Probing the Performance Counter Landscape: A Diagnostic Pivot in the cuzk Optimization Pipeline

The Message

[assistant] [bash] perf list 2>&1 | grep -E 'L1-dcache|L1-icache|LLC|dTLB|iTLB|l1d|l2'
  resctrl:pseudo_lock_l2                             [Tracepoint event]
  bp_l2_btb_correct
  ic_cache_fill_l2
  l2_cache_req_stat.all
  l2_cache_req_stat.dc_access_in_l2
  l2_cache_req_stat.dc_hit_in_l2
  l2_cache_req_stat.ic_access_in_l2
  l2_cache_req_stat.ic_dc_hit_in_l2
  l2_cache_req_stat.ic_dc_miss_in_l2
  l2_cache_req_stat.ic_fill_hit_s
  l2_cache_req_stat.ic_fill_hit_x
  l2_cache_req_stat.ic_fill_miss
  l2_cache_req_stat.ic_hit_in_l2
  l2_cache_req_stat.ls_rd_blk_c
  l2_cache_req_stat.l...

At first glance, this message appears to be a simple plumbing operation — a developer running a perf list command with a carefully crafted grep pattern to discover available hardware performance counters. But in the context of the broader optimization session, this single command represents a critical diagnostic pivot. It is the moment when the assistant, having encountered a stubborn performance regression that defied explanation through timing measurements alone, reaches for the hardware-level microscope to understand why a seemingly beneficial optimization (SmallVec) is causing a 5–6 second slowdown on a Zen4 architecture. This article dissects that moment: the reasoning, the assumptions, the knowledge required, and the knowledge produced.

Context: The Regression That Wouldn't Yield

To understand why this message was written, one must understand the crisis that precipitated it. The assistant was deep in Phase 4 of a multi-phase optimization campaign for the cuzk proving engine — a Groth16 proof generation pipeline for Filecoin's Proof-of-Replication (PoRep). The team had already achieved impressive throughput gains: Phase 2's async overlap pipeline delivered 1.27× improvement, and Phase 3's cross-sector batching pushed that to 1.46×. Phase 4 was supposed to be the compute-level optimization wave, targeting the hot loops of constraint synthesis.

Four optimizations were implemented in parallel:

• A1: Replace Vec with SmallVec (inline capacity 2) in the LinearCombination indexer, aiming to reduce heap allocations by storing small vectors inline.
• A2: Pre-size ProvingAssignment vectors to eliminate reallocation.
• A4: Parallelize B_G2 CPU MSMs across circuits.
• D4: Per-MSM window size tuning for the GPU tail MSMs.
• B1: Pin a/b/c vectors with cudaHostRegister for faster H→D transfers. But when the E2E benchmark was run, the results were baffling. Instead of the expected 15–25% synthesis speedup from reduced allocations, the SmallVec change (A1) caused a regression of 5–6 seconds. The assistant systematically eliminated other variables: A2 was reverted (it wasn't being called), B1 was reverted (it showed no benefit), leaving A1 as the sole culprit. A dedicated synth-only microbenchmark was built to isolate synthesis from GPU work, and it confirmed the regression: 55.5 seconds with SmallVec cap=2 versus a baseline with Vec. The microbenchmark then tested SmallVec with different inline capacities (cap=1, cap=2, cap=4) against plain Vec. All SmallVec variants were slower. This was deeply puzzling. SmallVec is a well-established optimization in the Rust ecosystem, designed precisely to reduce allocation overhead for small collections. Why would it be slower on a modern Zen4 server?

The Hypothesis: Cache Pressure on Zen4

The assistant formulated a hypothesis: Zen4's cache hierarchy might be the culprit. SmallVec stores its inline data within the SmallVec struct itself, which increases the struct's size. A SmallVec<[(usize, T); 2]> for a field element (which in bellpepper-core is a 32-byte bls12_381::Scalar) would be significantly larger than a Vec (which is just pointer, length, capacity — 24 bytes on 64-bit). If the LinearCombination struct is embedded in a hot data structure that's iterated millions of times during synthesis, the increased struct size could cause more L1 cache misses, more L2 misses, and ultimately more memory traffic. On a cache-sensitive architecture like Zen4's Zen 4 core (which has a 32KB L1 data cache and 1MB L2 per core), even a few dozen extra bytes per object could evict useful data and tank performance.

But this was just a hypothesis. To test it, the assistant needed hardware performance counter data — specifically, cache miss rates, instruction-per-cycle (IPC), and branch misprediction rates for both the Vec and SmallVec configurations. This is where the target message enters the story.

The Message: Probing for Available Hardware Counters

The command perf list 2>&1 | grep -E 'L1-dcache|L1-icache|LLC|dTLB|iTLB|l1d|l2' is a reconnaissance operation. The assistant is asking the Linux kernel's perf subsystem: "What cache-related hardware events are available on this system?"

