The Post-Restart Warmup: A Tactical Workaround for gRPC Timeout Failures in Distributed GPU Proving

Message Overview

In a single, deceptively brief message, the assistant executed a targeted edit to the benchmark.sh script that added a "post-restart warmup" proof between the daemon restart and the timed benchmark batch. The full message reads:

I'll add a "post-restart warmup" proof after the daemon restart, before the benchmark batch. This single proof will warm up the GPU kernels with the full partition_workers, and if it fails (broken pipe), we retry: [edit] /tmp/czk/docker/cuzk/benchmark.sh Edit applied successfully.

This message represents a critical moment in a multi-day debugging odyssey. It sits at the intersection of two simultaneous failures—one caused by a too-short timeout in the lifecycle manager, the other by a silent gRPC connection drop in the proving client—and embodies the kind of tactical, workaround-driven engineering that emerges when deep architectural fixes are impractical. To understand why this message was written, what reasoning it encodes, and why it ultimately proved insufficient, we must reconstruct the investigative trail that led to it.

The Two Failures: Belgium and Czechia

The immediate context for this message is the simultaneous collapse of two benchmark instances that had been running on Vast.ai rented GPU hardware. The Belgium instance (2× NVIDIA A40, 2 TB RAM) had been killed by the vast-manager's lifecycle monitor after exceeding a 20-minute timeout. The Czechia instance (2× RTX 3090, 251 GB RAM) had suffered a different fate: its benchmark batch failed with a gRPC "transport error" on the very first proof.

The assistant had already addressed the Belgium failure in the preceding messages ([msg 1159]–[msg 1162]) by increasing the benchmark timeout from 20 to 45 minutes in the vast-manager's Go source code, rebuilding the binary, and redeploying it to the controller host. That fix was straightforward—a constant change in a monitor loop—and was already in flight when the Czechia failure demanded attention.

The Czechia failure was more subtle. The assistant's investigation ([msg 1164]–[msg 1167]) revealed a puzzling picture: the warmup phase had completed successfully in 359 seconds using partition_workers=2, the PCE (Pre-Compiled Constraint Evaluator) cache had been generated, the daemon had been restarted with the full partition_workers=10, and then—immediately upon starting the 12-proof batch benchmark with concurrency=2—the client received a broken pipe error. Yet the daemon logs showed it was alive and processing: both proofs had been accepted, all 10 partitions per proof were synthesizing via the PCE fast path, and the scheduler was functioning normally.

The disconnect was critical: the daemon was working, but the client was timing out.

Root Cause Diagnosis: The gRPC Timeout

The assistant correctly identified the root cause. The gRPC library used by the Rust-based cuzk-bench client (tonic, a gRPC implementation built on top of Tokio) has a default timeout for RPC calls. When the daemon is freshly restarted with full partition workers, the GPU kernels for the new configuration must be compiled and cached—a process that can take several minutes. With concurrency=2 and partition_workers=10, the system was attempting to synthesize 20 partitions simultaneously (10 per proof × 2 proofs), and the first proof to complete still took longer than the gRPC client's timeout window.

The warmup proof that had succeeded earlier used only partition_workers=2, so it completed quickly and never triggered the timeout. But after the daemon restart with full workers, the first batch proof faced the full compilation overhead and the client gave up waiting.

The assistant then systematically explored whether the timeout was configurable ([msg 1170]–[msg 1176]). It tried querying the cuzk-bench binary on a local host, then on the controller, then via Docker. The batch --help output confirmed there was no --timeout flag. The timeout was baked into the Rust library itself—not something that could be patched from the shell.

The Decision: Work Around, Don't Fix

At this point, the assistant faced a fork. One path would be to modify the Rust source code of cuzk-bench or the gRPC server to increase or eliminate the timeout. The other path was to work around the limitation at the shell script level.

The assistant chose the workaround, and the reasoning is implicit in the message's structure. The warmup proof had already demonstrated that a single proof with reduced partition workers could complete successfully. But the problem wasn't the warmup—it was the first proof after the daemon restart with full workers. The solution was to insert another warmup proof after the restart, this time using the full partition_workers count, so that the GPU kernels would be compiled and cached before the timed batch began.

