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performance-investigation

Testing & Quality
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Profile and identify performance bottlenecks in Constantine cryptographic code using metering and benchmarking tools. Use when optimizing cryptographic algorithms, analyzing hotspots, or comparing implementations against reference benchmarks.

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Source SKILL.md: https://github.com/mratsim/constantine/blob/HEAD/.agents/skills/performance-investigation/SKILL.md

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What I do

Help identify and analyze performance bottlenecks in Constantine cryptographic implementations through:

  • Metering: Count operations (field mul, add, EC ops, scalar muls) with -d:CTT_METER flag
  • Benchmarking: Measure ops/s, ns/op, CPU cycles with -d:danger flag
  • Profiling: Create small test binaries for detailed analysis with perf or similar tools
  • Comparison: Compare implementations against reference (e.g., C-kzg-4844)

When to use me

Use this skill when:

  1. Implementation is slower than expected (e.g., 50% slower than reference)
  2. Need to identify hotspots in cryptographic algorithms (FK20, FFT, MSM, pairings)
  3. Optimizing critical paths in PeerDAS (EIP-7594) or KZG (EIP-4844) code
  4. Validating algorithmic complexity (e.g., O(n log n) vs O(n²))
  5. Comparing optimization strategies (fixed-base vs variable-base MSM)

How to investigate performance

Step 1: Enable Metering

Add {.meter.} pragma to suspect procedures:

import constantine/platforms/abstractions  # Re-exports metering/tracer

proc hotspotFunction*(...) {.meter.} =
  # This will be tracked when compiled with -d:CTT_METER

Step 2: Create Test Binary

Create a minimal test file in metering/ or build/:

# metering/m_your_test.nim
import
  constantine/platforms/metering/[reports, tracer],
  # ... your imports

proc main() =
  resetMetering()
  yourFunction()
  reportCli(Metrics, "UseAssembly")

when isMainModule:
  main()

Step 3: Compile with Metering

nim c -r --hints:off --warnings:off --verbosity:0 \
  -d:danger -d:CTT_METER \
  --outdir:build \
  metering/m_your_test.nim

Step 4: Analyze Output

Metering output shows:

  • # of Calls: How many times each proc was called
  • Throughput (ops/s): Operations per second
  • Time (µs): Total and average time per call
  • CPU cycles: Approximate cycle count (indicative only)

Look for:

  • High call counts × high avg time = primary bottleneck
  • EC operations (scalarMul, multiScalarMul) are typically 100-1000× slower than field ops
  • Variable-base MSM vs fixed-base MSM can differ by 5-10×

Step 5: For Detailed Profiling

Compile a small binary for perf:

nim c -d:danger --debugger:native \
  --outdir:build \
  metering/m_your_test.nim

# Then use perf, vtune, or similar
perf record -- ./build/m_your_test
perf report

Key Patterns in Constantine

Metering Infrastructure

  • constantine/platforms/metering/tracer.nim: Defines {.meter.} macro
  • constantine/platforms/metering/reports.nim: CLI reporting
  • constantine/platforms/abstractions.nim: Re-exports metering primitives
  • Flag: -d:CTT_METER enables metering (off by default)

Common Bottlenecks

  1. Scalar Multiplication (scalarMul_vartime)

  2. Multi-Scalar Multiplication (multiScalarMul_vartime)

    • Look for Pippenger vs fixed-base optimization opportunities

  3. FFT Operations (fft_nr, ec_fft_nr)

  4. Field Operations (prod, square, inv)

    • inv and inv_vartime are much slower than mul (~100×)
    • Batch inversion can help

Notes

  • Metering overhead: Metering adds ~10-20% overhead; use for relative comparison, not absolute timing
  • Cycle counts: CPU cycle measurements are approximate (affected by turbo boost, throttling)
  • Compiler effects: GCC vs Clang can differ significantly on bigint arithmetic
  • Assembly: Constantine's compile-time assembler improves performance; check UseASM_X86_64 flag