This was not the first attempt. In the preceding message ([msg 1051]), the assistant had already run perf list hw cache which showed only the legacy hardware events (branch-instructions, branch-misses, cache-misses, etc.) — the generic, cross-architecture abstractions that perf provides. But these generic events are often insufficient for detailed microarchitectural analysis. The cache-misses event, for example, typically counts last-level cache misses, which is too coarse to diagnose L1 pressure.

Then in [msg 1052], the assistant tried perf list cache 2>&1 | grep -E 'L1-dcache|L1-icache|LLC|dTLB|iTLB' which returned nothing — an empty result. This was a crucial negative signal. It meant that either (a) the system's perf didn't expose those specific event names, or (b) the AMD Zen4 PMU (Performance Monitoring Unit) uses different naming conventions than the Intel-style names the assistant was searching for.

The target message is the third attempt, using a refined grep pattern. The assistant broadened the search: instead of just the generic names, they added l1d and l2 as fallback patterns, hoping to catch any AMD-specific event names that contain these substrings. The output confirms this strategy partially worked: it found a rich set of l2_cache_req_stat.* events — AMD-specific events for L2 cache requests, categorized by request type (dc_access, dc_hit, ic_access, ic_hit, ic_miss, etc.). But notably, no L1-dcache, L1-icache, LLC, dTLB, or iTLB events appeared. The L1 events, if they exist on this platform, use different naming conventions that don't match the Intel-style patterns.

Assumptions and Their Consequences

This message reveals several assumptions the assistant was operating under:

Assumption 1: That cache-level perf events would be available on this Zen4 system. The assistant assumed that perf list would expose L1 and LLC events under standard names. This assumption was partially correct (L2 events were available) but partially wrong (L1 events weren't found under the expected names). On AMD Zen4, the PMU events are documented in AMD's Processor Programming Reference (PPR), and they often use different naming than Intel's equivalent events. The L1 data cache events on Zen4 might be named l1_dc_access, l1_dc_miss, etc., or they might require raw event codes rather than symbolic names.

Assumption 2: That the cache-pressure hypothesis was the most likely explanation for the SmallVec regression. This was a reasonable hypothesis, but not the only possible one. The regression could also be caused by:

• Increased instruction count due to SmallVec's branching logic (checking whether to use inline storage or heap storage on every access)
• Compiler optimization differences (SmallVec might prevent certain inlining or vectorization optimizations that Vec enables)
• Memory allocation pattern changes (SmallVec might change the allocation pattern in ways that interact poorly with jemalloc) Assumption 3: That perf stat with appropriate events would provide actionable data. The assistant assumed that collecting cache miss counts would clearly distinguish between the Vec and SmallVec configurations and point to a root cause. In practice, perf data can be noisy and requires careful interpretation, especially on modern out-of-order processors with complex cache hierarchies. Assumption 4: That the Zen4 architecture's cache behavior is the relevant factor. The assistant was running on an AMD EPYC (Zen4) processor. The SmallVec optimization might behave differently on different microarchitectures. The assumption that Zen4's cache hierarchy is particularly sensitive to struct size may be correct, but it's also possible that the regression is specific to this particular workload's access patterns rather than a general property of the architecture.

Input Knowledge Required

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

Linux perf subsystem: Understanding that perf list enumerates available performance monitoring events, that events can be hardware (PMU), software (kernel counters), tracepoint, or cache events, and that the grep patterns are searching for specific event names within the full listing.

AMD Zen4 microarchitecture: Knowledge that Zen4 has a 32KB L1 data cache, 1MB L2 cache, and that cache miss behavior can dominate performance for pointer-heavy data structures. Familiarity with AMD's event naming conventions (e.g., l2_cache_req_stat.*) versus Intel's conventions.

Rust memory model and SmallVec semantics: Understanding that SmallVec<[T; N]> stores up to N elements inline, avoiding heap allocation for small vectors, but at the cost of a larger struct footprint. The trade-off is reduced allocation overhead versus increased cache pressure.

The cuzk proving engine's synthesis hot path: Knowledge that constraint synthesis iterates over millions of constraints, each of which involves creating and combining LinearCombination objects. The LinearCombination struct contains a vector of (usize, T) pairs, and in the SmallVec variant, this vector has an inline capacity of 2.

The preceding diagnostic journey: Understanding that this perf probe is the culmination of a systematic debugging process that involved reverting optimizations, building a dedicated microbenchmark, and testing multiple SmallVec capacities.

