
per_proof_memory = partition_workers * 3GB
max_concurrency_by_ram = available_ram / per_proof_memory
max_concurrency_by_gpu = num_gpus * 3
concurrency = min(max_concurrency_by_ram, max_concurrency_by_gpu)

This formula assumed each partition worker consumed ~3GB per proof. But real-world data from a US-based instance (376GB RAM, 2x RTX 3090) told a different story: that machine OOM'd with concurrency=5 and partition_workers=10, even though the formula predicted it could handle 9 concurrent proofs. The discrepancy meant the per-worker memory estimate was fundamentally wrong.

The Epiphany: Recalculating Memory Demand

In [msg 1140], the assistant performed a critical post-mortem analysis. If 5 concurrent proofs × 10 partition workers each could OOM a 376GB machine, then each proof was consuming far more than 30GB. The math suggested 50-60GB per proof (10 workers × 5-6GB each). With 5 concurrent proofs demanding 250-300GB plus 100GB overhead, the system was right at the edge of 376GB—and the OOM killer tipped it over.

The fix was to increase the per-worker multiplier from 3GB to 6GB. This single change dramatically reduced safe concurrency estimates:

| Machine | Old Concurrency | New Concurrency | |---------|----------------|-----------------| | 251GB, 2 GPUs, pw=10 | 5 | 2 | | 376GB, 2 GPUs, pw=10 | 6 | 4 | | 500GB, 1 GPU, pw=16 | 3 | 3 | | 1001GB, 2 GPUs, pw=10 | 6 | 6 |

For the 251GB Czechia machine, concurrency dropped from 5 to 2—a 60% reduction that would halve memory pressure during benchmarks.

The Decision Fork: Rebuild or Let Run

Message [msg 1142] opens with the assistant facing a concrete operational decision. Two instances were already deployed:

  1. Czechia (32713896): 2× RTX 3090, 251GB RAM, already downloading parameters with the old entrypoint that would set concurrency=5
  2. Belgium (32713902): 2× A40, 1TB RAM, still pulling the Docker image, not yet running The assistant considers two paths: Path A — Rebuild and recreate: Destroy both instances, rebuild the Docker image with the corrected formula, push it, and create fresh instances. This guarantees safety but costs time—parameter downloads (already partially complete for Czechia) would restart from scratch. Path B — Let them run: The Czechia instance might survive because concurrency=5 only applies during the benchmark phase, not during warmup (which already had its own OOM fix). The Belgium instance might pick up the new image automatically since it hadn't pulled yet. But this gambles on the Czechia machine not OOM'ing. The assistant's reasoning reveals a sophisticated cost-benefit analysis. The Czechia instance was still downloading parameters (ETA 25 minutes), so it hadn't even reached the benchmark phase yet. The Belgium instance hadn't started at all. The window for intervention was still open.

The Decision: Prudence Over Speed

The assistant makes its choice: "Let me just rebuild, push, and recreate both instances to be safe. The param download will restart but it's better than wasting time on OOM failures."

This is a decision rooted in operational wisdom. The cost of a failed benchmark is not just the lost time—it's the entire instance rental cost, the manager's auto-destroy lifecycle (which would kill the instance and mark it as failed), and the cognitive overhead of debugging yet another failure. By contrast, the cost of rebuilding and recreating is a one-time delay of ~25 minutes for parameter downloads. The assistant correctly identifies that the expected value of letting the instances run is negative: even a moderate probability of OOM failure outweighs the guaranteed delay of redeployment.

This reasoning also reflects a deeper understanding of the system's lifecycle. The manager's handleBenchDone endpoint (implemented in earlier messages) automatically destroys instances that fail benchmarks. A concurrency=5 OOM on the Czechia machine would trigger this lifecycle, wasting the instance cost and requiring manual re-provisioning anyway. Better to proactively recreate than reactively recover.

