The Diagnostic Pivot: When Hypothesis-Driven Optimization Yields to Empirical Profiling

In any performance engineering effort, there comes a moment of reckoning — when carefully reasoned hypotheses about where time is being spent collide with cold, hard empirical data. Message 1166 of this opencode session captures precisely such a moment. It is a deceptively simple message: the assistant runs perf report on a previously captured profiling data file. But in the narrative arc of this optimization campaign, it represents the pivot point between two fundamentally different approaches to performance improvement — and the gateway to a discovery that would reshape the entire optimization strategy.

The Context of Disappointment

To understand why message 1166 matters, we must first understand what led to it. The assistant had been engaged in a months-long effort to optimize the Groth16 proof synthesis pipeline for Filecoin's proof-of-replication (PoRep) protocol. The synthesis phase — where the constraint system is evaluated to produce the A, B, and C polynomials — was consuming approximately 55 seconds per partition, and the goal was to reduce this.

The assistant had implemented three optimizations in rapid succession:

  1. A Vec recycling pool for LinearCombination temporaries inside the enforce method, intended to eliminate the estimated ~34% of runtime spent on jemalloc allocation and deallocation for the six Vecs created per constraint.
  2. An interleaved A+B evaluation that processed both linear combinations' terms in a single combined loop to improve instruction-level parallelism (ILP).
  3. Software prefetch intrinsics (_mm_prefetch) in the inner loops of the eval and eval_with_trackers functions to reduce cache miss latency. The microbenchmark results were deeply disappointing. The optimized build achieved an average of 55.0 seconds versus a baseline of 55.4 seconds — a mere 0.7% improvement. The perf stat counters told a contradictory story: instructions dropped 4.1% and branches dropped 10.4%, suggesting the recycling pool was eliminating allocations as designed, but IPC (instructions per cycle) also fell from 2.60 to 2.53, indicating the remaining code was running less efficiently. Suspecting the interleaved eval's more complex control flow was hurting pipeline utilization, the assistant reverted it. The result was even more confounding: with only the recycling pool and prefetch remaining, synthesis time regressed to 55.5 seconds — slightly worse than the unoptimized baseline. The assistant spent several messages hypothesizing why: perhaps jemalloc's thread-local cache was fast enough on the Zen4 CPU that the pool's overhead negated its savings; perhaps the recycled Vecs' growing capacity was wasting cache; perhaps the circuit code was creating additional allocations the pool couldn't reach. But without data, these were just guesses.

The Decision to Profile

Message 1165 records the critical decision: instead of continuing to speculate, the assistant would capture actual profiling data. The command was:

perf record -o /tmp/perf-synth.data -F 4000 --call-graph dwarf,16384 -- env FIL_PROOFS_PARAMETER_CACHE=/data/zk/params /home/theuser/curio/extern/cuzk/target/release/cuzk-bench synth-only --c1 /data/32gbench/c1.json --partition 0 -i 1

This sampled CPU cycles at 4000 Hz with DWARF call-graph unwinding, capturing 229,841 samples across the 55-second synthesis run. The resulting data file was 3.6 GB — a rich dataset waiting to be interrogated.

The Subject Message: Running the Report

Message 1166 is the execution of the analysis command:

[assistant] [bash] perf report -i /tmp/perf-synth.data -n --stdio --no-children 2>&1 | head -80
Warning:
Processed 248672 events and lost 17 chunks!

Check IO/CPU overload!

# To display the perf.data header info, please use --header/--header-only options.
#
#
# Total Lost Samples: 111
#
# Samples: 229K of event 'cpu/cycles/Pu'
# Event count (approx.): 225690157013
#
# Overhead       Samples  Command          Shared Object         Symbol                                                                                                                                                           ...

The output is truncated by head -80, showing only the warning about lost chunks (17 chunks lost out of 248,672 events processed, plus 111 lost samples — negligible for a dataset of this size), the total sample count of 229K, and the beginning of the flat profile table header. The actual function-level data is cut off, waiting to be revealed in the next message.

This truncation is itself significant. The head -80 boundary means the assistant is deliberately limiting the output to the most important entries — the top of the profile. In a flat profile sorted by self-time percentage, the first 80 lines would include the top 15-25 functions, which is precisely what the assistant needs to identify the dominant hotspots.

Assumptions and Incorrect Hypotheses

The subject message is the culmination of a chain of assumptions that were about to be overturned. The assistant's mental model, going into this profiling run, was shaped by several beliefs:

The Vec recycling pool should be the dominant optimization. The assistant had calculated that 6 Vec allocations per constraint × ~130 million constraints = ~780 million malloc/free calls, which they estimated at ~34% of synthesis time based on a prior perf stat analysis. The pool was designed to eliminate these. When it didn't produce the expected speedup, the assistant hypothesized that jemalloc was simply faster than expected, or that the pool's own overhead was eating the savings.

