The Allocation Symmetry: When Freeing Memory Reveals the Cost of Allocating It

In the high-stakes world of Filecoin proof generation, where a single Groth16 proof for 32 GiB of sealed data consumes ~200 GiB of peak memory and takes over a minute to compute, every second matters. The opencode coding session documented in this conversation had already achieved significant wins: a 13.2% end-to-end improvement through Boolean gadget optimizations and, most notably, a 10-second reduction in GPU wrapper time by moving destructor calls to background threads. The deallocation win was striking — simply freeing ~130 GB of Rust Vecs and ~37 GB of C++ vectors asynchronously rather than synchronously reclaimed 10 seconds that had been hiding in plain sight.

It was this victory that prompted the user's penetrating question in <msg id=1291>: "If there was a win in dealloc, is it possible that alloc can have a similar one?"

The subject message, <msg id=1292>, is the assistant's immediate response — a brief but consequential reply that launches a full investigation into whether the symmetric operation, allocation, harbors a similar hidden cost. This message is a masterclass in hypothesis-driven optimization: it takes a user's intuition, grounds it in concrete technical reasoning, and dispatches a task to gather evidence before any code is written.

The Reasoning: Connecting the Symmetry

The assistant's first move is to validate the user's intuition with a crisp technical analogy: "If freeing ~130 GB of Rust Vecs took ~10s, then allocating them during synthesis must have a similar cost — it's the same mmap/page-fault overhead in reverse."

This is the core insight. The deallocation win came from recognizing that synchronous munmap of large memory regions blocks the calling thread while the kernel tears down page tables and returns physical pages to the free list. The fix was to push that work onto a detached thread, letting the function return immediately. But if munmap has a measurable cost, its counterpart mmap — which must find contiguous virtual address space, fault in new pages, and zero them — must also carry overhead. The two operations are symmetric: what the kernel does on the way out, it must also do on the way in.

The assistant then sketches the allocation pattern during synthesis. The ProvingAssignment struct in bellperson holds four large Vecs: a, b, c (each growing to ~130 million entries, or ~4.17 GB), and aux_assignment (~23 million entries, ~0.74 GB). These Vecs are not pre-allocated; they grow organically via push(), which triggers Rust's geometric doubling strategy. For a Vec that grows from capacity 1 to 130 million, this means approximately ceil(log2(130,000,000)) ≈ 27 reallocations. Each reallocation follows the same pattern: mmap a new buffer of twice the current size, memcpy the old data into the new buffer, then munmap the old buffer.

The assistant quantifies this: "For 130M constraints, each Vec does ~27 reallocations (doubling from initial capacity). Each reallocation does a fresh mmap for the larger buffer + memcpy of old data + munmap of old buffer."

The Key Question and the Investigation

The message concludes with the pivotal question: "The key question is: do we already pre-allocate with known capacity, or do we let them grow organically?"

This question is the hinge point of the entire investigation. If the codebase already pre-allocates with known capacity (perhaps through a SynthesisCapacityHint API that the assistant suspects might exist), then the allocation cost is already minimized — each Vec gets exactly one mmap and zero memcpy overhead. If not, the ~27 reallocations per Vec represent potentially gigabytes of wasted memory copying and hundreds of system calls.

To answer this, the assistant dispatches a task tool call. The task is configured with a thoroughness directive and a prompt that asks it to investigate the bellperson source code, specifically the ProvingAssignment construction and the synthesize_circuits_batch function, to determine whether pre-allocation infrastructure exists and whether it's being used.

Assumptions Embedded in the Message

Several assumptions underlie this message, some explicit and some implicit:

The symmetry assumption: The assistant assumes that allocation cost mirrors deallocation cost in both mechanism and magnitude. This is reasonable — both involve kernel memory management operations — but it's not guaranteed. Allocation might be cheaper because the kernel can lazily back new allocations with zero-fill-on-demand pages, deferring the actual page fault cost until the memory is first accessed. Deallocation, by contrast, must happen immediately if the memory is to be reclaimed.

The geometric growth model: The assistant assumes Rust's standard Vec doubling strategy, which is correct for the default implementation. However, the actual number of reallocations depends on the initial capacity. If Vec::new() starts with capacity 0, the first push() triggers an allocation of capacity 1, then 2, 4, 8, etc. The estimate of ~27 reallocations for 130M entries is accurate under this model.

The mmap/memcpy/munmap model: The assistant assumes each reallocation involves a fresh mmap for the larger buffer, a memcpy of old data, and a munmap of the old buffer. This is correct for Rust's Vec on Linux with the system allocator (glibc's malloc uses mmap for large allocations above the M_MMAP_THRESHOLD, typically 128 KiB). For a 4 GB Vec, every reallocation beyond the first few hundred KiB would indeed use mmap.

The existence of a pre-allocation API: The assistant implicitly assumes that bellperson might already have a SynthesisCapacityHint or similar mechanism. This assumption is based on the observation that the deallocation fix required moving destructors to background threads — a relatively sophisticated operation — which suggests the codebase is mature enough to have considered allocation patterns.