The Execution: Docker Build and Push

Having made the decision, the assistant executes swiftly. The Docker build command runs with tail -5 to see only the final lines of output, confirming the image layers were exported successfully and the image was tagged. The push command shows layer reuse (most layers "already exists" from previous pushes) and only the changed layer being uploaded. The new digest sha256:55afb8fdbfe22607f4e6da5c31752cc93812c3b37337ee10efc46a2eeb82025f confirms the image was updated.

The efficiency of the Docker build is notable: the entire build took ~1 second for exporting layers, and the push reused all but one layer. This is because only the entrypoint.sh file changed—the rest of the image layers were cached. The assistant's iterative development approach (build, test, rebuild) was paying off in fast iteration cycles.

Assumptions and Their Validity

Several assumptions underpin this message:

Assumption 1: The 6GB/worker multiplier is correct. The assistant derived this from a single data point (the US instance OOM with concurrency=5, pw=10, 376GB RAM). While this is better than the original 3GB estimate, it's still an approximation. Different proof types (PoRep vs WindowPoSt) or different GPU architectures might have different memory footprints. The assistant implicitly acknowledges this by using a round number (6GB) rather than a precise calculation.

Assumption 2: Memory scales linearly with partition workers. The formula per_proof = partition_workers × 6GB assumes each additional worker adds exactly 6GB of memory. In reality, there are fixed costs per proof (circuit data, SRS parameters) that don't scale with worker count, and shared resources (PCE cache, GPU memory) that are reused across proofs. The linear model is a simplification.

Assumption 3: The 100GB overhead is sufficient. The formula reserves 100GB for the OS, SRS parameters (44GB), PCE cache (26GB), and other overhead. This leaves ~70GB of headroom on a 251GB machine after accounting for 2 concurrent proofs × 60GB = 120GB. Whether 70GB is enough depends on the actual memory fragmentation and allocation patterns of the proving software.

Assumption 4: Recreating instances is the safest path. This assumes that the new image will be pulled correctly, that the instances will start up without issues, and that the parameter download will complete successfully. Each of these steps has its own failure modes (network issues, disk space, Docker pull errors), but the assistant judges them less likely than an OOM failure.

Mistakes and Incorrect Assumptions

The most significant mistake in this message is not in what the assistant does, but in what it doesn't do: it doesn't validate the new concurrency formula against the Belgium machine's specs before deploying. The Belgium machine has 1TB RAM and 2× A40 GPUs. With pw=10, the new formula gives concurrency=6 (capped by GPU cap). But the A40 has 48GB VRAM—significantly more than the RTX 3090's 24GB. Different GPUs may have different proving throughput, which affects how many concurrent proofs are beneficial. The assistant assumes the GPU cap of num_gpus × 3 is universally correct, but this was calibrated for RTX 3090s.

Another subtle issue: the assistant doesn't consider that the Czechia instance's 251GB RAM might be a container limit rather than the host's total RAM. Earlier in the conversation ([msg 1127]), the assistant noted that the vast API showed cpu_ram: 257560 (251GB) for the BC Canada host, but /proc/meminfo showed 376GB for the US host. The discrepancy between advertised RAM and actual container-visible RAM is a known issue with Vast.ai. If the Czechia container actually has more RAM than 251GB, the conservative concurrency=2 might be unnecessarily restrictive.

The assistant also assumes that the Belgium instance hasn't pulled the old image yet. This is based on the SSH connection returning empty in [msg 1138]. But the empty response could also mean the container was running but the entrypoint hadn't started logging yet, or the SSH daemon wasn't ready. The assistant doesn't verify this assumption before deciding to rebuild.

Input Knowledge Required

To fully understand this message, one needs:

  1. The architecture of the proving system: How partition workers, concurrency, and benchmark phases interact. That each proof requires multiple partition workers to synthesize circuit partitions, and these workers run in parallel within a single proof process.
  2. The Vast.ai instance lifecycle: How instances are provisioned, how Docker images are pulled, how SSH access works, and how the --onstart-cmd mechanism triggers the entrypoint script.
  3. The manager's lifecycle management: The handleBenchDone endpoint that auto-destroys underperforming instances, and the registered → benchmarking → supervisor state machine.
  4. Docker build mechanics: Layer caching, image tagging, and push semantics. Understanding why a single-file change results in fast rebuilds.
  5. Linux OOM behavior: How the kernel selects processes to kill when memory is exhausted, and why this manifests as a process crash rather than a clean error.
  6. Memory estimation for cryptographic proving: The relationship between partition count, worker threads, and memory consumption in GPU-based proof synthesis.

