The Edit That Made Concurrency Dynamic: How One Message Fixed OOM Crashes in a Distributed Proving System

The Message

The subject message is deceptively brief — just a single line of commentary followed by a tool invocation:

Now replace the RAM detection section to also detect GPUs and set concurrency: [edit] /tmp/czk/docker/cuzk/entrypoint.sh Edit applied successfully.

This is message index 1125 in the conversation, and on its surface it appears to be a routine code edit. But this message represents the culmination of a long debugging chain that stretched across multiple GPU instances, continents, and days of troubleshooting. It is the moment where the assistant finally identified the root cause of persistent Out-of-Memory (OOM) crashes and applied a fix that transformed the system from a brittle, hardcoded configuration into a self-adapting, hardware-aware pipeline.

The Crisis That Led Here

To understand why this message was written, we must trace the events that preceded it. The session was building a distributed Filecoin proving system using custom CUDA-accelerated proving (cuzk) running on rented GPU instances from Vast.ai. The system had already survived several crises: a crash caused by mismatched is_extensible() flags in constraint systems (segment 0), multi-GPU threading bugs (segment 3), Docker build blockers (segments 4-5), and a full management dashboard implementation (segment 6).

But the most stubborn problem emerged in segment 8: OOM kills. The proving daemon (cuzk) was being killed by the Linux kernel's OOM killer on instances with insufficient RAM. The first victim was a BC Canada instance with 125GB RAM that died during the warmup phase — the initial Pre-Compiled Constraint Evaluator (PCE) extraction. The assistant fixed that by modifying benchmark.sh to detect the absence of a PCE cache and start the daemon with partition_workers=2 for the warmup proof, preventing the memory spike of simultaneous partition synthesis (<msg id=1106-1114>).

This fix worked beautifully. A US instance with 2× RTX 3090 and 376GB RAM successfully completed its warmup, generated the 26GB PCE file, and restarted the daemon with the full partition_workers=10 for the actual benchmark. But then disaster struck again: the benchmark process was killed by OOM during the batch run ([msg 1115]).

The Diagnostic Chain

The assistant's reasoning in the messages immediately preceding message 1125 reveals a careful diagnostic process. At [msg 1122], the assistant examined the dashboard and found the US instance had been killed with bench_rate: 0.0 — the benchmark script had failed completely. The auto-destroy lifecycle code (which the assistant had just implemented) correctly killed the instance, but the fundamental problem remained.

The assistant then performed a critical piece of reasoning:

The problem: 5 concurrent proofs x 10 partition workers = too much memory, even with 376GB RAM. The issue isn't just the warmup/PCE extraction — it's also the steady-state operation with high concurrency.

This was the key insight. The warmup fix had addressed the PCE extraction spike, but the actual benchmark was running 5 proofs concurrently, each using 10 partition workers. Each worker synthesizes a partition that uses significant RAM. The assistant estimated each proof at 30-40GB of memory demand, meaning 5 concurrent proofs needed 150-200GB just for synthesis, plus the 44GB SRS file, 26GB PCE cache, and OS overhead — well over the 376GB available.

The assistant considered three options at [msg 1122]:

  1. Reduce concurrency for the benchmark (e.g., 2 instead of 5)
  2. Reduce partition workers further
  3. Accept that 2× RTX 3090 with 376GB needs lower settings The chosen approach was to make concurrency dynamic, scaling it based on both GPU count and available RAM.

What the Edit Actually Changed

The edit modified the RAM detection section of /tmp/czk/docker/cuzk/entrypoint.sh. The previous code (visible at [msg 1123]) had a section starting at line 148 that detected total RAM from /proc/meminfo and set PARTITION_WORKERS based on whether RAM was below 400GB. But BENCH_CONCURRENCY was hardcoded to 5 at line 19.

The edit replaced this section to:

  1. Detect GPU count using nvidia-smi --query-gpu=count --format=csv,noheader or similar
  2. Calculate safe concurrency based on available memory, subtracting 100GB overhead, estimating per-proof memory as partition_workers × 4GB (later adjusted to × 3GB at [msg 1127]), and capping by GPU count
  3. Set both PARTITION_WORKERS and BENCH_CONCURRENCY dynamically The assistant verified the math at [msg 1127]: - 376GB RAM, 2 GPUs, partition_workers=10: Available = 276GB, per_proof = 30GB, max_conc = 9, gpu_cap = 6 → concurrency=6 - 500GB RAM, 1 GPU, partition_workers=16: Available = 400GB, per_proof = 48GB, max_conc = 8, gpu_cap = 3 → concurrency=3

Assumptions and Potential Mistakes

The edit rested on several assumptions, some of which proved problematic:

1. Memory estimation accuracy. The assistant assumed each partition worker consumes approximately 3-4GB of RAM per proof. This was a rough estimate based on observed behavior, not a precise measurement. The actual memory footprint depends on proof parameters (32GiB sector size), GPU architecture, and synthesis complexity. If the estimate was too low, OOMs could still occur; if too high, the system would underutilize available memory.

