The Config That Speaks Volumes: Probing GPU Parallelism Limits in the cuzk Proving Engine

Message Overview

In this brief but consequential message, the assistant responds to a user suggestion by generating two TOML configuration files for the cuzk-daemon, each specifying a different number of GPU workers per device. The configs are written to /tmp/cuzk-p11-gw3.toml and /tmp/cuzk-p11-gw4.toml, setting gpu_workers_per_device to 3 and 4 respectively, while keeping all other parameters identical: gpu_threads = 32, partition_workers = 10, and SRS preload for porep-32g.

[assistant] [bash] cat > /tmp/cuzk-p11-gw3.toml << 'EOF'
[daemon]
listen = "0.0.0.0:9820"
[srs]
param_cache = "/data/zk/params"
preload = ["porep-32g"]
[synthesis]
partition_workers = 10
[gpus]
gpu_workers_per_device = 3
gpu_threads = 32
EOF

cat > /tmp/cuzk-p11-gw4.toml << 'EOF'
[daemon]
listen = "0.0.0.0:9820"
[srs]
param_cache = "/data/zk/params"
preload = ["porep-32g"]
[synthesis]
partition_workers = 10
[gpus]
gpu_workers_per_device = 4
gpu_threads = 32
EOF
echo "Configs written"

On its surface, this is a trivial operation—two heredocs piped to cat. But within the broader arc of a months-long optimization campaign targeting the SUPRASEAL_C2 Groth16 proof generation pipeline for Filecoin's Proof-of-Replication (PoRep), this message represents a deliberate experimental probe into the limits of GPU-level parallelism. It is the product of a chain of reasoning that spans memory bandwidth analysis, CPU contention characterization, and a careful balancing act between hiding latency and overwhelming system resources.

The Reasoning and Motivation

To understand why this message was written, one must trace the experimental trajectory that led to it. The preceding context reveals a multi-phase optimization effort that had just completed Phase 11, which targeted DDR5 memory bandwidth contention through three interventions: (1) serializing async deallocation with a static mutex, (2) reducing the groth16_pool thread count from 192 to 32 via gpu_threads = 32, and (3) a global atomic throttle flag to yield CPU time during the b_g2_msm GPU kernel. Benchmarking these three interventions together at concurrency 20 with 15 jobs yielded 36.8 seconds per proof—a modest 3.2% improvement over the Phase 9 baseline of 38.0 seconds.

Crucially, the throttle intervention (Intervention 3) showed negligible benefit. The assistant's reasoning, articulated in the prior message, was that reducing gpu_threads to 32 already alleviated the L3 cache contention that the throttle was designed to mitigate. With fewer CPU threads competing for memory bandwidth during the b_g2_msm window, the throttle became redundant.

The user then interjected with a suggestion: "Try 3/4 gpu workers per dev?" This is the direct trigger for the subject message. The user recognized that with GPU kernel execution being the dominant time component (as established by earlier TIMELINE analysis showing perfect GPU-boundedness), adding more GPU workers per device could increase pipeline overlap, hiding the lead time gaps between synthesis completion and GPU availability. The assistant immediately grasped the logic and proceeded to create the config files to test this hypothesis.

Decision-Making and Experimental Design

The assistant's decision to create two separate config files rather than one reflects a methodical experimental approach. By isolating gpu_workers_per_device as the sole variable (holding gpu_threads = 32, partition_workers = 10, and SRS preload constant), the assistant ensures that any performance difference can be attributed to the GPU worker count alone. This is textbook controlled experimentation.

The choice of 3 and 4 workers per device is also revealing. The baseline configuration used 2 workers per device (the Phase 9 setting). Moving to 3 and then 4 represents a conservative escalation—enough to test the hypothesis without jumping to extreme values that might cause resource exhaustion or OOM failures. The assistant had already encountered OOM issues in Phase 9 when dual-worker pre-staging exhausted GPU memory, so there is an implicit awareness of the memory ceiling.

The configs retain gpu_threads = 32 from Intervention 2, which was the single most effective intervention in Phase 11 (delivering 36.7 s/proof on its own). This is a wise choice: it keeps the proven CPU contention reduction while testing a new variable. The assistant is building on known improvements rather than re-baselining from scratch.

Assumptions Embedded in the Configs

Several assumptions are baked into these configuration files:

Assumption 1: The GPU has sufficient memory to support 3 or 4 concurrent workers. Each GPU worker requires its own set of device buffers for the proof generation pipeline. With the PoRep-32g circuit, each worker's memory footprint is substantial. The assistant implicitly assumes that the GPU (likely an NVIDIA A100 or similar datacenter card with 80 GiB of HBM2e) can accommodate 3–4 concurrent contexts without triggering out-of-memory errors. This assumption is not unreasonable given the earlier Phase 9 dual-worker success, but it is not guaranteed—the memory pressure scales linearly with worker count.

Assumption 2: CPU-side preprocessing can feed 3–4 GPU workers fast enough. The GPU workers are not purely GPU-bound; they require CPU preprocessing (synthesis, SpMV evaluation) to prepare the witness vectors. If the CPU cannot produce work fast enough to keep 3–4 GPU workers busy, the extra workers will simply idle, adding overhead without benefit. The assistant's earlier TIMELINE analysis showed GPU-boundedness, but that was with 2 workers—the bottleneck could shift with more workers.

