The Quiet Kill: How a Simple pkill Command Revealed the Limits of Parallelism in GPU-Accelerated SNARK Proving

In the middle of a systematic performance sweep for the Phase 8 dual-worker GPU interlock, a brief, almost invisible message appears:

[bash] pkill -f cuzk-daemon 2>/dev/null; sleep 2; echo "killed"

At first glance, this is nothing more than an operational housekeeping step—killing a daemon process to restart it with a new configuration. But in the context of the broader optimization journey documented across the cuzk SNARK proving engine, this single command marks a critical inflection point: the moment when the assistant pivots from celebrating a successful optimization to stress-testing its boundaries. The user had just asked, "Try with config partition_workers = 30" ([msg 2233]), and this kill command is the first step in executing that request. What follows is a benchmark that reveals the fundamental tension between CPU synthesis parallelism and GPU utilization—a tension that ultimately defines the optimal operating point for the entire proving pipeline.

The Context: Phase 8 and the Partition Workers Sweep

To understand why this kill command matters, we must step back and examine the optimization arc that led to it. The cuzk SNARK proving engine generates Groth16 proofs for Filecoin's Proof-of-Replication (PoRep) protocol—a computationally intensive task that involves synthesizing circuit constraints across multiple partitions and then performing GPU-accelerated NTT (Number Theoretic Transform) and MSM (Multi-Scalar Multiplication) operations. Earlier phases had identified a critical bottleneck: the C++ static mutex in generate_groth16_proofs_c was locking across the entire GPU operation, including CPU preprocessing work that could run concurrently. Phase 8 introduced a "dual-worker GPU interlock" that narrowed the mutex scope to cover only the CUDA kernel region (NTT+MSM, batch additions, tail MSMs), allowing two GPU workers per device to interleave their execution—one worker runs CPU preprocessing while the other occupies the GPU with CUDA kernels.

The results were impressive. Single-proof GPU efficiency hit 100.0%—zero idle gaps between partitions—and multi-proof throughput improved 13.2% (44.0s/proof vs 50.7s for Phase 7 at concurrency=5, parallelism=3). The assistant had identified partition_workers=20 as the sweet spot for the 96-core test machine. But the user, demonstrating a healthy scientific skepticism, wanted to push further: "Try with config partition_workers = 30" ([msg 2233]).

The Kill: A Deliberate Operational Pattern

The assistant's response to this request follows a well-established operational pattern that has been refined across dozens of benchmark iterations in this session. The pattern has three phases: kill the existing daemon, update the configuration, start a new daemon. The subject message executes phase one.

The command itself is carefully constructed. pkill -f cuzk-daemon uses the -f flag to match against the full process command line, ensuring it catches the daemon even if it was launched with arguments or from a different working directory. The 2>/dev/null redirect suppresses error output—a pragmatic choice, since pkill will print an error if no matching process is found, and in this context the assistant knows a daemon is running (it had just completed benchmarks in [msg 2232]). The sleep 2 introduces a deliberate two-second pause, giving the operating system time to release resources held by the dying process: GPU memory allocations, CUDA context handles, file descriptors, and network sockets. Without this pause, a rapid restart could fail with "address already in use" errors on the daemon's listening port, or encounter GPU memory conflicts. Finally, echo "killed" provides a clear, parseable status signal that the assistant can verify in the subsequent message.

This pattern reveals an important assumption: that killing the daemon is safe. The assistant assumes no in-flight proofs are being processed—a reasonable assumption given that the previous benchmark had completed and the daemon was idle. It also assumes that pkill -f will uniquely identify the correct process. On a production server running multiple services, -f matching could theoretically catch unrelated processes whose command lines contain "cuzk-daemon" as a substring, but in this controlled benchmark environment, the risk is minimal.

What the Kill Enabled: The pw=30 Experiment

With the old daemon terminated, the assistant proceeds to start a new one with partition_workers=30 ([msg 2236]) and runs the standard 5-proof batch benchmark at concurrency=3 ([msg 2238]). The results are revealing:

60.4s/proof — significantly worse than pw=20 (44.0s/proof).

The first proof in the batch took 140 seconds of prove time—nearly double the normal duration. With 30 concurrent partition workers, the CPU synthesis threads are competing aggressively for the 96 hardware cores. The assistant's analysis correctly identifies the root cause: "30 partitions being synthesized simultaneously across 3 sectors competing hard for CPU and memory" ([msg 2239]). The GPU timeline confirms the diagnosis—partition GPU times balloon to 22 seconds (vs ~6.5s at pw=20), indicating that CPU preprocessing work is starving the GPU workers, delaying the moment when each partition's data is ready for CUDA kernel execution.

This is the classic "too many cooks" phenomenon in parallel systems. The Phase 8 dual-worker interlock had successfully eliminated GPU idle gaps by overlapping CPU preprocessing with GPU computation. But increasing partition_workers from 20 to 30 introduces a new bottleneck: CPU contention. The synthesis threads—which prepare circuit constraints, compute a/b/c vectors, and perform other CPU-bound work before handing data to the GPU—now compete so fiercely for cores that the GPU preprocessing pipeline starves. The GPU, which was running at 100% efficiency at pw=20, now sits idle waiting for CPU work to complete.

The Deeper Lesson: Finding the Optimal Operating Point

The pw=30 experiment, enabled by this simple kill command, completes the empirical picture of the Phase 8 optimization. The sweep across partition_workers values (10, 12, 15, 18, 20, 30) reveals a clear pattern:

Input Knowledge and Output Knowledge

The subject message draws on several pieces of input knowledge: the daemon process name ("cuzk-daemon"), the location of the configuration file (just updated in [msg 2234]), the operational state of the system (daemon is running and idle), and the standard restart procedure established through prior benchmarks. It also relies on the assistant's understanding of Unix process management—that pkill -f will match the process, that sleep 2 provides adequate cleanup time, and that the daemon's listening port will be released promptly.

The output knowledge created by this message is deceptively simple: the daemon process is terminated, and the system is ready for a fresh start. But this termination is not an end in itself—it is the enabling condition for the pw=30 experiment that follows. The real knowledge generated by this entire sequence is the empirical confirmation that partition_workers=30 degrades performance, and the deeper insight that the Phase 8 dual-worker interlock, while eliminating GPU idle gaps, has shifted the bottleneck to CPU synthesis contention. This finding directly informs the next optimization frontier: reducing CPU synthesis overhead through techniques like Phase 5 Wave 2/3 optimizations, which target the CPU hotpaths in circuit synthesis.

Conclusion

A single pkill command, buried in a long conversation about GPU interlocking, mutex scoping, and CUDA kernel optimization, is easy to overlook. But it represents a crucial moment in the scientific process of performance engineering: the willingness to test a hypothesis that might fail. The assistant could have declared pw=20 the winner and moved on. Instead, it executed the user's request to push to pw=30, knowing the result might be negative. The kill command is the operational enabler of that scientific curiosity—a small, precise action that clears the way for new data. And the data it unlocks—the sharp regression at pw=30—provides the clearest possible evidence that the optimal operating point has been found, not just guessed at.