Input Knowledge Required

To fully understand this message, the reader needs knowledge in several domains:

Rust memory management: Understanding Vec::push(), geometric growth, reallocation semantics, and the relationship between mmap/munmap and large heap allocations. The reader should know that Rust's Vec doubles its capacity when it runs out of space, and that each reallocation involves allocating new memory, copying existing elements, and freeing the old memory.

Linux kernel memory operations: The distinction between mmap (which creates a new mapping, potentially backed by anonymous pages that are zero-filled on first access) and munmap (which unmaps a region and returns pages to the kernel). The cost of these operations scales with the size of the memory region, particularly for munmap, which must update page tables and potentially initiate TLB shootdowns on multi-core systems.

The Groth16 proof pipeline: Understanding that circuit synthesis produces ProvingAssignment structs containing large vectors a, b, c, and aux_assignment, which hold the R1CS constraint evaluations. These vectors are the intermediate data that will later be consumed by the GPU proving phase.

The previous deallocation win: The context from <msg id=1290> shows that the assistant had just achieved a 10-second reduction in GPU wrapper time by making destructor calls asynchronous. This win is the direct motivation for the user's question and the assistant's investigation.

Output Knowledge Created

This message creates several forms of knowledge:

A concrete hypothesis: The hypothesis that allocation during synthesis has a measurable cost that can be reduced through pre-allocation. This is not yet confirmed — the task will determine whether the hypothesis is correct — but it is now explicitly formulated and testable.

A quantitative model: The estimate of ~27 reallocations per Vec, each involving mmap + memcpy + munmap, provides a framework for understanding the potential cost. If each reallocation copies the entire current contents, the total data copied across all reallocations for a single Vec growing to size N is approximately N * (1 + 1/2 + 1/4 + ...) ≈ 2N — meaning roughly twice the final size in total memory traffic. For a 4.17 GB Vec, that's ~8.34 GB of copying per circuit, or ~83 GB across 10 parallel circuits.

An investigation plan: The task tool call defines the scope of investigation: examine the ProvingAssignment constructors, check for pre-allocation APIs, and determine whether they're wired up in the pipeline callers. This plan is executable and will produce actionable results.

A decision framework: The key question — "do we already pre-allocate with known capacity, or do we let them grow organically?" — establishes a binary decision point. If pre-allocation exists but isn't used, the fix is straightforward: wire it up. If it doesn't exist, the fix requires adding new infrastructure. Either way, the investigation will determine the next steps.

The Thinking Process

The assistant's thinking in this message follows a clear pattern:

Acknowledge the insight: The user's question is recognized as "good intuition" — not just a casual suggestion but a technically grounded observation that connects two symmetric operations.
Map the symmetry: The assistant immediately draws the parallel between mmap (allocation) and munmap (deallocation), recognizing that the same kernel mechanisms are involved in both directions.
Quantify the pattern: Rather than staying at the conceptual level, the assistant estimates the number of reallocations (~27) and the cost per reallocation (mmap + memcpy + munmap), grounding the hypothesis in concrete numbers.
Identify the pivot point: The key question — pre-allocated or organic growth? — is the single piece of information that determines whether the hypothesis has merit. If pre-allocation is already done, the investigation is over before it starts. If not, there's potentially a significant win.
Dispatch evidence gathering: Rather than speculating further, the assistant launches a task to examine the actual code. This is a disciplined approach: form a hypothesis, identify the critical evidence, gather it, then decide.

The Broader Context

This message sits at a pivotal moment in the optimization effort. The assistant had just presented a sobering assessment in <msg id=1290>: Phase 4 was projected to deliver 2-3x throughput improvement but had only achieved 1.15x. Two textbook optimizations (SmallVec and cudaHostRegister) had been invalidated by real hardware testing. The team was facing diminishing returns on remaining Phase 4 items and contemplating a move to Phase 5's more radical PCE approach.

The user's question — born from the observation that deallocation had yielded an unexpected win — represents a refusal to accept diminishing returns without exhausting every avenue. It's the kind of question that comes from deep system understanding: if one side of a symmetric operation has hidden cost, the other side likely does too.

The assistant's response honors that insight by taking it seriously, reasoning through it quantitatively, and launching a proper investigation. Whether the allocation hypothesis proves out or not — and the chunk summary reveals it ultimately showed "zero measurable impact" — the process of testing it rigorously is itself valuable. It either produces a performance win or, equally importantly, confirms that the bottleneck is elsewhere, preventing wasted effort on a dead end.

In the end, the allocation investigation would reveal a fundamental asymmetry: Rust's geometric push() amortizes allocation cost across parallel computation, while the deallocation win came from the synchronous munmap of GPU-phase buffers that blocked the critical path. The synthesis bottleneck was purely computational, not allocational — a finding that reinforced the necessity of Phase 5's circuit-level transformations. But that conclusion could only be reached by following the thread the user's question had opened, starting with this single, focused message.