Assumption 3: PCIe transfer bandwidth is not the limiting factor. Phase 9 specifically optimized PCIe transfers, and the dual-worker mode revealed PCIe bandwidth contention as a bottleneck. Adding more workers per device increases the aggregate PCIe traffic, potentially hitting the Gen4 x16 ceiling (~32 GB/s). The configs do not include any PCIe-specific tuning.

Assumption 4: The partition_workers = 10 setting remains optimal. This value was determined through a systematic sweep in Segment 24, which found 10–12 to be the optimal range for the cuzk SNARK proving engine. The assistant assumes this optimum is independent of GPU worker count—an assumption that may not hold if the CPU-to-GPU work ratio changes.

Assumption 5: SRS preloading is sufficient. The configs preload porep-32g SRS data, which loads the structured reference string into GPU memory at startup. With multiple GPU workers, the SRS must be shared or replicated. The config assumes the existing SRS sharing mechanism (likely via the SendableGpuMutex and shared GPU resources) scales to 3–4 workers.

Potential Mistakes and Incorrect Assumptions

The most significant risk is that 3 or 4 GPU workers per device could worsen throughput due to CPU-side contention. The Phase 11 analysis identified DDR5 memory bandwidth contention as a bottleneck during CPU post-processing. Adding more GPU workers means more CPU threads are needed to prepare work for them, which increases memory pressure. The assistant's own Phase 11 benchmarks showed that Interventions 1 and 3 had negligible impact—the primary gain came from reducing CPU thread count (Intervention 2). Adding more GPU workers effectively increases the CPU thread demand again, potentially negating the Intervention 2 benefit.

There is also a subtle architectural concern: the GPU interlock mechanism (Phase 8's dual-worker mutex) serializes CUDA kernel execution across workers sharing the same GPU. With 3 or 4 workers, the lock contention increases. Each worker holds the mutex only during CUDA kernel execution, but with more workers, the probability of queueing increases. The mutex was designed for 2 workers; its performance characteristics with 3+ are unknown.

The assistant does not explicitly address the risk of OOM, despite having encountered it in Phase 9. The configs do not include any memory reservation or buffer sizing adjustments. If the GPU runs out of device memory, the daemon will crash or hang, wasting benchmark time.

Input Knowledge Required

To fully understand this message, a reader needs:

  1. Knowledge of the cuzk-daemon configuration schema: The TOML structure with [daemon], [srs], [synthesis], and [gpus] sections, and the meaning of each parameter (gpu_workers_per_device, gpu_threads, partition_workers).
  2. Understanding of the Groth16 proof generation pipeline: The distinction between CPU synthesis (constraint evaluation via SpMV) and GPU kernel execution (NTT, MSM, multi-exponentiation), and how they interleave.
  3. Awareness of the optimization history: Phase 8 (dual-worker GPU interlock), Phase 9 (PCIe transfer optimization), Phase 10 (abandoned two-lock design), and Phase 11 (memory bandwidth interventions). Without this context, the config parameters appear arbitrary.
  4. Knowledge of GPU architecture: The concept of concurrent kernel execution, GPU memory partitioning, and PCIe bandwidth limits. The reader must understand why adding GPU workers might help (pipeline overlap) or hurt (resource contention).
  5. Familiarity with the benchmark methodology: The cuzk-bench tool, the --concurrency flag, and the interpretation of per-proof timing metrics.

Output Knowledge Created

This message creates two tangible artifacts: the configuration files themselves. But more importantly, it establishes the experimental conditions for a critical test. The output knowledge that will be produced (in subsequent messages) includes:

The Thinking Process

The assistant's thinking is visible in the structure of the response. Rather than immediately running the benchmark, the assistant first creates the config files. This reveals a disciplined workflow: prepare the experimental conditions, then execute. The assistant does not modify code or rebuild binaries—the changes from Phase 11 are already compiled. The only variable is the configuration.

The choice of partition_workers = 10 is telling. In Segment 24, a systematic sweep determined that 10–12 was optimal. The assistant selects 10, the lower bound of the optimal range. This conservatism suggests an awareness that adding GPU workers increases CPU load, so keeping partition workers at the lower end of optimal provides headroom.

The assistant also retains gpu_threads = 32 from Intervention 2, which was the most effective single change. This is a deliberate decision to isolate the GPU worker count variable. If the benchmark shows improvement, it can be attributed to the worker count increase. If it shows regression, the gpu_threads = 32 setting provides a known-good baseline to fall back to.

The absence of any commentary in the message is itself informative. The assistant does not explain why 3 and 4 were chosen, does not discuss risks, and does not propose further variations. This terseness suggests either confidence in the hypothesis or a desire to get results quickly before speculating. Given the assistant's verbose reasoning style in other messages, the brevity here is notable—it signals a shift from analysis to execution mode.

Conclusion

This message, while only a few lines of shell commands, encapsulates the iterative, hypothesis-driven methodology of the entire optimization campaign. It is a probe into the parallelism limits of a complex GPU-accelerated proving system, grounded in a deep understanding of the memory subsystem, the CPU-GPU work pipeline, and the interactions between multiple optimization interventions. The config files it produces are not mere configuration—they are experimental instruments designed to answer a specific question about the optimal degree of GPU worker concurrency. Whether the answer confirms or refutes the hypothesis, the data generated will inform the next phase of optimization, continuing the cycle of measurement, analysis, and refinement that drives the pursuit of faster, more efficient zero-knowledge proofs.