Output Knowledge Created

This message produced several pieces of knowledge:

1. The set of available cache events on this Zen4 system. The assistant now knows that:

• L2 cache events are available under the l2_cache_req_stat.* namespace, with fine-grained sub-events for different access types (data cache access, instruction cache access, hits, misses, fill hits, fill misses).
• No L1-dcache or L1-icache events were found under the expected names.
• No LLC (Last Level Cache) events were found.
• No dTLB or iTLB events were found. 2. A constraint on the diagnostic plan. The assistant cannot directly measure L1 cache misses using symbolic perf event names. To get L1 data, they would need to either:
• Find the raw event codes for Zen4's L1 events and use perf stat -e rXXXX
• Use AMD's amd_uncore events if available
• Use perf mem or perf c2c for memory access profiling
• Rely on L2 events as a proxy (L2 misses often correlate with L1 misses for certain access patterns) 3. A refined search strategy. The assistant learned that broadening the grep pattern from specific names (L1-dcache) to substring matches (l1d, l2) yields more results. This is a methodological lesson: when probing an unfamiliar system, start broad and narrow down, rather than assuming standard naming conventions. 4. Confirmation that L2 events are usable for the comparison. The l2_cache_req_stat.ic_dc_miss_in_l2 and l2_cache_req_stat.dc_access_in_l2 events could serve as proxies for cache pressure. If the SmallVec variant shows significantly more L2 misses, that supports the cache-pressure hypothesis.

The Thinking Process: A Window into Diagnostic Reasoning

The sequence of messages leading up to this one reveals a structured diagnostic process:

1. Observe the symptom: SmallVec causes a 5–6s regression in synthesis time.
2. Isolate the variable: Build a synth-only microbenchmark to eliminate GPU noise.
3. Characterize the symptom: Test multiple SmallVec capacities; all are slower than Vec.
4. Formulate a hypothesis: Cache pressure from increased struct size.
5. Design a test: Use perf stat to compare hardware counter data between Vec and SmallVec.
6. Probe the test infrastructure: Check if the necessary perf events are available.
7. Iterate on the probe: First attempt (perf list cache) returns nothing. Second attempt (target message) with refined pattern finds L2 events. The target message is step 7 — the iteration. The assistant didn't give up when the first grep returned empty. Instead, they reasoned: "The events might exist under different names. Let me broaden the search to include substring matches for l1d and l2." This is a classic diagnostic maneuver: when a tool doesn't give you what you expect, check whether you're using the tool correctly before concluding the information doesn't exist. The output is incomplete (truncated with ...), which is typical for perf list on modern systems — the full listing can be hundreds of lines. But the visible output is sufficient: the assistant can see the L2 events and can proceed with the comparison using those.

What Happens Next

With this knowledge, the assistant can now construct the perf stat command to compare Vec and SmallVec. The command would look something like:

perf stat -e l2_cache_req_stat.dc_access_in_l2,l2_cache_req_stat.ic_dc_miss_in_l2,instructions,cpu-cycles,branch-misses ./cuzk-bench synth-only --partition 0

This would be run twice: once with the SmallVec configuration and once after reverting to Vec. The comparison would reveal:

• Does SmallVec cause more L2 cache accesses? (If so, the struct is evicting more cache lines.)
• Does SmallVec cause more L2 misses? (If so, the working set is overflowing the cache.)
• Does SmallVec change the IPC? (If IPC drops, the CPU is stalling more — possibly on cache misses.)
• Does SmallVec change branch misprediction rates? (SmallVec has more conditional branches for inline-vs-heap decisions.) The assistant would then use this data to either confirm or refute the cache-pressure hypothesis, and decide whether to revert A1 permanently or pursue an alternative approach (such as a custom arena allocator that recycles Vec allocations without the SmallVec overhead).

Broader Significance

This message, despite its brevity, exemplifies a crucial skill in performance engineering: the ability to pivot from high-level timing measurements to low-level hardware counter analysis when faced with a counterintuitive result. The SmallVec regression was unexpected — an optimization that should have helped was hurting. The assistant could have accepted the microbenchmark result and simply reverted the change, moving on to other optimizations. Instead, they chose to understand why it was slower, investing in diagnostic infrastructure that might reveal deeper insights about the workload's interaction with the hardware.

This diagnostic depth is what separates superficial optimization from deep performance engineering. The assistant is not just collecting numbers; they are building a mental model of how the code interacts with the microarchitecture, and using hardware counters to validate or refine that model. The perf list command is the first step in that validation — checking whether the measurement tools can provide the data needed to test the hypothesis.

The message also illustrates the importance of platform-specific knowledge. The assistant's initial grep patterns were influenced by Intel-style event naming conventions (L1-dcache, LLC, dTLB). When those patterns failed on an AMD system, the assistant adapted, broadening the search to find AMD-specific event names. This adaptability is essential when working across different hardware platforms, each with its own PMU event taxonomy.

Conclusion

The perf list command in message 1053 is a small but pivotal moment in a larger optimization narrative. It represents the transition from "what" (the SmallVec regression exists) to "why" (what microarchitectural mechanism causes it). The assistant is gathering the tools needed to test a specific hypothesis about cache pressure on Zen4, and in doing so, is learning the contours of the performance analysis toolkit available on this particular system. The output — a list of AMD-specific L2 cache events — will shape the next phase of the investigation, determining whether the cache-pressure hypothesis holds or whether the team must look elsewhere for the root cause of the regression. In the broader context of the cuzk optimization campaign, this message is a reminder that even the most carefully planned optimization can fail in unexpected ways, and that understanding those failures requires descending through multiple layers of abstraction — from application code to data structures to CPU cache hierarchies — until the true bottleneck reveals itself.