The 12-GiB Boundary: Probing Memory Ceilings in GPU Proving Pipeline Optimization

nohup /home/theuser/curio/extern/cuzk/target/release/cuzk-daemon --config /tmp/cuzk-p12-pw12.toml > /home/theuser/cuzk-p12-pw12.log 2>&1 &
echo "PID=$!"
PID=2303026

At first glance, message 2981 appears to be nothing more than a routine command: start a daemon with a new configuration file. But within the trajectory of the Phase 12 optimization campaign for the SUPRASEAL_C2 Groth16 proof generation pipeline, this message represents a critical diagnostic probe — an attempt to map the precise memory ceiling of a 755 GiB system under peak load. The assistant is not merely launching a process; it is testing a hypothesis about the relationship between synthesis parallelism and physical memory capacity, iterating toward the optimal configuration after a previous attempt crashed with an out-of-memory (OOM) error.

The Context: A Pipeline Under Pressure

To understand why this message was written, one must trace the events of the preceding minutes. The Phase 12 "split GPU proving API" had just been successfully implemented and benchmarked. The core innovation of Phase 12 was decoupling the b_g2_msm (a multi-scalar multiplication on the G2 curve) from the GPU worker's critical path, allowing the worker to pick up the next partition's GPU work without waiting for CPU-side post-processing to complete. The initial benchmark with partition_workers=10 (pw=10) yielded a throughput of 37.1 seconds per proof — a ~2.4% improvement over the Phase 11 baseline of 38.0 seconds ([msg 2962]).

The user then issued a natural suggestion: "Try higher synthesis partition_workers in config, maybe 15/20?" ([msg 2964]). The reasoning is sound: if the split API frees the GPU worker faster, the bottleneck may shift to synthesis throughput. Increasing the number of concurrent partition synthesis workers should, in theory, keep the GPU better fed with prepared work. The assistant agreed and immediately began testing.

The first probe, pw=15, failed catastrophically. The daemon process was killed by the Linux kernel's OOM killer, which triggered a broken pipe on the benchmark's RPC connection (<msg id=2977-2979>). The assistant diagnosed the failure with a back-of-the-envelope calculation: each partition synthesis uses approximately 13 GiB of memory, so 15 concurrent syntheses would consume ~195 GiB. Combined with the SRS parameter cache (44 GiB), the PCE (26 GiB), and other overheads, the total exceeded the 755 GiB available on the system.

The Decision to Probe pw=12

Message 2981 represents the second probe in this binary-search-like exploration of the memory ceiling. The assistant explicitly states its reasoning in the preceding message: "Let me try pw=12 as a middle ground between 10 and 15" ([msg 2979]). This is not a random choice — it is a deliberate interpolation. The assistant knows that pw=10 works (37.1s/proof, stable), and pw=15 fails (OOM). The question is where the boundary lies.

The choice of 12 is significant. It represents a 20% increase in synthesis parallelism over the working baseline. The assistant's mental model assumes a roughly linear relationship between partition worker count and memory pressure: each additional worker adds ~13 GiB of peak memory. Going from 10 to 12 adds ~26 GiB, which the assistant judges might fit within the remaining headroom. The config file written at message 2980 sets partition_workers = 12 alongside the existing gpu_workers_per_device = 2 and gpu_threads = 32 — the same GPU configuration that produced the 37.1s result.

Assumptions and Blind Spots

This message reveals several assumptions, some of which turn out to be incorrect. First, the assistant assumes that memory pressure scales linearly and independently with partition worker count. In reality, memory usage is a complex convolution of multiple concurrent resource consumers: the 15 concurrent jobs (j=15) each allocate ~3 GiB for parsing C1 output, the 12 partition workers each allocate ~13 GiB for synthesis, and the GPU workers hold their own allocations for NTT evaluation vectors and MSM operands. The assistant's calculation of 15 × 13 = 195 GiB for synthesis is a simplification that omits the interaction between these concurrent consumers.

Second, the assistant assumes that the OOM at pw=15 was caused purely by synthesis memory, rather than by some nonlinear interaction such as memory fragmentation, CUDA allocation overhead, or double-counting of shared structures. The SRS and PCE are loaded once and shared, but the assistant's arithmetic treats them as additive overheads — a reasonable first approximation, but one that may miss subtler effects.

