Diagnosing CPU Contention at Scale: The Partition Workers Sweep in Phase 8

Introduction

In the course of optimizing the cuzk SNARK proving engine for Filecoin PoRep, a critical tuning parameter emerged: partition_workers. This setting controls how many CPU threads concurrently synthesize circuit partitions before they are dispatched to the GPU for the CUDA kernel phase (NTT + MSM). After implementing Phase 8's dual-worker GPU interlock — which narrowed the C++ mutex scope and allowed two GPU workers per device to interleave their CUDA kernel execution with CPU preprocessing — the assistant and user needed to find the optimal partition_workers value. Message 2240 captures the analytical pivot point of that investigation: the moment when an extreme setting (partition_workers = 30) was tested, diagnosed, and rejected, confirming the earlier default of 20 as the superior choice.

The Message

The assistant wrote:

The problem is clear — the first partitions have enormous GPU times (22s, 50s!) due to CPU contention from 30 parallel synthesis workers. The prep_msm_ms and split_vectors_ms operations are getting starved. Once the initial burst settles, per-partition times drop to normal 6.2-6.9s.

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With pw=30, 3 sectors (30 partitions) synthesize simultaneously on a 96-core machine. That leaves too few CPU resources for the GPU's preprocessing thread pool.

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The pw=20 result was clearly better. Let me commit the Phase 8 implementation and note this finding: [bash] pkill -f cuzk-daemon 2>/dev/null; sleep 1; echo "done"

This short message packs a dense analytical payload: a root-cause diagnosis, a quantitative comparison, and a decision to finalize the implementation.

Why This Message Was Written: Context and Motivation

To understand why this message exists, one must trace back through the preceding conversation. The cuzk proving engine had undergone a multi-phase optimization journey. Phase 7 introduced per-partition dispatch, where each of the 10 partitions in a PoRep C2 proof is synthesized independently and sent to the GPU as soon as it's ready. Phase 8 then implemented a dual-worker GPU interlock: instead of a single thread holding a coarse C++ mutex across the entire generate_groth16_proofs_c function, the mutex was narrowed to cover only the CUDA kernel region (NTT + MSM, batch additions, tail MSMs). This allowed two GPU workers per device to interleave — one could run CPU preprocessing (vector splitting, b_g2_msm, etc.) while the other held the GPU.

The Phase 8 benchmark results were strong: 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 user then asked, in message 2233: "Try with config partition_workers = 30." This was a natural question — if more partition workers means more parallelism, would 30 outperform 20?

The assistant dutifully updated the config, restarted the daemon, and ran the benchmark. The result was a regression: 60.4s/proof, significantly worse than 44.0s/proof at pw=20. Message 2240 is the assistant's diagnostic analysis of that regression and the decision to reject pw=30.

The Thinking Process: A Diagnostic Deep Dive

The assistant's reasoning in message 2240 reveals a sophisticated mental model of the system's resource constraints. The key insight is that partition_workers does not operate in isolation — it interacts with the concurrency level (-c 5) and parallelism level (-j 3) of the batch benchmark. With -c 5 -j 3, up to 3 proofs are in flight simultaneously. Each proof has 10 partitions. With partition_workers = 30, the system attempts to synthesize 30 partitions concurrently across 3 sectors.

The assistant's first observation targets the GPU timing anomaly: "the first partitions have enormous GPU times (22s, 50s!)." These numbers are striking because the normal per-partition GPU time in Phase 8 was 6.4-6.7s (including CPU preprocessing). A 22s or 50s GPU time indicates severe starvation of the CPU preprocessing that must complete before the CUDA kernel can run. The assistant correctly identifies the culprit: "prep_msm_ms and split_vectors_ms operations are getting starved."

The second observation confirms the diagnosis: "Once the initial burst settles, per-partition times drop to normal 6.2-6.9s." This is classic contention behavior — the system suffers a cold-start penalty as all 30 partitions fight for CPU cores, memory bandwidth, and cache. Once the initial wave passes and some partitions complete, the remaining ones can proceed at normal speed.

