The Microscope Goes Deeper: Measuring Memory Subsystem Performance in Synthesis Optimization

In the relentless pursuit of shaving seconds off a 55-second synthesis bottleneck, a single perf stat command can speak volumes. Message [msg 1118] captures a pivotal measurement moment in the optimization of the SUPRASEAL_C2 Groth16 proof generation pipeline for Filecoin PoRep. The assistant, having just implemented and partially reverted a set of synthesis optimizations, turns the microscope toward the CPU's memory subsystem to understand why the expected performance gains failed to materialize.

The Message

The assistant executes the following command:

perf stat -e instructions,cycles,L1-dcache-loads,L1-dcache-load-misses,dTLB-load-misses,ls_l1_d_tlb_miss.all_l2_miss -- 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 | tee /tmp/perf-vec-l1-dtlb.txt

The output shows the synthesis microbenchmark completing in 55.094637649 seconds for a single partition of a 32 GiB PoRep sector, processing 130 million constraints.

Why This Message Was Written

This message sits at a critical juncture in a multi-phase optimization campaign. To understand its purpose, we must trace the narrative leading up to it.

In the preceding chunk (chunk 0 of segment 14), the assistant had implemented three synthesis optimizations based on a previous perf stat analysis that revealed approximately 34% of runtime was spent on jemalloc allocation and deallocation in the enforce hot loop. The three optimizations were:

1. A Vec recycling pool — an arena-style allocator that reuses six Vec allocations per enforce call instead of freeing and reallocating them.
2. Interleaved A+B evaluation — processing the A and B linear combination terms in a single combined loop to improve instruction-level parallelism (ILP).
3. Software prefetch intrinsics — adding _mm_prefetch to the inner loops of eval and eval_with_trackers to reduce cache miss latency. The results were disappointing: only a ~1% improvement (54.9s vs 55.5s baseline), far below the expected 15–25%. Worse, perf stat revealed that while instructions dropped 4.1%, IPC (instructions per cycle) also fell from 2.60 to 2.53, indicating that the interleaved eval's more complex control flow was hurting pipeline utilization. The assistant reverted the interleaved eval back to separate eval_with_trackers calls while keeping the recycling pool and prefetch, then rebuilt. Message [msg 1118] is the first measurement of this intermediate configuration. The assistant is asking: With the interleaved eval removed but the recycling pool and prefetch retained, what does the memory subsystem look like? The specific counters chosen reveal the hypothesis being tested.

The Reasoning Behind the Counter Selection

The choice of perf stat events is itself a window into the assistant's thinking. After ruling out allocation overhead (addressed by the recycling pool) and ILP limitations (the interleaved eval was reverted because it hurt IPC), the natural next suspect is the memory subsystem. The counters selected target three specific concerns:

• L1-dcache-loads and L1-dcache-load-misses: These measure L1 data cache behavior. If the synthesis hot path is thrashing the L1 cache, every cache miss costs ~4-5 cycles of stall. With 130 million constraints being processed, even a small increase in miss rate compounds dramatically.
• dTLB-load-misses: The data Translation Lookaside Buffer (TLB) caches virtual-to-physical address translations. A dTLB miss forces a page walk, which can cost hundreds of cycles. The synthesis code traverses large data structures (the LinearCombination vectors, the constraint system's internal arrays), and if these span many memory pages, TLB misses could be a significant bottleneck.
• ls_l1_d_tlb_miss.all_l2_miss: This is an AMD-specific counter (the ls_ prefix is characteristic of AMD Zen microarchitectures) that measures L1 dTLB misses that also miss the L2 TLB. These are the most expensive TLB misses — they require a full page table walk, potentially hitting DRAM. The presence of this counter tells us the assistant knows the target CPU is an AMD EPYC or Ryzen and is specifically worried about deep TLB miss penalties. The assistant is effectively running a diagnostic: Is the remaining bottleneck in the caches, the TLBs, or somewhere else entirely? The answer will determine whether the next optimization should target data layout (to improve cache locality), page size (to reduce TLB pressure), or something else entirely.

Input Knowledge Required

To fully grasp this message, one needs to understand several layers of context:

1. The synthesis hot path: The enforce() method in bellperson's ProvingAssignment is called ~130 million times for a 32 GiB PoRep partition. Each call processes three linear combinations (A, B, C) by iterating over their terms, evaluating them against the current variable assignments, and accumulating results. This is the ~55-second bottleneck being optimized.
2. The previous optimization cycle: The Vec recycling pool was implemented in LinearCombination (adding zero_recycled, from_coeff_recycled, and recycle methods) and in ProvingAssignment (adding a VecPool that reuses 6 Vecs per enforce call). The interleaved eval (eval_ab_interleaved) was implemented but then reverted after it caused an IPC regression from 2.60 to 2.53.
3. The perf stat tool: Linux's performance counter subsystem allows sampling of hardware events. The specific events chosen are architecture-dependent — ls_l1_d_tlb_miss.all_l2_miss is an AMD Zen-specific event, indicating the assistant has identified the microarchitecture and is using precise counters rather than generic ones.
4. The synth-only microbenchmark: This is a specialized subcommand (cuzk-bench synth-only) that runs only the synthesis phase without the GPU proving phase, allowing isolated measurement of the CPU bottleneck. It was built with the synth-bench feature flag in the previous message ([msg 1117]).