The message's phrasing reveals the key insight: "This single proof will warm up the GPU kernels with the full partition_workers." The assistant understood that the kernel compilation overhead was a one-time cost—once the kernels were cached, subsequent proofs would complete within the timeout. By running a single sacrificial proof before the batch, the system would absorb that one-time cost in a context where failure was acceptable (the script would retry).

Assumptions Embedded in the Fix

The post-restart warmup fix rested on several assumptions, some explicit and some implicit:

  1. The gRPC timeout is fixed and not configurable via CLI. This was validated by the assistant's investigation of the cuzk-bench --help output.
  2. A single proof with full partition workers will be sufficient to warm up the GPU kernels. This assumed that kernel compilation is triggered by the first use of a given configuration, not by some deeper condition.
  3. The retry logic will handle transient failures. The message states "if it fails (broken pipe), we retry," implying that a single retry would be sufficient—that the failure was a one-time phenomenon, not a persistent condition.
  4. The post-restart warmup will not itself cause an OOM. This was a critical implicit assumption. The warmup proof with partition_workers=2 had succeeded, but the post-restart warmup would use the full partition count. On a machine with 251 GB RAM (Czechia), 10 partition workers for a single proof might still be safe—but on the 125 GB machine that had originally OOM'd, this could be risky.
  5. The warmup proof's completion time will be within the gRPC timeout. This was the core bet: that the kernel compilation overhead, while significant, would not exceed the client's patience for a single proof.

What the Fix Actually Changed

The edit to benchmark.sh inserted a new code block between the daemon restart section and the batch benchmark invocation. The script already had retry logic for the initial warmup (the one with reduced partition workers); the post-restart warmup extended the same pattern to the post-restart phase. The flow became:

  1. Start daemon with partition_workers=2 (or detected safe value)
  2. Run warmup proof → generate PCE cache
  3. Restart daemon with full partition_workers
  4. NEW: Run post-restart warmup proof with full workers → compile GPU kernels
  5. Run batch benchmark (12 proofs, timed) The edit was validated with bash -n syntax checking ([msg 1178]), the Docker image was rebuilt and pushed to Docker Hub ([msg 1179]), and a new Czechia instance was created with the updated image ([msg 1180]).

Why the Fix Ultimately Failed

The chunk summary reveals that the post-restart warmup did not solve the problem. A new Czechia instance "crashed with a bench_rate of 0, likely due to an OOM crash during the post-restart warmup or batch benchmark." This suggests that the assistant's assumption about OOM safety was incorrect: running a single proof with full partition workers on a machine with limited RAM could still trigger an out-of-memory kill, especially if the kernel compilation itself required additional memory.

More broadly, the post-restart warmup was a tactical patch on a system that was revealing deeper unreliability. The Belgium instance with 2 TB RAM and 2× A40 GPUs achieved only 35.9 proofs/hour—below the 50 proofs/hour minimum—while a single RTX 4090 had previously achieved 41.32 proofs/hour. Hardware specs were not predicting real-world performance, and the workaround approach was reaching its limits.

The Strategic Pivot

The failure of the post-restart warmup fix, combined with the persistent unreliability of hardcoded thresholds, triggered a fundamental strategic shift in the session. The assistant abandoned the approach of tuning constants and workarounds and instead began building a data-driven experimental system: a host_perf database table to track benchmark results per host, an API to search Vast.ai offers filtered by GPU/RAM/price while overlaying known host performance, a deploy endpoint, and foundational UI code. The minimum proofs/hour rate was made configurable. The system would learn which hardware configurations actually worked, rather than relying on assumptions.

Conclusion

Message [msg 1177] is a perfect case study of workaround-driven engineering in distributed systems. It was born from a careful diagnosis of a gRPC timeout failure, shaped by the constraints of a system where the root cause (a baked-in timeout in a Rust library) was not easily addressable, and executed with the pragmatic goal of getting the benchmark pipeline working. Its failure revealed the limits of the workaround approach and catalyzed a more fundamental shift toward data-driven hardware discovery. The message itself is brief—a single sentence of intent followed by an edit confirmation—but the reasoning it encodes spans dozens of preceding messages of investigation, diagnosis, and constraint discovery. It is a moment where engineering judgment, constrained by time and architecture, chose the expedient path, and where the failure of that choice opened the door to a better system design.