2. The 100GB overhead buffer. The assistant reserved 100GB for the SRS file (44GB), PCE cache (26GB), OS, and daemon overhead. This was generous but necessary — the SRS file alone is mmap'd and can consume significant virtual memory. However, on machines with tight memory budgets (e.g., 125GB), this overhead left very little room for actual proving work.

3. GPU count detection reliability. The edit assumed nvidia-smi would be available in the container and would return accurate GPU counts. In Docker containers, GPU visibility depends on proper NVIDIA driver installation and the --gpus flag. If the container didn't have GPU access, the detection would fail, potentially defaulting to unsafe settings.

4. The vast API's misleading cpu_ram field. The assistant discovered at [msg 1109] that the vast API reported cpu_ram: 76800 (75GB) for a machine that actually had 376GB RAM. The API was reporting per-GPU-fraction, not total system RAM. The entrypoint reads /proc/meminfo directly, which gives the container-visible memory, but this discrepancy could cause confusion when comparing API data to actual hardware.

5. Concurrency cap by GPU count. The formula gpu_cap = num_gpus * 3 assumed each GPU can handle 3 concurrent proofs. This is reasonable for modern GPUs with sufficient VRAM, but doesn't account for VRAM constraints separately from system RAM. A GPU with 24GB VRAM might handle 2 proofs (12GB each) but struggle with 3.

Input Knowledge Required

To understand this message, one needs:

Output Knowledge Created

This message produced:

  1. A dynamically-configured entrypoint that adapts to the hardware it runs on, replacing hardcoded values with formulas based on detected RAM and GPU count.
  2. A reproducible fix for OOM crashes that could be applied to any instance, regardless of its hardware configuration.
  3. A template for hardware-aware configuration that could be extended to other parameters (e.g., GPU threads, pipeline mode).
  4. Validation data showing the fix worked: subsequent instances (Czechia with 2× RTX 3090, 251GB RAM; Belgium with 2× A40, 2TB RAM) correctly auto-configured and ran without OOM crashes ([chunk 8.0]).

The Thinking Process Visible in the Reasoning

The assistant's reasoning in the messages leading up to 1125 reveals a structured problem-solving approach:

Step 1: Observe the failure. The US instance was killed during the benchmark, not during warmup. The warmup fix worked, but the steady-state benchmark still OOM'd.

Step 2: Quantify the problem. 5 concurrent proofs × 10 partition workers = 50 simultaneous synthesis threads. Each thread uses significant memory. The assistant estimated 30-40GB per proof.

Step 3: Identify the root cause. The hardcoded BENCH_CONCURRENCY=5 didn't account for available memory or GPU count. It was set once and never revisited.

Step 4: Design the fix. Replace the hardcoded value with a formula: available_ram = total_ram - 100GB_overhead, per_proof_memory = partition_workers × 4GB, max_concurrency = available_ram / per_proof_memory, capped by gpu_count × 3.

Step 5: Verify the math. The assistant manually computed expected values for the known machines (376GB, 125GB, 500GB) to ensure the formula produced reasonable results.

Step 6: Iterate. After initial implementation, the assistant adjusted the per-worker memory estimate from 4GB to 3GB to be less conservative ([msg 1127]), noting that the 125GB case gave concurrency=1 which was too restrictive.

Step 7: Apply and test. The edit was applied, syntax-checked, and the Docker image was rebuilt and pushed (<msg id=1129-1130>).

The Broader Significance

This message is a turning point in the session. Before it, the system relied on hardcoded configuration values that worked for the developer's test machines but failed in the wild diversity of Vast.ai's GPU marketplace. After it, the system became self-adapting — able to run on anything from a single RTX 4090 with 500GB RAM to dual A40s with 2TB RAM, automatically selecting safe concurrency and partition worker counts.

The fix also completed the OOM saga that had dominated segment 8. The warmup fix (partition_workers=2 during PCE extraction) addressed the transient spike. The concurrency fix (dynamic scaling based on RAM and GPUs) addressed the steady-state overload. Together, they transformed the system from one that crashed on most instances to one that could reliably benchmark and prove on any hardware.

The assistant's approach here — diagnose, quantify, design, iterate, deploy — exemplifies the kind of systematic debugging that distributed systems demand. And the message itself, for all its brevity, captures the moment when understanding crystallized into action.