Loop Optimization

---
name: loop-optimization
description: "Decides hand-vs-compiler for loop transforms (unrolling, SIMD, fusion, hoisting). Use when reviewing/authoring a hot loop or tempted to hand-optimize one."
allowed-tools: "[]"
category: engineering-core
source_repo: athola/claude-night-market
source_path: "plugins/leyline/skills/loop-optimization/SKILL.md"
source_url: https://github.com/athola/claude-night-market/blob/HEAD/plugins/leyline/skills/loop-optimization/SKILL.md
---


# Loop Optimization: Hand vs Compiler

A decision rule for the five common loop transformations. Its value
is knowing when manual application is redundant (the compiler already
does it) or harmful (it defeats the vectorizer or fools your
benchmark).

## When To Use

- Reviewing a hot loop and a hand-rolled transform appears (unrolled
  body, shift-instead-of-multiply, bespoke SIMD).
- Authoring a loop that profiling proved hot, deciding whether to
  optimize it by hand.
- Pushing back on a "this is faster" claim about a loop micro-opt.

## When NOT To Use

- The loop is not proven hot by a profiler. Optimize nothing first.
- Architecture-level performance (caching layers, sharding): use
  `Skill(pensive:architecture-review)`.
- Detecting complexity hotspots (O(n^2) shapes):
  `Skill(pensive:performance-review)`.

## The decision rule

1. Profile first. No loop transform without a hot loop proven by a
   profiler.
2. In compiled languages (C, C++, Rust), trust the compiler for
   loop-invariant code motion and strength reduction: both run
   automatically at `-O2`/`-O3`, so the manual form is redundant. Leave
   unrolling to the compiler as well. Unlike the other two it is not on
   by default (GCC needs `-funroll-loops`), but the compiler owns the
   profitability decision and manual unrolling routinely defeats the
   auto-vectorizer.
3. If a loop will not vectorize, fix aliasing (`restrict` /
   `__restrict__`) and loop shape first. Confirm with an optimization
   report (`-fopt-info-vec-missed`, `-Rpass-missed=loop-vectorize`).
   Reach for intrinsics last and accept the portability cost.
4. The manual transforms that still pay: explicit SIMD on loops the
   compiler misses, loop fusion (guard against register and cache
   pressure), and multi-accumulator unrolling to break a floating-point
   reduction chain the compiler legally will not reorder.
5. In Python, the levers are: hoist invariants out of the loop,
   vectorize via NumPy, fuse passes via numexpr/Numba. Do not hand-unroll
   or hand-strength-reduce: the cost is bytecode dispatch, not loop
   control.
6. Validate every claimed speedup on production-distribution data.

## Per-technique reality

| Technique | Helps where | When NOT to apply by hand |
|-----------|-------------|---------------------------|
| Unrolling | C/C++/Rust FP reduction chains (multi-accumulator) | Auto-vectorizable loops (defeats vectorizer); OOO CPUs; icache pressure; Python |
| SIMD / vectorization | C/C++/Rust loops the compiler misses; Python via NumPy | Before fixing aliasing/loop shape; short trip counts; unverified that emitted SIMD runs |
| Loop fusion | Bandwidth-bound array loops; Python via numexpr/Numba | When it spills registers or mixes strided access; compute-bound bodies; blocks vectorization |
| Hoisting (LICM) | Python (no compiler does it); C/C++/Rust only when aliasing blocks the proof | `-O2`+ compiled code: redundant and can lengthen live ranges |
| Strength reduction | Compilers do it; near-useless by hand | `-O2`+ compiled code: blocks the compiler's IV analysis and vectorization |

## Two traps that invalidate "it is faster"

1. Synthetic-benchmark trap. A loop micro-opt validated on reused, small,
   or synthetic input can invert to slower on production data, because
   synthetic input hides effects such as branch misprediction on real
   value distributions. Benchmark on production-distribution data with
   optimizer barriers, or do not claim the win.
2. Emitted is not executed. Auto-vectorization fails silently. "The
   compiler emitted SIMD" does not mean "SIMD ran." Confirm with codegen
   or optimization reports, not source inspection.

Both traps tie into `Skill(imbue:proof-of-work)`: a speedup claim needs
evidence on representative data, not assertion.

## Exit Criteria

- [ ] The loop in question was profiled and is genuinely hot, or the
      recommendation is "do not optimize."
- [ ] For compiled languages, unrolling/LICM/strength-reduction were
      left to the compiler unless an optimization report shows the
      compiler failed (aliasing) and the manual form was verified faster.
- [ ] Any manual SIMD was preceded by an aliasing/loop-shape fix and a
      check that the vectorized path actually executes.
- [ ] Every speedup claim cites a benchmark on production-distribution
      data, not synthetic or reused input.

---

**Source:** [`athola/claude-night-market`](https://github.com/athola/claude-night-market) → `plugins/leyline/skills/loop-optimization/SKILL.md`