Third, there is an implicit assumption that the system's 755 GiB of RAM is fully available for the proving pipeline. In practice, the operating system, filesystem cache, and other processes consume some portion. The daemon itself has overhead for logging, networking, and the Rust runtime. These fixed costs are not accounted for in the assistant's mental model.

Input Knowledge Required

To understand this message, one must know several things that are not stated in the message itself. The reader must understand what partition_workers controls: it is the number of concurrent CPU threads that synthesize circuit partitions, each producing the a, b, c NTT evaluation vectors and other data structures needed by the GPU. Each such synthesis consumes approximately 13 GiB of peak memory. The reader must also know the system's total memory capacity (755 GiB, established in earlier segments of the conversation) and the memory footprints of other components: the SRS parameter cache (~44 GiB), the PCE (~26 GiB), and the per-job parsing overhead (~3 GiB per concurrent job).

The reader must also understand the architecture of the split API: that gpu_prove_start begins GPU work on a partition and returns a handle, while gpu_prove_finish waits for the background b_g2_msm thread to complete. The GPU worker can therefore pick up a new partition immediately after gpu_prove_start, without waiting for the MSM to finish. This is why increasing partition workers might improve throughput — more synthesized partitions can be queued for the GPU.

Output Knowledge Created

This message, combined with its aftermath, creates critical knowledge about the system's memory capacity envelope. The subsequent benchmark ([msg 2983]) reveals that pw=12 also OOMs, though the daemon manages to complete some work before dying. The first proof completes in 78.3 seconds (prove=71880 ms), but subsequent proofs slow down and the process eventually fails. This tells the assistant that the boundary lies somewhere below 12 — that even 12 concurrent partition syntheses, when combined with 15 concurrent jobs and 2 GPU workers, exceed the system's capacity.

More importantly, this message establishes that the Phase 12 optimization has reached a memory-imposed limit. The performance gain from the split API (37.1s vs 38.0s) is real but modest, and further gains through increased synthesis parallelism are blocked by the memory ceiling. This insight shapes the future direction of the project: instead of throwing more parallelism at the problem, the next optimizations must focus on reducing per-partition memory footprint, streaming partitions sequentially, or implementing the Sequential Partition Synthesis proposal documented in earlier segments.

The Thinking Process

The assistant's reasoning, visible in the surrounding messages, follows a systematic experimental methodology. It begins with a hypothesis (more partition workers → better GPU utilization → higher throughput), tests it at pw=15, observes OOM, and immediately formulates a refined hypothesis (the boundary is somewhere between 10 and 15). It then selects pw=12 as a midpoint probe. This is textbook binary-search optimization: given a working lower bound and a failing upper bound, test the midpoint to narrow the feasible region.

The assistant also demonstrates an awareness of the cost structure. When pw=15 fails, it does not simply retry with a slightly lower value — it performs a rough memory accounting to explain the failure. This accounting, while simplified, shows a mental model of the system's resource consumption that guides the choice of 12 as the next probe point.

The Broader Significance

Message 2981 is a microcosm of the entire optimization campaign. It captures the moment when a performance optimization runs into a hard physical constraint — not a software bug, not a algorithmic inefficiency, but the simple fact that a 755 GiB machine cannot hold 15 × 13 GiB of synthesis data simultaneously. The split API succeeded in its primary goal (decoupling GPU work from CPU post-processing), but the next bottleneck is not compute or latency — it is memory capacity.

This realization is itself a valuable output. It reframes the optimization problem: the path to higher throughput does not lie in increasing parallelism within the current memory model, but in fundamentally rearchitecting how data flows through the pipeline. The Sequential Partition Synthesis proposal, which streams partitions one at a time rather than synthesizing them all concurrently, directly addresses this bottleneck. The assistant's willingness to probe the boundary — to crash the daemon and learn from the failure — is characteristic of the engineering mindset that drives this work forward.

In the end, message 2981 is a testament to the iterative, empirical nature of high-performance systems optimization. It is not the message that contains the breakthrough, but the message that discovers the wall — and discovering where the wall is is often as valuable as finding the path around it.