The Two-Line Investigation: How a Simple find Command Uncovered the Hidden Cost of GPU Optimization

In the middle of a deep-dive performance analysis session, the assistant executed what appears at first glance to be a trivial command:

find extern/supraseal-c2 -name '*.cuh' -o -name '*.cu' | xargs grep -l 'msm_t' 2>/dev/null

The output was equally terse:

extern/supraseal-c2/cuda/groth16_cuda.cu
extern/supraseal-c2/cuda/groth16_ntt_h.cu

Two file paths. No timing data. No code changes. No grand conclusions. Yet this two-line exchange ([msg 1103]) represents a critical turning point in a multi-hour optimization campaign — a moment when the assistant realized that the performance model they had been reasoning from might be incomplete, and that the true cost of an optimization could be hiding in plain sight, outside the GPU's own instrumentation.

The Context: A Regression That Shouldn't Exist

To understand why this simple file search matters, we must reconstruct the situation that led to it. The assistant had been working through "Phase 4" of a performance optimization project for the cuzk proving engine — a Groth16 proof generation pipeline for Filecoin's PoRep (Proof-of-Replication) consensus mechanism. The pipeline had already been heavily optimized through Phases 1-3, achieving a baseline of approximately 88.9 seconds per proof (54.7s synthesis, 34.0s GPU).

Phase 4 introduced four compute-level optimizations, labeled A1 through D4:

• A1: Replace Vec with SmallVec for inline storage of LinearCombination terms (reverted due to IPC regression)
• A2: Pre-size ProvingAssignment allocations (reverted due to regression)
• A4: Parallelize the B_G2 multi-scalar multiplication (MSM) across CPU threads
• B1: Pin a/b/c vectors with cudaHostRegister for faster host-to-device transfers (reverted)
• D4: Per-MSM window tuning — dynamically select the optimal window size for each MSM operation After extensive profiling and reverting of A1, A2, and B1, only A4 and D4 remained active. The expectation was that these two optimizations together would improve throughput: A4 by overlapping B_G2 computation with GPU kernels, and D4 by choosing optimal MSM window sizes automatically. But the E2E test results told a different story. The Phase 4 final configuration clocked in at 93.2 seconds — slower than the 88.9s baseline. The synthesis time had increased marginally (55.8s vs 54.7s), but the GPU time had jumped dramatically: from 34.0s to 37.2s, a 9.4% regression. Even more puzzling, the CUDA internal timing — measured directly on the GPU — was essentially unchanged at ~25.5s. The extra 11.7 seconds were being spent outside the GPU kernels, in the Rust-side overhead between the bellperson prover library and the CUDA code. This was the "bellperson wrapper gap" — the time spent marshaling data, launching kernels, and synchronizing.

The Moment of Doubt

In [msg 1102], the assistant laid out two competing hypotheses for the increased overhead:

1. The par_map thread pool startup for parallel B_G2 (A4) was adding synchronization overhead
2. The D4 per-MSM window tuning was producing suboptimal window sizes, making individual MSM operations less efficient The assistant then tried to investigate hypothesis #2 by grepping for window size configuration in what they assumed was the relevant file: extern/supraseal-c2/cuda/msm_t.cuh. But the file didn't exist at that path — the grep command returned "No such file or directory." This is the critical juncture that leads directly to our subject message. The assistant had been reasoning about D4 based on a mental model of where the MSM tuning code lived. That model was wrong. The file msm_t.cuh — if it existed at all — was not where expected.

The Subject Message: A Systematic Search

The command in [msg 1103] is a textbook example of systematic debugging. Rather than guessing at file paths or grepping blindly, the assistant uses a two-stage pipeline:

1. find extern/supraseal-c2 -name '*.cuh' -o -name '*.cu' — enumerate all CUDA source and header files in the project
2. xargs grep -l 'msm_t' — search each file for the string msm_t, returning only matching filenames The 2>/dev/null suppresses any error messages from files that can't be read, keeping the output clean. This is a deliberate choice: the assistant wants a clean, parseable list of files that reference msm_t, not a wall of error messages. The result identifies two files: groth16_cuda.cu and groth16_ntt_h.cu. Both are in the extern/supraseal-c2/cuda/ directory. The assistant now knows where to look for the MSM tuning logic.

Why This Matters: The Knowledge Gap

The subject message reveals an important aspect of the assistant's thinking process: the willingness to admit when a mental model is incomplete and to gather ground truth before proceeding.

In [msg 1102], the assistant had confidently asserted a hypothesis about D4's window sizes and attempted to verify it by reading a specific file. When that file wasn't found, the assistant didn't double down on the hypothesis or guess at alternative paths. Instead, they stepped back and asked a more fundamental question: "Where does msm_t actually live in this codebase?"

This is a subtle but crucial distinction. The assistant could have tried find . -name 'msm_t.cuh' or guessed at other paths. Instead, they chose a more general approach: find all CUDA files, then search for the symbol. This is more robust because it doesn't depend on knowing the exact filename — it finds any file that uses msm_t, regardless of what the file is called.

Input Knowledge Required

To understand this message, the reader needs:

1. The project structure: extern/supraseal-c2/ is a vendored copy of the supraseal-c2 library, which contains CUDA kernels for Groth16 proving. The .cuh extension denotes CUDA header files, .cu denotes CUDA source files.
2. The optimization taxonomy: The assistant is working through a numbered list of optimizations (A1, A2, A4, B1, D4). D4 is "per-MSM window tuning" — dynamically selecting the optimal window size for multi-scalar multiplication operations. msm_t is likely a type or function related to MSM computation.
3. The performance regression: The assistant has just discovered that GPU time increased by 9.4% despite CUDA internal timing being unchanged. The gap is in Rust-side overhead, and D4's window tuning is a suspect because suboptimal windows could increase the number of MSM rounds or reduce computational density.
4. The previous failed grep: In [msg 1102], the assistant tried grep -n 'window\|wbits\|get_wbits\|C =' extern/supraseal-c2/cuda/msm_t.cuh and got "No such file or directory." This establishes that the expected file path was wrong.
5. Unix command-line tools: The find + xargs + grep pipeline is a standard pattern for searching file contents across a directory tree. The -l flag to grep prints only matching filenames, not the matching lines themselves.

Output Knowledge Created

The message produces a small but critical piece of knowledge: the two files that reference msm_t. This allows the assistant to:

1. Read those files to understand how MSM window sizes are configured
2. Verify whether D4's tuning is producing optimal or suboptimal windows
3. Determine whether D4 is the cause of the 11.7s overhead gap
4. Either confirm or rule out hypothesis #2 about the regression In the subsequent messages ([msg 1104] and [msg 1105]), the assistant continues this investigation by searching for MSM-related files and sppark (a CUDA MSM library), building a complete picture of the MSM implementation.

The Broader Significance

This message exemplifies a pattern that recurs throughout performance engineering: the most important debugging step is often the simplest one. When a performance model fails to predict reality, the temptation is to construct elaborate new hypotheses. But the most productive response is often to gather more data — to find the code, read the numbers, and let the evidence speak.

The assistant could have spent time theorizing about thread pool startup costs, cache effects, or memory bandwidth. Instead, they chose to locate the relevant source code and read it directly. This is the engineering equivalent of "measure, don't guess" — applied not just to timing data, but to code structure.

The two-line find command in [msg 1103] is a small moment in a long optimization session. But it represents a disciplined approach to debugging: when your mental model fails, don't speculate — go find the code and read it.