When the Profiler Fails: A Diagnostic Dead End in CUDA Synthesis Optimization

The Subject Message

In a single, deceptively simple command, the assistant attempts to analyze performance data:

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

And receives a stark reply:

failed to open perf.data: No such file or directory  (try 'perf record' first)

This is message index 1162 in a long optimization session targeting the SUPRASEAL_C2 Groth16 proof generation pipeline for Filecoin PoRep. On its surface, it is a trivial failure — a missing file, a forgotten step. But in context, it represents a critical inflection point where a carefully constructed diagnostic strategy collapses, forcing the assistant to abandon one line of investigation and pivot to another. Understanding why this message was written, what assumptions it rested on, and what its failure meant requires reconstructing the entire chain of reasoning that led to this moment.

The Optimization Context: Three Changes, One Disappointment

The assistant had just implemented three synthesis optimizations drawn from a prior analysis of perf stat data. The first was a Vec recycling pool for LinearCombination temporaries inside ProvingAssignment::enforce. Each constraint evaluation creates three LinearCombination objects (for A, B, and C constraints), each backed by two Vec allocations (one for input terms, one for auxiliary terms), totaling six heap allocations per constraint. With approximately 130 million constraints in a Filecoin PoRep C2 proof, that amounted to roughly 780 million malloc/free calls — previously estimated at ~34% of synthesis time. The recycling pool replaced these with six Vec::pop() and Vec::push() operations on a pre-allocated pool, which should have been dramatically cheaper.

The second optimization was an interleaved A+B evaluation function (eval_ab_interleaved) designed to improve instruction-level parallelism. Instead of evaluating the A constraint's linear combination terms, then the B constraint's terms, then the C constraint's terms in three sequential loops, the interleaved version processed terms from A and B in a combined loop, alternating between them to keep the CPU's execution pipeline fuller and reduce dependency stalls.

The third was software prefetch — _mm_prefetch intrinsics added to the inner loops of the evaluation functions, hinting the CPU to load upcoming terms' data into L1 cache before it was needed.

The microbenchmark results were deeply disappointing. The optimized build averaged 55.0 seconds per synthesis, compared to a baseline of 55.4 seconds — a mere 0.7% improvement. The Vec recycling pool alone should have saved an estimated 7–8 seconds. Something was fundamentally wrong with the diagnosis.

The perf stat Clues

To understand the failure, the assistant ran perf stat comparing the optimized build against the baseline. The counters told a contradictory story. Instructions had dropped by 4.1% — the recycling pool was indeed eliminating allocation code paths. Branch instructions had dropped by 10.4% — fewer jemalloc internals were being executed. But IPC (instructions per cycle) had fallen from 2.60 to 2.53, a 2.7% regression. The code was doing less work, but each instruction was taking more cycles. L3 cache fills had increased by 8.9%, suggesting the memory subsystem was under more pressure, not less.

The interleaved A+B eval was the prime suspect. Its more complex control flow — computing min/max of term counts, maintaining two prefetch streams, branching between A and B term processing — could be confusing the branch predictor and creating pipeline bubbles that negated any ILP benefit. The assistant reverted the interleaved eval back to separate eval_with_trackers calls, keeping only the recycling pool and prefetch. The result was even worse: 55.5 seconds, slightly slower than the original Vec baseline.

This was the moment of crisis. The recycling pool — theoretically the biggest win — was contributing nothing measurable. The assistant's mental model of where time was being spent was clearly wrong.

The perf record Attempt

At this point, the assistant needed deeper data. perf stat gives aggregate counts across the entire run, but to understand where the time was going, call-graph profiling was required. The assistant ran:

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

The output captured in the conversation is garbled binary data — control characters, escape sequences, and raw bytes that perf record writes to stderr as it initializes profiling events. This is normal behavior for perf record, which is designed for interactive terminal use and emits progress information in a terminal-friendly format. The binary output visible in the message is the tool's stderr being captured raw by the session infrastructure.

Then came message 1162: the attempt to analyze the captured data with perf report, which failed because perf.data could not be found.

Why the perf.data Was Missing

Several explanations are possible, and the assistant does not explicitly diagnose which one occurred. The most likely is that perf record requires elevated privileges to set up performance monitoring counters — typically CAP_PERF_EVENT or root access, or the kernel parameter kernel.perf_event_paranoid must be set appropriately. On many production or development systems, unprivileged perf record is restricted. The command may have appeared to run (producing the garbled terminal output) but silently failed to create the data file, or created it in a location the subsequent perf report invocation did not check.

Another possibility is that the working directory changed between commands. The perf record binary output visible in the conversation suggests the command did execute, but the perf.data file may have been written to the working directory of the benchmark binary rather than the shell's working directory, or vice versa. A third possibility is that the perf record process was killed or crashed before writing the data file — the garbled output could indicate a partial or corrupted run.

The Assumptions That Failed

This message reveals several assumptions the assistant was operating under:

First, that perf record would succeed. The assistant had already used perf stat successfully in the previous message (msg 1155), which works with similar kernel facilities. But perf record with -g (call-graph) and --call-graph dwarf (DWARF-based unwinding) is a more invasive operation that may require additional capabilities or kernel configuration.

Second, that the perf.data file would be in the expected location. The assistant did not specify an output path with -o, relying on the default ./perf.data. If the working directory of the shell and the working directory of the spawned process differed — or if perf record changed directories during initialization — the file could end up elsewhere.

Third, that the diagnostic path was viable. The assistant was pursuing a hypothesis that jemalloc was not the bottleneck and that the real hotspot lay elsewhere — perhaps in the circuit's closure code that creates temporary LinearCombination objects via Boolean::lc(). But without perf report data, this hypothesis could not be confirmed through profiling.

The Pivot That Followed

The failure of perf report forced a change in strategy. Unable to profile the full synthesis run, the assistant would need to reason from first principles about where the time was actually going. The subsequent messages in the conversation show the assistant examining the circuit code directly, identifying that Boolean::lc() — which creates a new LinearCombination for each boolean variable in the constraint — was being called inside the enforce closures, creating dozens of temporary allocations per constraint that the recycling pool never touched. The pool only recycled the six Vecs created by enforce itself, but the real allocation storm was inside the closures.

This led to the implementation of add_to_lc and sub_from_lc methods on Boolean and Num, allowing boolean terms to be added directly to an existing LinearCombination without creating a temporary. This was the optimization that finally moved the needle.

The Broader Lesson

Message 1162 is a study in the fragility of performance diagnosis. The assistant had built a sophisticated mental model of where time was being spent — 34% on jemalloc alloc/dealloc, derived from perf stat data showing jemalloc functions at the top of the profile. But that model was incomplete. The recycling pool addressed the allocations that perf stat had highlighted, but those allocations were not the true bottleneck. The real bottleneck was invisible to perf stat because it was distributed across dozens of smaller allocation sites inside the circuit closures — each too small to appear individually in the aggregate profile, but collectively dominating the runtime.

The failed perf record attempt was not a setback in the conventional sense. It was a signal that the current diagnostic approach had reached its limits. The assistant could not profile deeper with the available tools, so it had to reason from code structure instead. That reasoning — tracing the allocation patterns inside the closures — led to the optimization that actually worked.

In this sense, the error message "failed to open perf.data: No such file or directory" is not the end of a diagnostic path but the beginning of a more thoughtful one. It is a reminder that performance optimization is not a purely empirical exercise. When the profiler fails, the optimizer must become a reader of code, tracing data flows and allocation patterns by hand. Message 1162 captures that moment of transition — the instant when the assistant realizes the tools cannot answer the question, and the question must be reformulated.