Output Knowledge Created

This message produces several valuable outputs:

1. A baseline measurement: The synthesis time of 55.09s serves as the baseline for the current code configuration (recycling pool + prefetch, without interleaved eval). This can be compared against the 55.5s baseline from before any optimizations and the 54.9s from the full three-optimization set.
2. Memory subsystem counters: The perf stat output (written to /tmp/perf-vec-l1-dtlb.txt and displayed in the terminal) provides L1 cache miss rates, dTLB miss rates, and L2 TLB miss rates. These numbers will reveal whether memory access patterns are the dominant bottleneck.
3. A diagnostic signal: If L1 cache miss rates are low but dTLB miss rates are high, the bottleneck is TLB pressure — the working set spans too many pages. If both are high, the data layout is fundamentally cache-unfriendly. If both are low, the bottleneck lies elsewhere (perhaps in instruction count or branch misprediction).

Assumptions and Potential Mistakes

The assistant makes several assumptions in this message:

1. That the recycling pool and prefetch are actually active and beneficial: The interleaved eval was reverted, but the recycling pool and prefetch were kept. The assistant assumes these two optimizations are net positive, but the ~1% improvement from the full three-optimization set could have been entirely from one of them (or even from random variation). A cleaner approach would be to measure each optimization individually, but the combinatorial cost of that is high.
2. That memory subsystem counters will reveal the bottleneck: This assumes the bottleneck is in memory access latency rather than in computation (e.g., field arithmetic in the finite field operations). If the bottleneck is actually in the Rust compiler's generated code for the field multiplication loops, no amount of cache optimization will help.
3. That the AMD-specific counter ls_l1_d_tlb_miss.all_l2_miss is available and meaningful: This counter may not exist on all AMD Zen variants, or its semantics may differ between generations. If the counter is not supported, perf stat will report <not supported> and the diagnostic is incomplete.
4. That a single iteration is sufficient: Running only one iteration (-i 1) means the measurement includes startup overhead (loading the C1 file, initializing data structures) and may have higher variance. Multiple iterations would provide a more stable baseline, but the 55-second runtime makes repeated runs time-consuming.

The Thinking Process Visible in the Reasoning

The assistant's thinking process, visible across the surrounding messages, reveals a systematic, data-driven optimization methodology:

1. Measure first: Before any optimization, collect perf stat data to identify the hottest functions and the biggest bottlenecks. This identified jemalloc alloc/dealloc as ~34% of runtime.
2. Hypothesize and implement: Based on the data, design optimizations targeting the identified bottleneck. The Vec recycling pool targets allocation overhead; interleaved eval targets ILP; prefetch targets cache miss latency.
3. Test and measure: Run the optimized code and collect both wall-clock time and hardware counters. The disappointing ~1% improvement triggered a deeper investigation.
4. Isolate and revert: When the combined optimization set didn't deliver, revert individual components to identify which one is causing regressions. The interleaved eval was reverted because it dropped IPC from 2.60 to 2.53.
5. Deeper diagnostics: With the interleaved eval removed, run more targeted perf stat counters to probe the memory subsystem. This is where message [msg 1118] sits.
6. Iterate: The next message ([msg 1119]) collects allocation fill counters (ls_dmnd_fills_from_sys.*) to understand whether memory bandwidth or DRAM latency is the issue. This continues the diagnostic chain. This is textbook performance engineering: each measurement narrows the hypothesis space, and each optimization is tested in isolation when possible. The assistant is not throwing random optimizations at the code — they are systematically narrowing down where the CPU cycles are going.

The Broader Context

This message is part of Phase 4 of a larger optimization campaign documented across segments 9–14 of the conversation. The overall goal is to reduce the end-to-end proof generation time for Filecoin PoRep, which involves both synthesis (~55s) and GPU proving (~37s). Earlier phases addressed pipeline architecture (Phase 2: async overlap of synthesis and GPU proving, achieving 1.27x throughput) and cross-sector batching (Phase 3: 1.46x throughput improvement). Phase 4 targets compute-level optimizations, and this message represents the diagnostic phase after the first wave of optimizations failed to deliver.

The synthesis phase remains the single largest CPU bottleneck, and understanding its memory access patterns is crucial for the next optimization iteration. Whether the answer lies in better data locality, larger page sizes (using hugetlb or transparent_hugepages), or a complete restructuring of the constraint evaluation loop, the answer begins with the counters collected in this message.