The third observation is the root cause: "With pw=30, 3 sectors (30 partitions) synthesize simultaneously on a 96-core machine. That leaves too few CPU resources for the GPU's preprocessing thread pool." This is the critical insight. The machine has 96 CPU cores. With 30 partition workers actively synthesizing, plus the GPU preprocessing threads, plus the daemon's own infrastructure (rayon thread pool of 192 threads, but actual physical cores are 96), the system is oversubscribed. The GPU preprocessing threads — which need to run on CPU to prepare data before the CUDA kernel can execute — cannot get scheduled, causing the GPU to wait idly while CPU cores are saturated with synthesis work.

Assumptions Made

The assistant's analysis rests on several assumptions, most of which are well-founded:

  1. The bottleneck is CPU contention, not memory bandwidth or I/O. The assistant implicitly assumes that the 22s-50s GPU times are caused by CPU starvation rather than, say, memory bandwidth saturation or NUMA effects. This is reasonable given that the symptom (first partitions slow, later ones fast) matches CPU contention patterns, but it's worth noting that memory bandwidth could also be a factor when 30 partitions are being synthesized simultaneously.
  2. The 96-core machine is the limiting resource. The assistant assumes that the machine's 96 physical cores are the relevant constraint. This is correct for CPU-bound workloads, but it's possible that hyperthreading (192 logical threads via rayon) could mitigate some contention. The assistant implicitly recognizes that hyperthreading doesn't help here because synthesis is compute-heavy, not latency-bound.
  3. The GPU preprocessing thread pool is distinct from synthesis workers. The assistant assumes that GPU preprocessing (prep_msm_ms, split_vectors_ms) runs on threads that compete with synthesis workers for CPU cores. This is correct — in the Phase 8 architecture, the GPU worker thread (one per device) performs CPU preprocessing outside the mutex, and these operations use the rayon thread pool or other CPU resources.
  4. The relationship between partition count and sector count is linear. The assistant calculates 3 sectors × 10 partitions = 30 concurrent partitions. This assumes all 3 in-flight proofs are in synthesis simultaneously, which is true given the benchmark's concurrency model.

Mistakes and Incorrect Assumptions

While the assistant's analysis is largely sound, there are some potential nuances that are not explored:

  1. The "first partitions" anomaly might have multiple causes. The assistant attributes the 22s-50s GPU times entirely to CPU contention. However, cold-start effects in the GPU itself (e.g., CUDA kernel compilation caching, memory allocation laziness) could also contribute. The assistant does not check whether the GPU was also experiencing thermal throttling or power capping during the first few seconds of the benchmark.
  2. The diagnosis relies on aggregate GPU timing, not per-function profiling. The assistant cites prep_msm_ms and split_vectors_ms as starved operations, but these are inferred from the C++ timing logs (the CUZK_TIMING lines seen in earlier messages). A more precise diagnosis would require per-function CPU profiling (e.g., perf or flamegraphs) to confirm exactly which operations are contending.
  3. The conclusion that "pw=20 was clearly better" might be premature for all workloads. The benchmark uses a specific workload (5 proofs, concurrency 3, PoRep-32G). For different proof types (e.g., SnapDeals, Winning PoSt) or different hardware configurations (e.g., 128-core machines, or machines with more GPU memory), the optimal partition_workers might differ. The assistant does not qualify the recommendation.
  4. The assistant does not explore intermediate values between 20 and 30. The user asked specifically for 30, and the assistant tested it. But the optimal value might be 22, 24, or 25. The assistant's conclusion that "pw=20 was clearly better" is correct for the tested values, but a full sweep might reveal a different optimum.

