The Unbounded Baseline: A Question That Redefined Memory Management for GPU Proving

In the midst of a deep architectural discussion about memory management for the cuzk GPU proving engine, a single question from the user cut to the heart of an unspoken assumption. The message, brief and precise, reads in full:

Is PoRep SRS/PCE unloaded when e.g. Snap proof needs to be made? Are both kept in memory?

This question, appearing as message index 2067 in the conversation, is deceptively simple. On its surface, it asks for a factual clarification about whether two large memory structures — the Structured Reference String (SRS) and the Pre-Compiled Constraint Evaluator (PCE) — are evicted when the engine switches between proof types. But beneath that surface, the question reveals a profound insight: the user had recognized that the memory management problem was not merely about sizing a budget for a single proof type, but about the cumulative, unbounded growth of baseline memory as the engine encounters more circuit types over its operational lifetime.

The Context That Made the Question Possible

To understand why this question was asked, one must understand the conversation that preceded it. The user and assistant had been collaborating on designing a comprehensive memory management architecture for cuzk (see [msg 2066]), a GPU-based proving engine for Filecoin's zk-SNARK proofs. The assistant had just delivered an exhaustive analysis of how SRS and PCE consume memory: SRS loading ~44 GiB of CUDA-pinned memory for a PoRep 32 GiB proof, and PCE occupying ~26 GiB of heap memory for the same circuit. The assistant had mapped the entire memory lifecycle — from SRS's cudaHostAlloc pinned allocation to PCE's OnceLock static storage — and had begun sketching a MemoryBudget system to replace the fragile, static partition_workers semaphore that was the only existing throttle on memory consumption.

But the assistant's analysis, thorough as it was, had focused on the memory dynamics of a single proof type. It had described the baseline as 70 GiB (44 GiB SRS + 26 GiB PCE) and calculated how many partitions could fit in the remaining ~176 GiB of a 256 GiB machine. The user's question exploded that frame: what happens when the engine needs to handle multiple proof types? What if, after proving a PoRep, the daemon receives a SnapDeals challenge? Does the PoRep SRS get evicted to make room for the SnapDeals SRS? Or do both accumulate, silently eating into the working memory budget?

The Reasoning Behind the Question

The user's reasoning reveals a sophisticated mental model of the system. They had absorbed the assistant's detailed breakdown of SRS and PCE memory footprints and immediately recognized a critical gap: the analysis had implicitly assumed a single-circuit workload. But real Filecoin proving engines handle multiple proof types — PoRep, WindowPoSt, WinningPoSt, SnapDeals — over their operational lifetime. The user was thinking ahead to the operational reality that a daemon might start up, prove a PoRep, then receive a SnapDeals challenge, then a WindowPoSt challenge, accumulating SRS and PCE entries for each.

The question also reveals the user's assumptions about what a well-engineered system should do. By asking "Is PoRep SRS/PCE unloaded?" the user implicitly assumes that eviction might be the expected behavior — that a mature system would manage its memory by releasing resources for circuit types that are no longer active. The question is phrased as a neutral inquiry, but it carries the weight of a design critique: "You've described the memory consumption of a single proof type, but what about the real world where we have multiple types? Is there any mechanism to handle that, or does memory just grow without bound?"

Input Knowledge Required

To ask this question, the user needed to have internalized several pieces of knowledge from the preceding conversation:

What SRS and PCE are: The SRS is the Structured Reference String, a ~44 GiB CUDA-pinned memory structure loaded from a .params file and used by the GPU for Groth16 proving. The PCE is the Pre-Compiled Constraint Evaluator, a ~26 GiB heap structure that caches the fixed R1CS constraint matrices for a circuit, eliminating redundant synthesis work.
That both are process-lifetime allocations: The assistant had explained that SRS is stored in SrsManager.loaded as Arc<SuprasealParameters> and PCE in static OnceLock variables — both effectively immortal once loaded.
That different proof types have different SRS/PCE sizes: The assistant had provided a table showing PoRep 32G SRS at ~44 GiB, WindowPoSt 32G at ~57 GiB, WinningPoSt at ~184 MiB, and SnapDeals at ~33 GiB.
That the existing memory budget was dead code: The working_memory_budget config option was never enforced, and partition_workers was the only real throttle — a static count unaware of actual memory pressure. With this foundation, the user could see the looming problem: if SRS and PCE for every circuit type the daemon encounters are kept forever, the baseline memory consumption grows monotonically over the daemon's lifetime, and no existing mechanism adjusts the partition concurrency to compensate.

The Output Knowledge: What the Question Uncovered

The assistant's response (see [msg 2068]) confirmed the user's implicit fear: neither SRS nor PCE is ever evicted. The SrsManager has an evict() method, but it is only wired to a gRPC EvictSRS RPC — never called internally. The PCE OnceLock variables are write-once, never cleared. There is no eviction path at all.

The assistant then quantified the cumulative impact:

SRS PoRep 32G ......... 44 GiB  (pinned)
SRS SnapDeals 32G ..... 33 GiB  (pinned)
PCE PoRep 32G ......... 26 GiB  (heap)
PCE SnapDeals 32G ..... ~17 GiB (heap)
──────────────────────────────────
Baseline ............. ~120 GiB

On a 256 GiB machine: 256 - 120 - 10 (OS) = ~126 GiB for working set
→ only ~9 partitions at 13.6 GiB each (vs ~12 with PoRep alone)

And if WindowPoSt also appears, the baseline balloons to ~203 GiB, leaving room for only ~3 partitions. The engine's throughput would silently degrade as more circuit types accumulate, with no warning to the operator.

This was a pivotal moment in the design conversation. The user's question forced the assistant to confront a design flaw that the previous analysis had overlooked. The MemoryBudget system being designed could not simply compute a static budget at startup based on total RAM minus a fixed baseline — it had to account for the fact that the baseline itself grows over time as new circuit types are encountered. The budget needed to be dynamic, recalculated as SRS and PCE entries accumulate.

The Broader Significance

The question also raised a deeper architectural question that the assistant articulated in response: should LRU eviction be added for SRS and PCE? PCE can be reloaded from disk in ~5 seconds, and SRS in ~30-60 seconds. Eviction would be feasible if the system could tolerate the latency of re-loading on the first proof after eviction. This trade-off — between memory pressure and reload latency — became a central design consideration for the memory management architecture.

The user's question transformed the design conversation from "how do we size a static budget?" to "how do we manage a dynamically growing baseline?" It forced the assistant to consider LRU caches, eviction policies, and admission control that accounts for cumulative SRS/PCE growth. The final cuzk-memory-manager.md specification (see [chunk 14.0]) incorporated LRU eviction for SRS and PCE caches with a 5-minute minimum idle time, triggered under memory pressure — a direct response to the problem the user had identified.

Conclusion

The user's question at message 2067 is a masterclass in how a short, precise question can reshape an architectural discussion. By asking whether SRS and PCE are unloaded when switching between proof types, the user exposed a fundamental flaw in the assistant's initial analysis: the assumption of a single-circuit workload. The question forced the design to confront the reality of multi-circuit proving, leading to a more robust memory management system with LRU eviction, dynamic budget recalculation, and admission control that accounts for cumulative baseline growth. It is a reminder that the most powerful questions are often the simplest — and that the assumptions we fail to question are the ones that most need examining.