Output Knowledge Created

This message produces several concrete outputs:

  1. A new Docker image (theuser/curio-cuzk:latest with digest sha256:55afb8fdbfe22607f4e6da5c31752cc93812c3b37337ee10efc46a2eeb82025f) containing the corrected concurrency formula.
  2. A decision record: The assistant's reasoning about why recreating instances is preferable to letting them run, which serves as documentation for future operational decisions.
  3. An implicit test case: The recreated instances will serve as a validation of the new 6GB/worker formula. If they survive the benchmark without OOM, the formula is confirmed. If they OOM, further adjustment is needed.
  4. A refined mental model: The assistant's understanding of memory consumption in the proving pipeline has been updated from "~3GB per partition worker" to "~5-6GB per partition worker," a 2× correction that reflects the gap between theoretical and observed memory usage.

The Thinking Process: A Window into Operational Reasoning

The assistant's chain of thought in this message reveals a sophisticated operational reasoning process:

  1. Situation assessment: "The two instances are already running with the old concurrency formula. The Czechia one will use concurrency=5 (which might OOM), and Belgium hasn't started yet."
  2. Option generation: Two clear paths—rebuild and recreate, or let run and observe.
  3. Risk analysis: The assistant weighs the probability of OOM (high for Czechia with concurrency=5 on 251GB) against the cost of redeployment (parameter download restart, ~25 minutes).
  4. Temporal consideration: "Czechia instance is still downloading params (ETA 25min). The Belgium one hasn't started yet." The window for intervention is still open.
  5. Decision with justification: "Let me just rebuild, push, and recreate both instances to be safe. The param download will restart but it's better than wasting time on OOM failures."
  6. Execution: Swift Docker build and push, with output monitoring to confirm success. This is textbook operational decision-making under uncertainty. The assistant doesn't have perfect information about whether the Czechia instance will OOM, but it has enough evidence to make a probabilistic judgment. The key insight is that the cost of being wrong about "let it run" (a crashed instance, wasted rental cost, and debugging time) is much higher than the cost of being wrong about "rebuild" (a 25-minute download delay).

Broader Significance

Message [msg 1142] is a microcosm of the entire segment's theme: the tension between theoretical modeling and empirical reality. The assistant built a mathematical model of memory consumption, tested it against real-world data, found it deficient, corrected it, and then made an operational decision based on the corrected model. This cycle—model, test, correct, deploy—is the essence of engineering complex systems.

The message also illustrates a critical principle of distributed systems operations: when in doubt, reset to a known good state. Rather than trying to patch a running system (which would require SSH access to the container, editing files, and restarting processes—all fragile operations), the assistant chooses to rebuild from scratch with the corrected configuration. This is the container philosophy in action: immutable infrastructure, disposable instances, and clean redeployment.

Finally, the message shows the value of fast iteration cycles. The Docker build took ~1 second and the push reused cached layers. This speed made the "rebuild and recreate" option feasible. If each rebuild took 30 minutes, the assistant might have chosen differently. The investment in a well-structured Docker build pipeline (with layer caching, multi-stage builds, and efficient dependency management) paid off in operational flexibility.

Conclusion

Message [msg 1142] is deceptively simple—a Docker build and push command—but it represents the culmination of a deep investigative process into memory management in GPU proving systems. The assistant's decision to proactively recreate instances rather than let them run with a dangerous configuration demonstrates operational maturity: knowing when to cut losses and redeploy rather than gamble on marginal gains. The corrected concurrency formula, while still an approximation, is grounded in empirical observation rather than theoretical guesswork. This message marks the transition from reactive debugging (fixing OOMs as they occur) to proactive prevention (configuring systems to avoid OOMs in the first place)—a crucial step in building reliable distributed proving infrastructure.