Input Knowledge Required

To fully understand message 2240, a reader needs knowledge of:

  1. The cuzk proving engine architecture: How partitions are synthesized on CPU and dispatched to GPU for CUDA kernel execution. The Phase 8 dual-worker interlock, where two GPU workers per device interleave CPU preprocessing with CUDA kernel execution.
  2. The partition_workers parameter: A configuration value controlling how many CPU threads concurrently synthesize circuit partitions. Each partition represents one of the 10 segments of a PoRep C2 proof.
  3. The benchmark methodology: The -c 5 -j 3 flags mean 5 proofs total with 3 concurrent. With 10 partitions per proof, this means up to 30 partitions in flight simultaneously.
  4. The hardware constraints: A 96-core machine with a single GPU (or multiple GPUs, but the benchmark uses one). The rayon thread pool is configured to 192 threads (2× physical cores).
  5. The Phase 7 vs Phase 8 performance baseline: Phase 7 achieved 50.7s/proof at pw=20; Phase 8 achieved 44.0s/proof at pw=20. The pw=30 regression to 60.4s/proof is understood relative to these baselines.
  6. The GPU timing breakdown: The prep_msm_ms and split_vectors_ms metrics are internal C++ timing counters that measure CPU-side preprocessing time before CUDA kernel launch. These are distinct from the pure CUDA kernel time (NTT + MSM, ~3.3s per partition).

Output Knowledge Created

Message 2240 produces several important pieces of knowledge:

  1. Empirical confirmation that partition_workers = 30 causes regression: The quantitative result (60.4s/proof vs 44.0s/proof) provides a clear data point that higher partition counts are not better.
  2. Root-cause diagnosis of CPU contention: The assistant identifies the mechanism — oversubscription of CPU cores causes GPU preprocessing threads to be starved, inflating per-partition GPU times by 3-7× during the initial burst.
  3. A validated mental model of resource interaction: The relationship between partition_workers, concurrency, and physical core count is now empirically grounded. The model predicts that partition_workers × in_flight_sectors should not exceed available CPU cores (minus headroom for GPU preprocessing).
  4. A decision to finalize Phase 8: The assistant decides to commit the Phase 8 implementation with pw=20 as the recommended setting. This is a project-management decision that closes the optimization loop for this parameter.
  5. A template for future tuning: The diagnostic approach — test extreme values, analyze timing anomalies, identify contention patterns — can be reused for other parameters (e.g., synthesis_lookahead, gpu_workers_per_device).

The Broader Significance

Message 2240 is a microcosm of the entire optimization journey. It demonstrates that in complex systems, parallelism is not monotonic — more workers do not always yield better throughput. The system has a "sweet spot" where CPU and GPU resources are balanced, and exceeding it causes contention that hurts both. This is a classic Amdahl's Law / Universal Scalability Law phenomenon, but applied to a heterogeneous CPU+GPU pipeline.

The message also illustrates the value of empirical testing guided by a strong mental model. The assistant did not need to run a full sweep from 1 to 100 to understand that pw=30 was too high — the timing anomaly (22s-50s first partitions) was diagnostic enough to identify the root cause. This is the hallmark of a mature optimization process: the ability to interpret performance signals and map them to architectural causes.

Finally, the message shows the importance of understanding the full system context. The partition_workers parameter does not exist in isolation — it interacts with concurrency, parallelism, physical core count, GPU preprocessing requirements, and memory bandwidth. A change that looks like "more parallelism" can backfire when it starves a dependent subsystem. The assistant's analysis correctly traces the symptom (inflated GPU times) through the causal chain (CPU contention → starved preprocessing → delayed CUDA launch) to the root cause (oversubscription).

Conclusion

Message 2240 is a concise but powerful diagnostic message that closes a critical tuning loop in the cuzk proving engine optimization. By testing the extreme value of partition_workers = 30, observing the regression, and correctly attributing it to CPU contention starving GPU preprocessing, the assistant provides both a quantitative result and a reusable analytical framework. The decision to commit Phase 8 with pw=20 as the recommended setting is well-supported by the evidence. This message exemplifies the kind of systems-level thinking required to optimize heterogeneous CPU+GPU pipelines, where the interaction between parameters is as important as their individual effects.