The Search for perf.data: A Pivotal Debugging Moment in Synthesis Optimization

Introduction

In the midst of a deep optimization campaign targeting the Groth16 proof synthesis pipeline for Filecoin's PoRep (Proof of Replication), a single bash command stands out as a quiet but critical turning point. Message [msg 1163] in this opencode session is deceptively simple:

[assistant] [bash] ls -la perf.data 2>/dev/null || ls -la /home/theuser/curio/perf.data 2>/dev/null || find /tmp -name 'perf.data' -maxdepth 1 2>/dev/null

A three-location search for a Linux perf profiling data file. On its face, it is a routine diagnostic command—checking whether a file exists, trying the current directory first, then the project root, then /tmp. But to understand why this message was written, and what it reveals about the assistant's reasoning, assumptions, and debugging methodology, one must trace the arc of the optimization effort that led to this moment.

The Optimization Campaign: High Hopes and Disappointing Results

The assistant had just implemented three ambitious synthesis optimizations derived from a prior perf stat analysis. The first was a Vec recycling pool (arena allocator) for LinearCombination temporaries inside the enforce hot loop—the core constraint-addition function called roughly 130 million times during a single PoRep C2 proof synthesis. The second was an interleaved A+B evaluation that processed the A and B linear combination terms in a combined loop to improve instruction-level parallelism (ILP). The third was software prefetch via _mm_prefetch intrinsics in the evaluation loops, intended to reduce cache miss latency.

The expected payoff was substantial. The recycling pool alone was projected to reclaim roughly 34% of synthesis time spent on jemalloc allocation and deallocation. The interleaved eval and prefetch were expected to add further gains. Yet when the assistant ran the synth-only microbenchmark ([msg 1154]), the results were sobering: 54.9 seconds versus a baseline of 55.5 seconds—a mere 0.7–1.1% improvement, orders of magnitude below expectations.

The First Diagnostic Layer: perf stat

Faced with this puzzle, the assistant did not guess or speculate. They reached for a systematic diagnostic tool: perf stat ([msg 1155]). The hardware counter data revealed a paradox:

Isolating the Variables

The assistant's next move was methodical: revert the interleaved eval while keeping the recycling pool and prefetch ([msg 1156]–[msg 1158]). This would isolate whether the interleaving was the cause of the IPC regression. The result ([msg 1159]–[msg 1160]) was even more puzzling: 55.5 seconds average—essentially identical to the original baseline. The pool and prefetch alone were providing zero measurable benefit.

This was a genuine head-scratcher. The assistant reasoned through several possibilities ([msg 1160]): perhaps jemalloc's thread-local cache on Zen4 was so fast (5–7 ns per allocation) that the pool's own Vec::pop()/Vec::push() overhead consumed the savings. Perhaps most LinearCombination Vecs were tiny (1–2 elements), making jemalloc's small-size-class fast path nearly free. Perhaps the recycled Vecs had grown large capacities over time, wasting cache.

The Need for Deeper Profiling

At this point, the assistant faced a decision. The perf stat counters provided aggregate-level insight but could not reveal which functions were consuming the cycles. To understand why the optimizations were failing, the assistant needed a call-graph profile—a breakdown of where the CPU was actually spending its time.

The assistant ran perf record ([msg 1161]) with call-graph capture:

perf record -g -F 4000 --call-graph dwarf -- 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 2>&1 | tail -5

But the output was garbled—binary data from perf mixed with the benchmark's stdout. The assistant then tried to read the profile with perf report ([msg 1162]):

perf report -n --stdio --no-children 2>&1 | head -60

And received the error: "failed to open perf.data: No such file or directory".

Message 1163: The Search

This brings us to the subject message. The perf.data file was not in the current working directory. Why? The perf record command had been executed from some directory (likely the curio project root or the cuzk subdirectory), but perf report was run from a different directory—or the file had been written elsewhere.

Message [msg 1163] is the assistant's response: a pragmatic, fault-tolerant search across three likely locations:

  1. perf.data in the current directory—the default location where perf record writes its output.
  2. /home/theuser/curio/perf.data—the project root, a likely working directory.
  3. /tmp/perf.data—a fallback, in case the file was written to a temporary location. The command uses 2>/dev/null to suppress error messages (like "No such file or directory" from ls) and chains the commands with ||, so each subsequent command runs only if the previous one failed. The find with -maxdepth 1 limits the search to /tmp's top level, avoiding a recursive traversal of the entire temporary directory.

What This Message Reveals

On the surface, this is a trivial file-finding command. But in context, it reveals several important aspects of the assistant's debugging methodology:

Methodical Problem Solving

The assistant does not panic or guess. They follow a structured diagnostic pipeline: benchmark → perf stat → perf record → perf report. When a step fails (the missing perf.data), they do not re-run the entire pipeline—they find the missing piece and continue. This is the hallmark of systematic debugging: each tool answers a specific question, and the assistant knows which question to ask next.

Assumptions About the Environment

The assistant assumes that perf record ran successfully and wrote its data somewhere accessible. The three-location search reflects assumptions about where the working directory might have been during the perf record invocation. The first two locations cover the most likely scenarios; /tmp is a common fallback for tools that write temporary files.

The Cost of Tooling Complexity

The garbled output from perf record (message [msg 1161]) hints at a deeper challenge: profiling tools interact poorly with complex Rust binaries that produce structured logging. The 2>&1 redirection mixed perf's binary output with the benchmark's stdout, making it impossible to see whether the command succeeded. This is a recurring theme in performance engineering—the tools that provide the most insight are often the hardest to use correctly.

A Pivot Point

Most importantly, message [msg 1163] is a pivot point. The assistant is about to discover the true bottleneck. The perf record data, once located, will reveal that the recycling pool was targeting the wrong allocations. The real culprit, as the chunk summary notes, is the temporary LinearCombination objects created inside closures via Boolean::lc(), UInt32::addmany, and SHA-256 gadgets—allocations that the pool never touched because they happened inside the closure functions, not in the enforce method itself.

This insight will lead to a fundamentally different optimization: adding add_to_lc and sub_from_lc methods to Boolean and Num that operate in-place on an existing LinearCombination, eliminating temporary object creation at the hottest call sites. The recycling pool was treating the symptom; the in-place methods treat the cause.

Conclusion

Message [msg 1163] is a reminder that even the most mundane commands in a coding session can carry significant weight. A search for a missing profiling data file is not just a file lookup—it is the bridge between a failed optimization hypothesis and the data that will refute it. The assistant's methodical approach—implement, benchmark, profile, isolate, search, analyze—is the essence of performance engineering. And the willingness to follow the data, even when it leads away from cherished assumptions, is what separates effective optimization from wishful thinking.

The perf.data file, once found, would tell a story the assistant did not expect to hear. But finding it required this small, unremarkable command—a quiet moment of debugging that made the subsequent breakthrough possible.