The Thread That Broke the Pipeline: Isolating CPU Contention in the cuzk Proving Engine

In the high-stakes world of Filecoin PoRep proof generation, every millisecond counts. When the cuzk proving engine's parallel synthesis optimization yielded only a disappointing 7% throughput improvement despite saturating the GPU, the bottleneck was clear: CPU contention. But diagnosing the root cause required understanding a subtle interaction between two completely separate thread pools—one in Rust, one in C++—both racing for the same 96 cores. Message [msg 1907] captures the first step in resolving this contention: a surgical edit to a CUDA source file that would give the operator explicit control over how many CPU threads the GPU preprocessing pipeline consumes.

The Contention Problem

The story begins in the previous session ([msg 1891]), where the assistant implemented a synthesis_concurrency parameter that allowed multiple proofs to be synthesized in parallel on the CPU while the GPU worked on a previously submitted proof. The waterfall timeline instrumentation revealed the GPU was idling for ~12 seconds between proofs because synthesis (at ~39 seconds) took longer than GPU proving (at ~27 seconds). The obvious fix was to overlap two syntheses so the GPU never waited.

But the benchmark results told a more complicated story. With concurrency=2 and j=2, throughput improved by only 7% (from 45.3s to 42.2s per proof). With j=3, GPU utilization hit 90.7% but throughput barely budged. And with j=4, performance regressed to 60.2s per proof. Something was throttling the system long before the GPU became the bottleneck.

The assistant dispatched a research subagent ([msg 1893]) to analyze CPU thread usage across the pipeline. The findings were illuminating. Two independent thread pools were both auto-detecting all available CPUs:

  1. Rust's rayon global thread pool — used by synthesis (bellperson circuit building, PCE evaluation, SpMV computation)
  2. The C++ groth16_pool (an sppark::thread_pool_t static instance) — used by b_g2_msm and preprocessing during GPU proving When parallel synthesis kicked off a second proof while the GPU was still proving the first, both thread pools simultaneously tried to use all 96 cores. The resulting CPU oversubscription caused the regression observed at concurrency=2, j=4.

The Decision: Two Knobs, One Environment Variable

The assistant's analysis in [msg 1904] considered several approaches for controlling the C++ pool. The thread_pool_t class in sppark had two constructors: one accepting an explicit thread count, and another accepting a string interpreted as a hex CPU affinity mask. The affinity-mask approach was deemed "too complex for users." A lambda-based approach using getenv() was considered but dismissed due to static initialization ordering concerns.

The chosen solution was elegant and minimal: modify the single line static thread_pool_t groth16_pool; in groth16_cuda.cu to read an integer from the CUZK_GPU_THREADS environment variable, defaulting to 0 (auto-detect) when unset. This gave the Rust daemon control over the C++ pool size without requiring FFI changes or complex configuration plumbing.

Message [msg 1907]: The Edit

The subject message itself is deceptively brief:

[assistant] ### Step 1: Modify groth16_cuda.cu to read CUZK_GPU_THREADS env var [edit] /home/theuser/curio/extern/supraseal-c2/cuda/groth16_cuda.cu Edit applied successfully.

This is step 1 of a six-step implementation plan outlined in [msg 1905]. The full plan was:

  1. Modify groth16_cuda.cu to read CUZK_GPU_THREADS env var
  2. Add gpu_threads field to GpuConfig in config.rs
  3. Add rayon dependency to cuzk-daemon
  4. Wire daemon main.rs to set the env var and configure the rayon global pool
  5. Update the example TOML configuration
  6. Build and benchmark The brevity is a function of the tool interface: the assistant is using the edit tool, which applies a diff to a file and reports success. The actual content of the edit—the specific code change—is not shown in the message text, but the preceding analysis in [msg 1904] reveals the likely implementation: replacing static thread_pool_t groth16_pool; with a constructor call that reads the environment variable.

Input Knowledge Required

To understand this message, one must know:

Output Knowledge Created

This message produced a modified C++ source file that externalizes thread pool configuration. The immediate output is a mechanism for the Rust daemon to control C++ thread consumption via an environment variable. The downstream effects ripple through the subsequent steps:

Assumptions and Potential Pitfalls

The approach rests on several assumptions. First, that setting CUZK_GPU_THREADS as an environment variable before the C++ static initializer runs is sufficient—this is safe because the groth16_pool static is initialized at library load time, which happens when the Rust binary loads the shared library. Second, that the rayon global pool can be configured once at startup and never needs reconfiguration—this holds because thread counts are static for the daemon's lifetime. Third, that partitioning cores between synthesis and GPU work is a valid isolation strategy—this assumes the two workloads have minimal shared resource contention beyond CPU cores (e.g., cache and memory bandwidth).

A subtle assumption is that the b_g2_msm computation is the only significant CPU work during GPU proving. If other C++ code paths also use the groth16_pool, limiting its thread count could inadvertently slow them down. The assistant's earlier analysis in [msg 1893] confirmed that b_g2_msm is the primary CPU consumer during GPU proving, but the assumption is not exhaustively verified.

The Bigger Picture

This message represents a critical architectural insight: in a heterogeneous compute pipeline spanning Rust and C++, thread management cannot be left to auto-detection. Each layer's thread pool must be explicitly sized to prevent destructive interference. The CUZK_GPU_THREADS environment variable is a small change with large implications—it transforms the C++ thread pool from an opaque, uncontrollable resource consumer into a configurable component of the proving pipeline.

The benchmark results that follow ([msg 1919] onward) will determine whether this isolation strategy actually resolves the contention. But regardless of the outcome, the architectural pattern established here—explicit cross-language thread pool configuration—is a necessary foundation for any serious heterogeneous compute system. The message is a reminder that performance optimization often requires understanding not just what each component does, but how they interact when competing for shared resources.