The 6 Vecs per enforce call were the primary allocation source. This assumption was based on the structure of the enforce method, which creates three LinearCombination objects (for A, B, and C constraints), each with two Indexer Vecs (one for input terms, one for auxiliary terms). The assistant had traced the code paths and concluded that these were the only Vec allocations happening inside the hot loop.

The closures use the recycled lc parameter. The enforce method passes a recycled LinearCombination::zero() to each closure. The assistant assumed the closures would build upon this object, avoiding additional allocations.

Each of these assumptions was about to be proven wrong.

Input Knowledge Required

To understand the significance of this message, the reader needs familiarity with several domains:

Linux perf profiling: The perf record and perf report toolchain for sampling CPU performance counters. The -F 4000 flag sets the sampling frequency, --call-graph dwarf enables stack unwinding for call chains, and --no-children reports self-time rather than inclusive time.

Groth16 SNARK synthesis: The phase of zero-knowledge proof generation where the constraint system is evaluated to produce polynomial coefficients. In Filecoin's PoRep, this involves ~130 million rank-1 constraint system (R1CS) constraints, each requiring evaluation of three linear combinations.

The bellperson/bellpepper-core architecture: The Rust libraries implementing the constraint system and gadget library. LinearCombination is the core data structure representing a linear combination of variables with coefficients. Boolean::lc() creates a temporary LinearCombination from a boolean variable. UInt32::addmany is a gadget for 32-bit unsigned integer addition.

CPU microarchitecture concepts: Instruction-level parallelism (ILP), instructions per cycle (IPC), pipeline utilization, branch prediction, cache hierarchy, and prefetching.

The prior optimization attempts: The Vec recycling pool, interleaved A+B eval, and software prefetch that were implemented and benchmarked in the preceding messages.

Output Knowledge Created

The subject message itself produces only the raw perf report header. The actionable knowledge comes in the following messages (1167-1173), where the assistant extracts the top functions and discovers the truth:

11.14%  ProvingAssignment::enforce
 8.52%  __mulx_mont_sparse_256
 6.82%  UInt32::addmany
 6.51%  bellpepper_core::gadgets::... (Boolean::lc)

The critical finding: Boolean::lc() alone consumes 6.51% of runtime, and UInt32::addmany consumes 6.82%. More importantly, the recycling pool functions (take, give, recycle, zero_recycled) appear zero times in the profile — meaning the pool is either not being called or is so cheap it doesn't register. The real bottleneck is not the 6 Vecs per enforce call, but the dozens of temporary LinearCombination objects created inside the closures via Boolean::lc().

Further analysis revealed that 84% of closures do use the recycled lc parameter, but they then call Boolean::lc() inside the closure body, creating fresh allocations that bypass the pool entirely. Each UInt32::addmany calls Boolean::lc() 32 times per operand — for SHA-256, this means thousands of temporary LC allocations inside a single enforce call.

The Thinking Process: A Systematic Debugging Methodology

The assistant's journey to this profiling run reveals a disciplined debugging methodology. When the optimizations failed to produce results, the assistant did not simply try random changes. Instead:

  1. Measured the aggregate impact using a microbenchmark (55.0s vs 55.4s baseline).
  2. Collected hardware performance counters via perf stat, revealing the contradictory signals of reduced instructions but lower IPC.
  3. Isolated variables by reverting the interleaved eval to determine if it was causing the IPC regression.
  4. Re-measured after the revert, finding the pool+prefetch alone was still ineffective.
  5. Formulated and evaluated hypotheses about why the pool wasn't helping — jemalloc speed, pool overhead, cache waste from oversized Vecs.
  6. Recognized the limits of speculation and decided to collect direct empirical evidence via perf record. This progression from hypothesis to measurement to deeper measurement is textbook performance engineering. The key insight is that the assistant did not trust its own mental model when the data contradicted it — it sought more granular data rather than doubling down on the existing assumptions.

The Broader Lesson

Message 1166 represents a methodological turning point. Before this message, the assistant was operating under a model where allocation overhead was the dominant cost, and the solution was to recycle allocations. The perf profile revealed a different reality: the dominant cost was not the 6 Vecs per enforce but the explosion of temporary LinearCombination objects created by Boolean::lc() inside the circuit closures.

This discovery led directly to a new optimization strategy: instead of recycling the 6 Vecs, add add_to_lc and sub_from_lc methods to Boolean that directly add or subtract a boolean's term(s) to an existing LinearCombination without creating a temporary LC. The hottest call sites — UInt32::addmany, Num::add_bool_with_coeff, sha256_ch, sha256_maj, and lookup gadget closures — were patched to use these in-place methods.

The message itself is just a command invocation and its truncated output. But in the narrative of this optimization campaign, it is the moment when guesswork ended and data-driven insight began. The assistant stopped asking "why isn't my optimization working?" and started asking "what is actually consuming the cycles?" — and the answer changed everything.